WSCG 2019
27. International Conference on
Computer Graphics, Visualization and Computer Vision 2019
http://www.wscg.eu
http://www.wscg.cz
Primavera Hotel and Congress Center
Plzen, Czech Republic
May 27 – 30, 2019
Conference Program
Schedule within a session can change without a notice
Conference Chair
Prof. Vaclav Skala
University of West Bohemia
Plzen, Czech Republic
http://www.vaclavskala.eu/
Registration – Primavera Hotel and Congress Center
· Monday 17:00 – 20:00
· Every conference day during breaks
Technical Sessions
· Tuesday – Wednesday – all the days
·
Thursday – morning
afternoon Pilsen city short guided tour & beer tasting
Details at: http://wscg.zcu.cz/Plzen-Grand-Tour.pdf
Conference Dinner
·
Place to be announced, scheduled to Wednesday
check the Information/Registration desk of the WSCG conference
Supporting organizations
·
Department of Computer Science and Enginnering,
Faculty of Applied Sciences
University of West Bohemia, Plzen, Czech Republic
· NVIDIA, Czech Republic, http://www.nvidia.com
·
Center of Computer Graphics and Visualization, Univ.
of West Bohemia
WSCG 2019
TECHNICAL SESSIONS
Session A Tuesday 8:30 - 10:10
·
Besenthal, S., Maisch, S., Ropinski, T.: Multi-Resolution
Rendering for Computationally Expensive Lighting Effects
Paper Code: B17 (Full) Abstract Paper Additional File
·
Kobrtek, J., Milet, T., Herout, A.: Silhouette
Extraction for Shadow Volumes Using Potentially Visible Sets
Paper Code: A41 (Full) Abstract Paper Additional File
·
Listemann, M., Trapp, M., Döllner, J.: Lens-based
Focus+Context Visualization Techniques for Interactive Exploration of Web-based
Reachability Maps
Paper Code: C17 (Full) Abstract Paper
·
Sugihara,K.: Straight Skeleton Computation Optimized
for Roof Model Generation
Paper Code: C67 (Full) Abstract Paper Additional File
·
Shcherbakov,A.,Frolov,V.: Dynamic Radiosity
Paper Code: C47 (Full) Abstract Paper
Session B Tuesday 10:30 - 12:10
·
Schult,T., Klose,U., Hauser,T.-K., Ehricke,H.-H.: Anatomy-Focused
Volume Line Integral Convolution for Brain White Matter Visualization
Paper Code: C41 (Full) Abstract Paper
·
Ogniewski, J.: Cubic Spline Interpolation in Real-Time
Applications using Three Control Points
Paper Code: A43 (Full) Abstract Paper
·
Merten,N., Saalfeld,S., Preim,B.: Floor Map
Visualizations of Medical Volume Data
Paper Code: B05 (Full) Abstract Paper Additional File
·
Brunet J-N, Magnoux V., Ozell B., Cotin S.: Corotated
meshless implicit dynamics for deformable bodies
Paper Code: C53 (Full) Abstract Paper
·
Belyaev, S.Y., Smirnov, P.O., Smirnova, N.D.,
Shubnikov, V.G.: Fast Adaptive Undersampling for Volume Rendering
Paper Code: A37 (Full) Abstract Paper Additional File
Session C - Welcome - Tuesday 12:10 - 12:30
LUNCH
Session D Tuesday 14:00 - 15:40
·
Qi,Z., Lee,J.K.: Automated Object Tracking in Sterile
Pharmacy Compounding
Paper Code: D19 (Full) Abstract Paper Additional File
·
Lykke,J.R., Olsen,A.B., Berman,P., Bærentzen,J.A., Frisvad,J.R.:
Accounting for Object Weight in Interaction Design for Virtual Reality
Paper Code: D07 (Full) Abstract Paper Additional File
·
Khaksari Haddad,H., Laurendeau,D.: Alignment of Point
Clouds for Comparison of Infrastructures in Civil Engineering on Quality
Control in Metrology
Paper Code: D59 (Full) Abstract Paper
·
Clinton L. Jeffery: The City Metaphor in Software
Visualization
Paper Code: D47 (Full) Abstract Paper Additional File
·
Lefkovits, S., Lefkovits, L., Szilágyi, L.: CNN
Approaches for Dorsal Hand Vein Based Identification
Paper Code: C59 (Short) Abstract Paper Additional File
Session E Tuesday 16:00 - 18:00
·
Hauenstein, J., Newman, T.: Exhibition and Evaluation
of Two Schemes for Determining Hypersurface Curvature in Volumetric Data
Paper Code: D02 (Full) Abstract Paper Additional File
·
Sekmen, S., Akyüz, A. O.: Compressed Exposure
Sequences for HDR Imaging
Paper Code: D43 (Full) Abstract Paper Additional File
·
Raposo,A.N., Gomes,A.J.P.: Pi-surfaces: products of
implicit surfaces towards constructive composition of 3D objects
Paper Code: C83 (Full) Abstract Paper Additional File
·
Bustacara-Medina, C., Flórez-Valencia, L.: An
automatic stopping criterion for nonlinear anisotropic diffusion
Paper Code: A89 (Full) Abstract Paper
·
Daoud,Z., Ben Hamida, A., & Ben Amar,C: Automatic
video fire detection approach based on PJF color modeling and spatio-temporal
analysis
Paper Code: A73 (Full) Abstract Paper Additional File
·
Basov,A.Y., Budak,V.P.,
Grimailo,A.V.: The Role of Polarization in The Multiple Reflections Modeling
Paper Code: B73 (Poster) Abstract Paper Additional File
--------------------------------
Session F Wednesday 8:30 - 10:10
·
Riachy,C., Al-Maadeed,N., Organisciak,D., Khelifi,F., Bouridane,A.:
3D Gaussian Descriptor for Video-based Person Re-Identification
Paper Code: D67 (Full) Abstract Paper Additional File
·
Lee,H., Sa,J., Shin,H., Chung,Y., Park,D., Kim,H.:
Deep Learning-based Overlapping-Pigs Separation by Balancing Accuracy and
Execution Time
Paper Code: B03 (Full) Abstract Paper
·
Hausdorf,A., Hinzmann,N., Zeckzer,D.: SyCaT-Vis:
Visualization-Based Support of Analyzing System Behavior based on System Call
Traces
Paper Code: C11 (Full) Abstract Paper
· Organisciak,D., Riachy,C., Aslam,N., Shum, H.P.H.: Triplet Loss with
Channel Attention for Person Re-identification
Paper Code: D61 (Full) Abstract Paper Additional File
·
Möller,J., Meyer,B., Eisemann,M.: Porting A Visual
Inertial SLAM Algorithm To Android Devices
Paper Code: A79 (Short) Abstract Paper
Session G Wednesday 10:30 - 12:10
·
Leipnitz,A., Strutz,T., Jokisch, O.: Performance
Assessment of Convolutional Neural Networks for Semantic Image Segmentation
Paper Code: B61 (Full) Abstract Paper Additional File
·
Roig-Maimo, MF., Mas-Sanso,R.: Collateral effects of
the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices
Paper Code: C89 (Full) Abstract Paper Additional File
·
Qureshi,H.S., Wizcorek,R.: Curb Detection for a
Pedestrian Assistance System using End-to-End Learning
Paper Code: C02 (Full) Abstract Paper Additional File
·
Munoz-Arango,Juan S., Reiners,D., Cruz-Neira, C.:
Analyzing pixel spread correlation with lenticular lens efficiency on multi
user VR displays
Paper Code: C19 (Full) Abstract Paper Additional File
·
Khemmar,R., Gouveai,M., Decoux,D., Ertaud,J.Y.: Real
Time Pedestrian and Object Detection and Tracking-based Deep Learning.
Application to Drone Visual Tracking
Paper Code: B83 (Short) Abstract Paper Additional File
Session H - Common Photo - Wednesday 12:10 - 12:30
COMMON PHOTO or VIDEO - DO NOT
MISS IT
Session K Wednesday 14:00 - 15:40
·
Shirley, T., Presnov, D., Kolb, A.: A Lightweight
Approach to 3D Measurement of Chronic Wounds
Paper Code: B67 (Full) Abstract Paper Additional File
·
Wallelign,S., Polceanu M., Jemal T., Buche C.,: Coffee
Grading with Convolutional Neural Networks using Small Datasets with High
Variance
Paper Code: C61 (Full) Abstract Paper Additional File
·
Rotger, G., Moreno-Noguer, F., Lumbreras, F., Agudo,
A.: Detailed 3D Face Reconstruction from a Single RGB Image
Paper Code: C43 (Full) Abstract Paper Additional File
·
Wegen, O., Trapp, M., Döllner, J., S. Pasewaldt: Performance
Evaluation and Comparison of Service-based Image Processing based on Software
Rendering
Paper Code: D05 (Full) Abstract Paper
·
Bouressace Hassina: Title Segmentation in Arabic
Document Pages
Paper Code: B89 (Short) Abstract Paper
Session L Wednesday 16:00 - 17:30
·
Smolik,M.: Radial Basis Functions and Space
Subdivision for Large Scattered Vector Fiields Approximation
Paper Code: Z1 (Tutorial) Abstract Paper Additional File
·
Katragadda,S.: Identification of stereo rig alignment
error based on vertical disparity map
Paper Code: D17 (Full) Abstract Paper Additional File
·
Barina,D., Chlubna,T., Solony,M., Dlabaja,D., Zemcik,P.:
Evaluation of 4D Light Field Compression Methods
Paper Code: C13 (Full) Abstract Paper Additional File
·
Zechmeister, S., Cornel, D., Waser, J.: 3D Annotations
for Geospatial Decision Support Systems
Paper Code: D13 (Full) Abstract Paper Additional File
·
Hemani M., Sinha A., Balaji K.: Stylized Sketch
Generation using Convolutional Networks
Paper Code: B97 (Full) Abstract Paper Additional File
·
Pérez Gutierrez, M., Córdova-Cruzatty, A.: Obstacle
detection algorithm by means of images with a ZED camera using ROS software in
a drone
Paper Code: D29 (Short) Abstract Paper
CONFERENCE DINNER
Is to be organized outside
Order DISCOUNTED ticket for the Conference Dinner ticket at the
Conference REGISTRATION desk – places are limited
(Monday or Tuesday morning)
!!! DINNER IS NOT at the Primavera Hotel and Congress Center !!!
Session M Thursday 8:30 - 10:10
·
Friedrich,M., Guimerà Cuevas,F., Sedlmeier,A.,
Ebert,A.: Evolutionary Generation of Primitive-Based Mesh Abstractions
Paper Code: A61 (Full) Abstract Paper Additional File
·
Novikov,M. M., Galeev,R. M., Knyaz,V. A.,: Creating
digital models of paleontological sample by photogrammetry and computed
tomography
Paper Code: A71 (Full) Abstract Paper Additional File
·
Cibulski,L.,May,T.,Preim,B.,Bernard,J.,Kohlhammer,J.:
Visualizing Time Series Consistency for Feature Selection
Paper Code: C37 (Full) Abstract Paper Additional File
·
Marín,C., Chover,M., Sotoca,J.M.: Prototyping a game
engine architecture as a multi-agent system
Paper Code: B29 (Short) Abstract Paper Additional File
Session N Thursday 10:30 - 12:00
·
Karolyi,M., Krejci,J.,
Scavnicky,J., Vyskovsky,R., Komenda,M.: Tools for development
of interactive web-based maps: application in healthcare
Paper Code: A31 (Short) Abstract Paper
·
Blum,S., Cetin,G., Stuerzlinger,W.: Immersive
Analytics Sensemaking on Different Platforms
Paper Code: D31 (Short) Abstract Paper Additional File
·
Saddiqa, M., Rasmussen, L., Larsen, B., Magnussen, R.,
Pedersen, J.M.: Open Data Visualization in Danish Schools: A case Study
Paper Code: B07 (Short) Abstract Paper Additional File
·
Silva,M., Martino,J.: Improving facial attraction in
videos
Paper Code: D23 (Short) Abstract Paper
·
Trapp, M., Pasewaldt, S., Döllner, J.: Techniques for
GPU-based Color Palette Mapping
Paper Code: D71 (Short) Abstract Paper
·
Dubrovskaya, M: Automatic Human Heart Search on
CT-Scans
Paper Code: B43 (Short) Abstract Paper
Session P - Closing Session - Thursday 12:00 - 12:10
Guided tour on Thursday will
probably start at 15:00 at the Center of the Plzen City
Information will be on the conference registration
desk.
INFORMATION for Presenters
See the WEB site for latest details
It is recommended to use your own computer for presentation.
Presenters are requested to verify presentation in advance – time slot is to be kept strictly
Available
· Data projector with standard PC interface
· PC (not fast) with Windows
If you use Apple or other platform, make sure that you have all the convertors for power supply and data projector connection
ABSTRACTS
FULL PAPERS
A37: Fast Adaptive Undersampling for Volume Rendering
Belyaev, S.Y., Smirnov, P.O., Smirnova, N.D., Shubnikov, V.G.
Abstract:
Adaptive undersampling is a method for accelerating the rendering process by
replacing the calculation of a volume integral with an interpolation procedure
for a number of pixels. In this paper, we propose a method for accelerating the
volume integral calculation for the rest of the pixels, i.e. those pixels for
which interpolation cannot be done with sufficient accuracy. This method
requires two passes through the input data. On the first pass, rendering is
done into a low-resolution texture. At this stage, the values of the volume
integral on a set of intervals of a given length are calculated and saved into
a special G-buffer alone with the pixel’s color. On the second pass, these values
are used to determine colors of the pixels. For those pixels whose result is
not precise enough, the volume integral is calculated on one or several
intervals, rather than the whole ray. The proposed method allows one to
accelerate adaptive undersampling by a factor of 1.5 on average, depending on
the input data.
A41: Silhouette Extraction for Shadow Volumes Using Potentially Visible Sets
Kobrtek, J., Milet, T., Herout, A.
Abstract:
In this paper we present a novel approach for accelerated silhouette computation
based on potentially visible sets stored in the octree acceleration structure.
Scene space, where the light source can appear, is subdivided into voxels. The
octree voxels contain two precomputed sets of edges that potentially or always
belong to the silhouette. We also propose a novel method of octree compression
for reduction of the memory footprint of the resulting acceleration structure.
Using our novel technique we were able to considerably decrease the silhouette
computational complexity and reduce its sensitivity to the number of edges.
D19: Automated Object Tracking in Sterile Pharmacy Compounding
Qi,Z., Lee,J.K.
Abstract:
A new method to automatically track objects in the process of sterile pharmacy
compounding is introduced. The method performs the automatic object tracking by
applying a simple object detection followed by an object tracking
algorithm.
The advantages of the method are: it is simple and efficient; it enables both
single and multiple objects tracking; and it does not need large sets of
templates, nor any training set. The experimental results show that the method
reasonably tracks the objects accurately.
A43: Cubic Spline Interpolation in Real-Time Applications using Three Control Points
Ogniewski, J.
Abstract:
Spline interpolation is widely used in many different applications like
computer graphics, animations and robotics. Many of these applications are run
in real-time with constraints on computational complexity, thus fueling the
need for computational inexpensive, real-time, continuous and loop-free data
interpolation techniques.
Often Catmull-Rom splines are used, which use four control points: the two
points between which to interpolate as well as the point directly before and
the one directly after. If interpolating over time, this last point will lie in
the future. However, in real-time applications future values may not be known
in advance, meaning that Catmull-Rom splines are not applicable.
In this paper we introduce another family of interpolation splines (dubbed
Three-Point-Splines) which show the same characteristics as Catmull-Rom, but
which use only three control points, omitting the one “in the future”.
Therefore they can generate smooth interpolation curves even in applications
which do not have knowledge of future points, without the need for more
computational complex methods. The generated curves are more rigid than
Catmull-Rom, and because of that the Three-Point-Splines will not generate
self-intersections within an interpolated curve segment, a property that has to
be introduced to Catmull-Rom by careful parameterization. Thus, the
Three-Point-Splines allow for greater freedom in parameterization, and can
therefore be adapted to the application at hand, e.g. to a requested curvature
or limitations on acceleration/deceleration.
We will also show a method that allows to change the control points during an
ongoing interpolation, both with Thee-Point-Splines as well as with Catmull-Rom
splines.
A61: Evolutionary Generation of Primitive-Based Mesh Abstractions
Friedrich,M., Guimerà Cuevas,F., Sedlmeier,A., Ebert,A.
Abstract:
The procedural generation of data sets for empirical algorithm validation and
deep learning tasks in the area of primitive-based geometry is cumbersome and time-consuming
while ready-to-use data sets are rare.
We propose a new and highly flexible framework based on Evolutionary Computing
that is able to create primitive-based abstractions of existing triangle meshes
favoring fast running times and high geometric variation over reconstruction
precision.
These abstractions are represented as CSG trees to widen the scope of possible
applications.
As part of the evaluation, we show how we successfully used the generator to
create a data set for the evaluation of neural point cloud segmentation
pipelines and additionally explain how to use the system to create artistic
abstractions of meshes provided by publicly available triangle mesh databases.
A71: Creating digital models of paleontological sample by photogrammetry and computed tomography
Novikov,M. M., Galeev,R. M., Knyaz,V. A.,
Abstract:
The specificity of paleoanthropological research and accurate anthropological
documentation is due to the complexity of the form of anthropological objects,
their uniqueness, high historical and scientific value. The main purpose of
this work is to develop methods for creating digital 3D models with a high
degree of informativity for the development of digital documentation systems of
paleoanthropological objects. The article presents a comparative analysis of
digital models of anthropological objects obtained using photogrammetry and
computed tomography. It is shown that the combined use of non-contact methods
of videogrammetry and computed tomography allows to create high-precision
three-dimensional models with photorealistic texture and accurate internal and
hidden geometry. The proposed approach allows not only to create virtual
collections for wide sharing of specialists, but also using modern methods of
additive manufacturing to make exact copies of unique artifacts.
A73: Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis
Daoud,Z., Ben Hamida, A., & Ben Amar,C
Abstract:
Recently, due to the huge damage caused by fires in many countries in the
world, fire detection is getting more and more interest as an increasing
important issue. Nowadays, the early fire detection in video surveillance
scenes is emerging as an alternative solution to overcome the shortcomings of
the current inefficient sensors. In this paper, we propose a new video
based-fire detection method exploiting color and motion information of fire.
Our approach consists in detecting all moving regions in the scene to select
then areas likely to be fire. Further, motion analysis is required to identify
the accurate fire regions. The proposed method is evaluated on different video
datasets containing diverse fire and non-fire videos. Experimental results
demonstrate the effectiveness of our proposed method by achieving high fire
detection and low false alarms rates. Moreover, it greatly outperforms the
related works with 98.81 % accuracy and only 2 % of false positive rate.
A89: An automatic stopping criterion for nonlinear anisotropic diffusion
Bustacara-Medina, C., Flórez-Valencia, L.
Abstract:
Nonlinear anisotropic diffusion (NAD) filtering is a procedure based on
nonlinear evolution PDEs which seeks to improve images qualitatively by
removing noise while preserving details and even enhancing edges. However,
well-known implementations are sensitive to parameters which are necessarily
tuned to sharpen a narrow range of edge slopes; otherwise, edges are either
blurred or staircased. One important parameter is the iterations number, for
that reason, in this paper is proposed a stopping criterion for the diffusion
process. To meet this goal, two stopping criteria were compared. The first is
the stopping criterion proposed by Joao et. al., which is based on the Mean
Squared Error (MSE). The second is our proposed method based on the CIRR
contrast measure. To this end, a comparative analysis of five diffusion methods
is performed. Four of them are nonlinear anisotropic diffusion methods and the
fifth is the Perona-Malik method. According to the tests performed, the number
of iterations required by the smoothing algorithms using the proposed stopping
criterion is lower.
B03: Deep Learning-based Overlapping-Pigs Separation by Balancing Accuracy and Execution Time
Lee,H., Sa,J., Shin,H., Chung,Y., Park,D., Kim,H.
Abstract:
The crowded environment of a pig farm is highly vulnerable to the spread of
infectious diseases such as foot-and-mouth disease, and studies have been
conducted to automatically analyze behavior of pigs in a crowded pig farm
through a video surveillance system using a top-view camera. Although it is
required to correctly separate overlapping-pigs for tracking each individual
pigs, extracting the boundaries of each pig fast and accurately is a
challenging issue due to the complicated occlusion patterns such as X shape and
T shape. In this study, we propose a fast and accurate method to separate
overlapping-pigs not only by exploiting the advantage (i.e., one of the fast
deep learning-based object detectors) of You Only Look Once, YOLO, but also by
overcoming the disadvantage (i.e., the axis aligned bounding box-based object
detector) of YOLO with the test-time data augmentation of rotation.
Experimental results with the occlusion patterns between the overlapping-pigs
show that the proposed method can provide better accuracy and faster processing
speed than one of the state-of-the-art deep learning-based segmentation
techniques such as Mask R-CNN (i.e., the performance improvement over Mask
R-CNN was about 11 times, in terms of the accuracy/processing speed performance
metrics).
B05: Floor Map Visualizations of Medical Volume Data
Merten,N., Saalfeld,S., Preim,B.
Abstract:
Typically, volumetric medical image data is examined by assessing each slice of
an image stack individually. However, this enables observers to assess in-plane
spatial relationships between anatomical structures only and requires them to
keep track of relationships along the third anatomical plane mentally.
Therefore, visualization techniques are researched to support this task by
depicting spatial information along the third plane, but they can introduce a
high degree of abstraction. To overcome this, we present a novel approach that
transforms image stacks with labeled anatomical structures into maps with a
three-dimensional layout, namely floor maps. Since this approach increases the
visual complexity under certain conditions, some clinical application
scenarios, e.g. diagnosis and therapy planning, probably will not benefit.
Thus, the approach is mainly aimed to support student training and the generation
of clinical reports. We also discuss how to enhance the slice-based exploration
of medical image stacks via floor maps and present the results of an informal
evaluation with three trained anatomists.
B17: Multi-Resolution Rendering for Computationally Expensive Lighting Effects
Besenthal, S., Maisch, S., Ropinski, T.
Abstract:
Many lighting methods used in computer graphics such as indirect illumination
can have very high computational costs and need to be approximated for real
time applications. These costs can be reduced significantly by using upsampling
techniques which tend to introduce artifacts and affect the visual quality of
the rendered image. This paper introduces a versatile approach for accelerating
the rendering of screen space methods while maintaining the visual quality.
This is achieved by exploiting the low frequency nature of many of these
illumination methods and the geometrical continuity of the scene. First the
screen space is dynamically divided into separate subimages, then the lighting
is rendered for each subimage in an adequate resolution and lastly the
subimages are put together in order to compose the final image. This is done by
identifying edges in the scene and generating masks precisely specifying which
part of the image is included in which subimage. The masks therefore determine
which part of the image is rendered in which resolution. The upsampling and
merging process of the subimages is done stepwise and allows optically soft
transitions between the different resolution levels. For this paper the
introduced multi-resolution rendering method was implemented and tested on
three commonly used lighting methods. These are screen space ambient occlusion,
soft shadow mapping and screen space global illumination.
B61: Performance Assessment of Convolutional Neural Networks for Semantic Image Segmentation
Leipnitz,A., Strutz,T., Jokisch, O.
Abstract:
Convolutional neural networks are applied successfully for image classification
and object detection. Recently, they have been adopted to semantic segmentation
tasks and several new network architectures have been proposed. With respect to
automotive applications, the Cityscapes dataset is often used as a benchmark.
It is one of the biggest datasets in this field and consists of a training, a
validation, and a test set. While training and validation allow the
optimisation of these nets, the test dataset can be used to evaluate their
performance.
Our investigations have shown that while these networks perform well for images
of the Cityscapes dataset, their segmentation quality significantly drops when
applied to new data. It seems that they have limited generalisation abilities.
In order to find out whether the image content itself or other image properties
cause this effect, we have carried out systematic investigations with modified
Cityscapes data. We have found that camera-dependent image properties like
brightness, contrast, or saturation can significantly influence the segmentation
quality. This papers presents the results of these tests including eight
state-of-the-art CNNs. It can be concluded that the out-of-the-box usage of
CNNs in real-world environments is not recommended.
B67: A Lightweight Approach to 3D Measurement of Chronic Wounds
Shirley, T., Presnov, D., Kolb, A.
Abstract:
This paper presents a light-weight process for 3D reconstruction and
measurement of chronic wounds using a commonly available smartphone as an image
capturing device. The first stage of our measurement pipeline comprises the
creation of a dense 3D point cloud using structure-from-motion (SfM).
Furthermore, the wound area is segmented from the surrounding skin using
dynamic thresholding in CIELAB color space and a surface is estimated to
simulate the missing skin in the
wound area. Together with a mesh reconstruction of the wound, the skin surface
and the segmented wound is used to calculate the wound dimensions, i.e., its
length, surface area and volume. We evaluate the presented pipeline using three
wound phantoms, representing different stages in healing, and compare the
subsequently scanned and measured wound dimensions with manually measured ones.
B97: Stylized Sketch Generation using Convolutional Networks
Hemani M., Sinha A., Balaji K.
Abstract:
The task of synthesizing sketches from photographs has been pursued with image
processing methods and supervised learning based approaches. The former lack
flexibility and the latter require large quantities of ground-truth data which
is hard to obtain because of the manual effort required. We present a
convolutional neural network based framework for sketch generation that does
not require ground-truth data for training and produces various styles of sketches.
The method combines simple analytic loss functions that correspond to
characteristics of the sketch. The network is trained on and evaluated for
human face images. Several stylized variations of sketches are obtained by
varying the parameters of the loss functions. The paper also discusses the
implicit abstraction afforded by the deep convolutional network approach which
results in high quality sketch output.
C02: Curb Detection for a Pedestrian Assistance System using End-to-End Learning
Qureshi,H.S., Wizcorek,R.
Abstract:
Our goal is to develop an assistance system for supporting road crossing among
older pedestrians. In order to accomplish this, we propose detecting the curb
stone from the pedestrians" point of view. To tackle this problems, we chose
to fuse two sensors, a Camera and a Leddar, and use an algorithm that applies
an end-to-end learning approach. The convolutional neural network was chosen to
process the images acquired from the mono camera by filming the curb and its
surroundings. The artificial neural network was selected to process the point
cloud data of the Leddar acquired in the form of arrays from the 16 channels of
the Leddar. A prototype was developed for data collection and testing purposes.
It consists of a structure carrying both sensors mounted on a walker. The data
from both sensors were collected with multiple factors taken into
consideration, such as, weather, light conditions and, approaching angles. For
the training of algorithms, an end-to-end learning approach was selected where
we labelled the complete image or array rather than labelling the individual
pixels or points in the data. The networks were trained and, the features from
the parallel networks were concatenated and given as the input to the fully
connected layers to train the complete network. The experimental results show
an accuracy of more than 99\% and robustness of the end-to-end learning
approach. Both sensors are relatively inexpensive and are in fusion together,
they are able to efficiently accomplish the task of detecting the curb stone
from the pedestrians" point of view.
C11: SyCaT-Vis: Visualization-Based Support of Analyzing System Behavior based on System Call Traces
Hausdorf,A., Hinzmann,N., Zeckzer,D.
Abstract:
Detecting anomalies in the behavior of a computer system is crucial for
determining its security. One way of detecting these anomalies is based on the
assessment of the amount and sequence of system calls issued by processes.
While the number of processes on a computer can become very large, the number
of system calls issued during the lifespan of such a process and its
subprocesses can be humongous. In order to decide whether these anomalies are
due to the intended system usage or if they are caused by malicious actions,
this humongous amount of data needs being analyzed. Thus, a careful analysis of
the system calls’ types, their amount, and their temporal sequence requires
sophisticated support. Visualization is frequently used for this type of tasks.
Starting with a carefully aggregation of the data presented in an overview
representation, the quest for information is supported by carefully crafted
interactions. These allow filtering the tremendous amount of data, thus
removing the standard behavior data and leaving the potentially suspicious one.
The latter can then be investigated on increasingly finer levels. Supporting
this goal-oriented analysis, we propose novel interactive visualizations
implemented in the tool SyCaT-Vis. SyCaT-Vis fosters obtaining important
insights into the behavior of computer systems, the processes executed, and the
system call sequences issued.
C13: Evaluation of 4D Light Field Compression Methods
Barina,D., Chlubna,T., Solony,M., Dlabaja,D., Zemcik,P.
Abstract:
Light field data records the amount of light at multiple points in space,
captured by either an array of cameras or by a light-field camera that uses
microlenses. Since the storage and transmission requirements for such data are
tremendous, compression techniques for light fields are gaining momentum in
recent years. Although plenty of efficient compression formats do exist for
still and moving images, there is only a little research on the impact of these
methods on light field imagery. In this paper, we evaluate the impact of
state-of-the-art image and video compression methods on quality of images
rendered from light field data. The methods include recent video compression
standards, especially AV1 and XVC finalised in 2018. To fully exploit the
potential of common image compression methods on four-dimensional light field
imagery, we have extended these into three and four dimensions. In the paper,
we show that the four-dimensional light field data can be compressed much more
than independent still images while maintaining the same visual quality of a perceived
picture. We gradually compare the compression performance of all image and
video compression methods, and eventually answer the question, "What is
the best compression method for light field data?".
C17: Lens-based Focus+Context Visualization Techniques for Interactive Exploration of Web-based Reachability Maps
Listemann, M., Trapp, M., Döllner, J.
Abstract:
Reachability maps are powerful means to help making location-based decisions,
such as choosing convenient sites for subsidiaries or planning vacation trips.
Existing visualization approaches, however, introduce drawbacks concerning an
effective acquisition of relevant information and an efficient data processing.
In this paper, we introduce the first approach known so far to apply focus+context
techniques to web-based reachability maps. We therefore propose a real-time
enabled combination of an isochrone-based and a network-based representation of
travel times obtained from multi-modal routing analyses using interactive
lenses. We furthermore present a GPU-accelerated image-based method to compute
isochrones based on a travel time-attributed network on client-side and thus
achieve reduced data transmission efforts while yielding isochrones of higher
precision, compared to generalized geometry-based approaches.
C19: Analyzing pixel spread correlation with lenticular lens efficiency on multi user VR displays
Munoz-Arango,Juan S., Reiners,D., Cruz-Neira, C.
Abstract:
One of the all time issues with Virtual Reality systems regardless if they are
head-mounted or projection based is that they can only provide perspective
correctness to one user. Such limitation affects collaborative work which is
nowadays the standard anywhere. One of the approaches for generating different
perspective correct images to several users is through optical routing. This
approach relies on bending the light to generate perspective correct images to
the engaged users. Lenticular lenses can bend the light to be able to generate
perspective correct images for several users depending on their positions. On
this paper we present an analysis that lets us understand the pixel spread
correlation with lenticular lens efficiency on multi user VR displays.
C37: Visualizing Time Series Consistency for Feature Selection
Cibulski,L.,May,T.,Preim,B.,Bernard,J.,Kohlhammer,J.
Abstract:
Feature selection is an effective technique to reduce dimensionality, for
example when the condition of a system, e.g. a sensor-equipped car, is to be
understood from multivariate observations. The selection of variables often
involves a priori assumptions about underlying phenomena. To avoid the
associated uncertainty, we aim at a selection criterion that only relies on the
observations. For nominal data, consistency criteria meet this requirement: a
variable subset is consistent, if no observations with equal values on the
subset have different output values. Such a model-agnostic criterion is also
desirable for forecasting. However, consistency has not yet been applied to
multivariate time series. In this work, we propose a visual consistency-based
technique for analyzing a time series subset"s discriminating ability with
respect to temporal observations. An overview visualization conveys the
consistency of output progressions that are associated with comparable
observations. Interaction concepts and detail visualizations enable analysts to
steer the feature selection process towards inconsistencies. We demonstrate the
technique"s applicability based on two real-world usage scenarios. The
results indicate that the technique allows analysts to assess the combined
discriminating ability of time series without any knowledge about underlying
phenomena. It is therefore open to any application that involves multivariate
time series for forecasting purposes.
C41: Anatomy-Focused Volume Line Integral Convolution for Brain White Matter Visualization
Schult,T., Klose,U., Hauser,T.-K., Ehricke,H.-H.
Abstract:
3D visualization of volumetric line integral convolution (LIC) datasets has
been a field of constant research. So far, most approaches have focused on
finding suitable transfer functions and defining appropriate clipping
strategies in order to solve the problem of occlusion. In medicine, extensions
of the LIC algorithm to diffusion weighted magnetic resonance imaging (dwMRI)
have been proposed, allowing highly resolved LIC volumes to be generated. These
are used for brain white matter visualization by LIC slice images, depicting
fiber structures with good contrast. However, 3D visualization of fiber pathways
by volume rendering faces the problem of occlusion of anatomic regions of
interest by the dense brain white matter pattern. In this paper, we introduce
an anatomy-focused LIC algorithm, which allows specific fiber architectures to
be visualized by volume rendering. It uses an anatomical atlas, matched to the
dwMRI dataset, during the generation of the LIC noise input pattern. Thus,
anatomic fiber structures of interest are emphasized, while surrounding fiber
tissue is thinned out and its opacity is modulated. Additionally, we present an
adaptation of the orientation-dependent transparency rendering algorithm, which
recently has been proposed for fiber streamline visualization, to LIC data. The
novel methods are evaluated by application to dwMRI datasets from glioma
patients, visualizing fiber structures of interest in the vicinity of the
lesion.
C43: Detailed 3D Face Reconstruction from a Single RGB Image
Rotger, G., Moreno-Noguer, F., Lumbreras, F., Agudo, A.
Abstract:
This paper introduces a method to obtain a detailed 3D reconstruction of facial
skin from a single RGB image. To this end, we propose the exclusive use of an
input image without requiring any information about the observed material nor
training data to model the wrinkle properties. They are detected and
characterized directly from the image via a simple and effective parametric
model, determining several features such as location, orientation, width, and
height. With these ingredients, we propose to minimize a photometric error to
retrieve the final detailed 3D map, which is initialized by current techniques
based on deep learning. In contrast with other approaches, we only require
estimating a depth parameter per wrinkle, making our approach fast and intuitive.
Extensive experimental evaluation is presented in a wide variety of synthetic
and real images, including different skin properties and facial expressions. In
all cases, our method outperforms the current approaches regarding 3D
reconstruction accuracy, providing striking results for both large and fine
wrinkles.
C47: Dynamic Radiosity
Shcherbakov,A.,Frolov,V.
Abstract:
In this paper we propose novel radiosity implementation which we called . By
storing and updating local form factor matrix of closest to the observer
patches we have solved 2 main problems of radiosity algorithm: (1) quadratic
complexity of algorithm and thus difficulty of application it to large-scale
scenes, and (2) possibility of changing geometry on the fly (dynamic geometry).
C53: Corotated meshless implicit dynamics for deformable bodies
Brunet,J.-N., Magnoux,V., Ozell,B., Cotin,S.
Abstract:
This paper proposes a fast, stable and accurate meshless method to simulate
geometrically non-linear elastic behaviors. To address the inherent limitations
of finite element (FE) models, the discretization of the domain is simplified
by removing the need to create polyhedral elements. The volumetric locking
effect exhibited by incompressible materials in some linear FE models is also
completely avoided. Our approach merely requires that the volume of the object
be filled with a cloud of points. To minimize numerical errors, we construct a
corotational formulation around the quadrature positions that is well suited
for large displacements containing small deformations. The equations of motion
are integrated in time following an implicit scheme. The convergence rate and
accuracy are validated through both stretching and bending case studies.
Finally, results are presented using a set of examples that show how we can
easily build a realistic physical model of various deformable bodies with
little effort spent on the discretization of the domain.
C61: Coffee Grading with Convolutional Neural Networks using Small Datasets with High Variance
Wallelign,S., Polceanu M., Jemal T., Buche C.,
Abstract:
Convolutional Neural Networks (CNNs) have been established as a powerful class
of models for image recognition problems. Despite their success in other areas,
CNNs have been applied only for very limited agricultural applications due to
the need for large datasets. The aim of this research is to design a robust CNN
model that classifies raw coffee beans into their 12 quality grades using small
datasets which have high data variability. The dataset contains images of raw
coffee beans acquired in two sets using different acquisition technique under
varying illuminations which poses a complex challenge to designing a robust
model. To design the model, preprocessing techniques were applied to the input
in order to reduce task irrelevant features. But adding the preprocessing
techniques did not improve the performance of the CNN model for our dataset. We
have also used ensemble methods to solve the high variance that exists in
networks when working with small datasets. Finally, we were able to design a
model that classifies the beans into their quality grades with an accuracy of
89.01% on the test dataset.
C67: Straight Skeleton Computation Optimized for Roof Model Generation
KENICHI, SUGIHARA
Abstract:
3D building models are important in several fields, such as urban planning and
BIM (Building Information Model). However, enormous labor has to be consumed to
create these 3D models. In order to automate laborious steps, a GIS and CG
integrated system is proposed for automatically generating 3D building models,
based on building polygons (building footprints) on digital maps. Digital maps
show most building polygons" edges meet at right angles (orthogonal
polygon). In either orthogonal or non-orthogonal polygons, we proposed for
automatically generating 3D building models with general shaped roofs by
straight skeleton computation, which was proposed through two events. However,
some roofs are not created by these two events. In this paper, extended
methodologies are proposed for adding degeneration events besides the
conventional events, and monotone polygon nodes sorting.
C83: Pi-surfaces: products of implicit surfaces towards constructive composition of 3D objects
Raposo,A.N., Gomes,A.J.P.
Abstract:
Implicit functions provide a fundamental basis to model 3D objects, no matter
they are rigid or deformable, in
computer graphics and geometric modeling. This paper introduces a new
constructive scheme of implicitly-defined
3D objects based on products of implicit functions. This scheme is in contrast
with popular approaches like
blobbies, meta balls and soft objects, which rely on the sum of specific
implicit functions to fit a 3D object to a set
of spheres.
C89: Collateral effects of the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices
Roig-Maimo, MF., Mas-Sanso,R.
Abstract:
We have developed an image-based head-tracker interface for mobile devices that
uses the information of the front camera to detect and track the user"s
nose position and translate its movements into a pointing metaphor to the
device. However, as already noted in the literature, the measurement errors of
the motion tracking leads to a noticeable jittering of the perceived motion. To
counterbalance this unpleasant and unwanted behavior, we have applied a Kalman
filter to smooth the obtained positions.
In this paper we focus on the effect that the use of a Kalman filter can have
on the throughput of the interface. Throughput is the human performance measure
proposed by the ISO 9241-411 for evaluating the efficiency and effectiveness of
non-keyboard input devices. The softness and precision improvements that the
Kalman filter infers in the tracking of the cursor are subjectively evident.
However, its effects on the ISO"s throughput have to be measured
objectively to get an estimation of the benefits and drawbacks of applying a
Kalman filter to a pointing device.
D02: Exhibition and Evaluation of Two Schemes for Determining Hypersurface Curvature in Volumetric Data
Hauenstein, J., Newman, T.
Abstract:
Advancements in methodologies for determining 3-dimensional manifold
(hypersurface) curvature in volumetric data are presented. Such determinations
are requisite in certain shape-based visualization and analysis tasks. The methods
explored here are convolution-based approaches. In addition to motivating and
describing these methods, an evaluation of their (1) accuracy and (2)
computational performance is also presented. That evaluation includes
benchmarking on both noise-free and noisy volumetric data.
D03: Grayscale and Color Basis Images
Valery Gorbachev, Elena Kaynarova, Anton Makarov, Elena Yakovleva
Abstract:
Orthogonal transformation of digital images can be represented as a decomposition
over basis matrices or basis images. Grayscale and color basis images are
introduced. For particular case of DWT found basis wavelet images have a block
structure similar to frequency bands of the the DWT coefficients. A
steganographic scheme for frequency domain watermarking based on this
representation is considered. Presented example of detection algorithm
illustrates how this representation can be used for frequency embedding
techniques.
D05: Performance Evaluation and Comparison of Service-based Image Processing based on Software Rendering
Wegen, O., Trapp, M., Döllner, J., S. Pasewaldt
Abstract:
This paper presents an approach and performance evaluation of performing
service-based image processing using software rendering implemented using
Mesa3D. Due to recent advances in cloud computing technology (w.r.t. both,
hardware and software) as well as increased demands of image processing and
analysis techniques, often within an eco-system of devices, it is feasible to
research and quantify the impact of service-based approaches in this domain
w.r.t. cost-performance relation. For it, we provide a performance comparison
for service-based processing using GPU-accelerated and software rendering.
D07: Accounting for Object Weight in Interaction Design for Virtual Reality
Lykke,J.R., Olsen,A.B., Berman,P., Bærentzen,J.A., Frisvad,J.R.
Abstract:
Interaction design for virtual reality (VR) rarely takes the weight of an
object - let alone its moment of inertia - into account. This clearly reduces user
immersion and could lead to a break-in-presence. In this work, we propose
methods for providing a higher fidelity in interactions with virtual objects.
Specifically, we present different methods for picking up, handling, swinging,
and throwing objects based on their weight, size, and affordances. We conduct
user studies in order to gauge the differences in performance as well as sense
of presence of the proposed techniques compared to conventional interaction
techniques. While these methods all rely on the use of unmodified VR
controllers, we also investigate the difference between using controllers to
simulate a baseball bat and swinging a real baseball bat. Interestingly, we
find that realism of the motions during interaction is not necessarily an important
concern for all users. Our modified interaction techniques, however, have the
ability to push user performance towards the slower motions that we observe
when a real bat is used instead of a VR controller on its own.
D13: 3D Annotations for Geospatial Decision Support Systems
Zechmeister, S., Cornel, D., Waser, J.
Abstract:
In virtual 3D environments, it is easy to lose orientation while navigating or
changing the view with zooming and panning operations. In the real world,
annotated maps are an established tool to orient oneself in large and unknown
environments. The use of annotations and landmarks in traditional maps can also
be transferred to virtual environments. But occlusions by three-dimensional
structures have to be taken into account as well as performance considerations
for an interactive real-time application. Furthermore, annotations should be
discreetly integrated into the existing 3D environment and not distract the
viewer"s attention from more important features. In this paper, we present
an implementation of automatic annotations based on open data to improve the
spatial orientation in the highly interactive and dynamic decision support
system Visdom. We distinguish between line and area labels for object-specific
labeling, which facilitates a direct association of the labels with their
corresponding objects or regions. The final algorithm provides clearly visible,
easily readable and dynamically adapting annotations with continuous levels of
detail integrated into an interactive real-time application.
D17: Identification of stereo rig alignment error based on vertical disparity map
Katragadda,S.
Abstract:
Poor quality of 3D video content can lead to headache, blurry vision and overall
exhaustive experience for the viewer. To ensure quality and comfortable 3D
experience for the end consumer, common production errors must be detected and
corrected. Vertical disparity is one of these distortions and is caused by
improper stereo-camera setup. This paper aims at identifying the possible
rotational and placement errors that cause the vertical disparity. An
estimation of these errors is necessary to produce good quality 3D content.
According to our knowledge, there exists no method to identify rig alignment
errors without the knowledge of camera setup parameters and this work is the
first step in that direction. Feature point detection has proven to be an
interesting approach to find vertical disparity present in the given stereo
image pair. In this work feature extraction techniques such as SIFT, SURF and
Harris features are efficiently used to compute reliable and robust vertical
disparity patterns. This paper classifies vertical disparity patterns according
to rig errors. If the vertical disparity values are too small or ambiguous to
be identified by pattern analysis, this paper uses graphical analysis that
highlights the relationship between the total vertical disparity and the
contribution of each possible error to the total. Experimental results show
that the proposed approach identifies the reason behind the presence of
vertical disparity of some stereo image pairs.
D43: Compressed Exposure Sequences for HDR Imaging
Sekmen, S., Akyüz, A. O.
Abstract:
High dynamic range (HDR) imaging techniques allow photographers to capture the
luminance distribution in the real-world as it is, freeing them from the
limitations of capture and display devices. One common approach for creating
HDR images is the multiple exposures technique (MET). This technique is
preferred by many photographers as multiple exposures can be captured with
off-the-shelf digital cameras and later combined into an HDR image. In this
study, we propose a storage scheme in order to simplify the maintenance and
usability of such sequences.
In our scheme, multiple exposures are stored inside a single JPEG file with the
main image representing a user-selected reference exposure. Other exposures are
not directly stored, but rather their differences with each other and the
reference is stored in a compressed manner in the metadata section of the same
file. This allows a significant reduction in file size without impacting
quality. If necessary the original exposures can be reconstructed from this
single JPEG file, which in turn can be used in a standard HDR workflow.
D47: The City Metaphor in Software Visualization
Clinton L. Jeffery
Abstract:
A city metaphor has become a popular method of visualizing properties of
program code. This paper provides an overview of research projects that employ
this metaphor for a wide range of software engineering tasks. Thusfar projects
employing the city metaphor have primarily focused on visualizing static and
semi-static properties of software repositories,such as understanding how a
program’s source code structure is changing over time, and who is changing
what. This paper compares these existing code cities and suggests likely
avenues of future research.
D59: Alignment of Point Clouds for Comparison of Infrastructures in Civil Engineering on Quality Control in Metrology
H.Khaksari Haddad, D. Laurendeau
Abstract:
For 3D point cloud registration, Go-ICP cite{yang2016go} has been shown to
obtain the global optimal solution for a pair composed of a model point cloud
and a data point cloud. Go-ICP mostly has been investigated only on standard
sets of point clouds. In this paper, we demonstrate the remarkable efficacy of
Go-ICP for the alignment of very complex large-scale point clouds to their
corresponding deformed CAD models. In particular, given two distinct sets of
point clouds taken from the exterior and the interior of a building,
experiments demonstrate that Go-ICP is able to successfully align both of these
sets to the point cloud of the CAD model of the whole building (both exterior
and interior information included). With the experimentation presented in this
paper, we demonstrate that Go-ICP can achieve excellent alignment results and
that this approach can be deployed in applications aiming at comparing CAD
models of a building ("as designed" model) to the point cloud of the
actual building ("as-built" model). Experiments also demonstrate the
efficacy of Go-ICP to align a deformed copy of a man-made object to the
original object in quality control applications.
D61: Triplet Loss with Channel Attention for Person Re-identification
Organisciak,D., Riachy,C., Aslam,N., Shum, H.P.H.
Abstract:
The triplet loss function has seen extensive use within person
re-identification. Most works focus on either improving the mining algorithm or
adding new terms to the loss function itself. Our work instead concentrates on
two other core components of the triplet loss that have been under-researched.
First, we improve the standard Euclidean distance with dynamic weights, which
are selected based on the standard deviation of features across the batch.
Second, we exploit channel attention via a squeeze and excitation unit in the
backbone model to emphasise important features throughout all layers of the
model. This ensures that the output feature vector is a better representation
of the image, and is also more suitable to use within our dynamically weighted
Euclidean distance function. We demonstrate that our alterations provide
significant performance improvement across popular re-identification data sets,
including almost 10% mAP improvement on the CUHK03 data set. The proposed model
attains results competitive with many state-of-the-art person re-identification
models.
D67: 3D Gaussian Descriptor for Video-based Person Re-Identification
Riachy,C., Al-Maadeed,N., Organisciak,D., Khelifi,F., Bouridane,A.
Abstract:
Despite being often considered less challenging than image-based person
re-identification (re-id), video-based person re-id is still appealing as it
mimics a more realistic scenario owing to the availability of pedestrian
sequences from surveillance cameras. In order to exploit the temporal
information provided, a number of feature extraction methods have been
proposed. Although the features could be equally learned at a significantly
higher computational cost, the scarce nature of labelled re-id datasets
encourages the development of robust hand-crafted feature representations as an
efficient alternative, especially when novel distance metrics or multi-shot
ranking algorithms are to be validated. This paper presents a novel
hand-crafted feature representation for video-based person re-id based on a
3-dimensional hierarchical Gaussian descriptor. Compared to similar approaches,
the proposed descriptor (i) does not require any walking cycle extraction,
hence avoiding the complexity of this task, (ii) can be easily fed into
off-shelf learned distance metrics, (iii) and consistently achieves superior
performance regardless of the matching method adopted. The performance of the
proposed method was validated on PRID2011 and iLIDS-VID datasets outperforming
similar methods on both benchmarks.
SHORT PAPERS
Karolyi,M., Krejcí,J., Štavnický,J., Vyškovský,R., Komenda,M.
Abstract:
Interactive visualisations on the Internet have become commonplace in recent
years. Based on such publicly available visualisations, users can obtain
infor-mation from various domains quickly and easily. A location-specific
method of data presentation can be much more effective using map visualisation
than using traditional methods of data visualisation, such as tables or graphs.
This paper pre-sents one of the possible ways of creating map visualisations in
a modern web environment. In particular, we introduce the technologies used in
our case togeth-er with their detailed configuration. This description can then
serve as a guide for the customisation of the server environment and application
settings so that it is easy to create the described type of visualisation
outputs. Together with this man-ual, specific cases are presented on the
example of an application which was de-veloped to display the location of
medical equipment in the Czech Republic based on data collected from healthcare
providers.
Möller,J., Meyer,B., Eisemann,M.
Abstract:
Simultaneous Localization and Mapping aims to identify the current position of
an agent and to map his surroundings at the same time. Visual inertial SLAM
algorithms use input from visual and motion sensors for this task. Since modern
smartphones are equipped with both needed sensors, using VI-SLAM applications
becomes feasible, with Augmented Reality being one of the most promising
application areas. Android, having the largest market share of all mobile
operating systems, is of special interest as the target platform. For iOS there
already exists a high-quality open source implementation for VI-SLAM: The
framework VINS-Mobile.
In this work we discuss what steps are necessary for porting it to the Android
operating system. We provide a practical guide to the main challenge: The
correct calibration of device specific parameters for any Android smartphone.
We present our results using the Samsung Galaxy S7 and show further improvement
possibilities.
Trapp, M., Pasewaldt, S., Döllner, J.
Abstract:
This paper presents a GPU-based approach to color quantization by mapping of
arbitrary color palettes to input images using Look-Up Tables (LUTs). For it,
different types of LUTs, their GPU-based generation, representation, and
respective mapping implementations are described and their run-time performance
is evaluated and compared.
Saddiqa, M., Rasmussen, L., Larsen, B., Magnussen, R., Pedersen, J.M.
Abstract:
Increasingly public bodies and organizations are publishing Open Data for citizens
to improve their quality of life and solving public problems. But having Open
Data available is not enough. Public engagement is also important for
successful Open Data initiatives. There is an increasing demand for strategies
to actively involve the public exploiting Open Data, where not only the
citizens but also school children and young people are able to explore,
understand and extract useful information from the data, grasp the meaning of
the information, and to visually represent findings. In this research paper, we
investigate how we can equip our future generation with the skills to
understand and make use of Open Data. We present the results of a survey among
Danish school teachers and pupils. The survey focuses on how we can introduce
interactive visualization using Open Data, and which competencies are needed
for pupils and teachers to understand, use and present the data. We briefly
review Copenhagen city’s Open Data and existing open source software suitable
for visualization, to study which open source software pupils can easily adapt
to visualize Open Data and which data-sets teachers can relate to their
teaching themes. Our study shows that introducing Open Data visualization can
bring fruitful outcomes in everyday teaching and that to actively use Open Data
in schools, teachers and pupils need to boost their digital skills.
Marín,C., Chover,M., Sotoca,J.M.
Abstract:
The game engines are one of the essential and daily used applications on the
game development field. These applications are designed to assist in the
creation of this type of contents. Nevertheless, their usage and development
are very complex. Moreover, there are not many research papers about the game engine
architecture definition and specification. In this sense, this work presents a
methodology to specify a game engine defined as a multi-agent system. In such a
way, from a multi-agent approximation, a game engine architecture can be
prototyped in a fast way. Also, this implementation can be exported to a
general programming language for maximum performance, facilitating the
definition and comprehension of the mechanisms of the game engine.
Dubrovskaya, M
Abstract:
Here is proposed a solution for actual problem of heart allocation on
tomography data. One of the most popular tomography types used for heart is CT,
thus current technology, proposed here, is initially developed for CT data. The
main part of the algorithm consists of four stages: preliminary source volume
limitation, forming hypotheses of lungs location, determination of approximate
surrounding heart cube and searching for a point within the cube which is for
sure inside the heart. The algorithm also includes a preliminary stage:
coordinate system allocation. This stage is essential when position of a
scanned patient is unknown relative to a tomograph. During the stage we find,
where left and right, top and bottom, front and back sides of human body are.
The stage consists itself also of four parts: definition of Ox and Oy axes and
determination of Oy, Oz and Ox directions, by analyzing anatomy of a patient,
with regard to standard human anatomy. Method is fast, doesn`t require any
human guidance and preliminary adjustment. The whole method is convenient as
automatic preprocessing for unlimited scope of problems, e.g. segmentation,
surface or solid model reconstruction, etc. Proposed method provides flexible
way for further local heart positioning refinement and works even for static,
not timed, tomography data.
R. Khemmar, M. Gouveai, B. Decoux, JY. Ertaud
Abstract:
This work aims to show the new approaches in embedded vision dedicated to
object detection and tracking for drone visual control. Object/Pedestrian
detection has been carried out through two methods: 1. Classical image
processing approach through improved Histogram Oriented Gradient (HOG) and
Deformable Part Model (DPM) based detection and pattern recognition methods. In
this step, we present our improved HOG/DPM approach allowing the detection of a
target object in real time. The developed approach allows us not only to detect
the object (pedestrian) but also to estimates the distance between the target
and the drone. 2. Object/Pedestrian detection-based Deep Learning approach. The
target position estimation has been carried out within image processing. After
this, the system sends instruction to the drone engine in order to correct its
position. For this visual servoing, we have developed two kinds of PID
controllers. The platform has been validated under different scenarios by
comparing measured data to ground truth data given by the GPS of the drone.
Several tests which were carried out at ESIGELEC car park and Rouen city center
validate the developed platform.
Bouressace Hassina
Abstract:
Recent studies on text line segmentation have not focused on title segmentation
in complex structure documents, which may represent the upper rows in each
article of a document page. Many methods cannot correctly distinguish between
the titles and the text, especially when it contains more than one title. In
this paper, we discuss this problem and then present a straightforward and
robust title segmentation approach. The proposed method was tested on PATD
(Printed Arabic Text Database ) images and we achieved good results.
Lefkovits, S., Lefkovits, L., Szilágyi, L.
Abstract:
In this paper we present a dorsal hand vein recognition method based on
convolutional neural networks (CNN). We implemented and compared two novel CNNs
trained from end-to-end to the most important state-of-the-art deep learning
architectures (AlexNet, VGG, ResNet and SqueezeNet). We applied the transfer
learning and fine-tuning techniques for the purpose of dorsal hand vein-based
identification. The experiments carried out studied the accuracy and training
behaviour of these network architectures. The system was trained and evaluated
on the best-known database in this field, the NCUT, which contains low
resolution, low contrast images. Therefore, different pre-processing steps were
required, leading us to investigate the influence of a series of image quality
enhancement methods such as Gaussian smoothing, inhomogeneity correction,
contrast limited adaptive histogram equalization, ordinal image encoding, and
coarse vein segmentation based on geometrical considerations. The results show
high recognition accuracy for almost every such CNN-based setup.
Silva,M., Martino,J.
Abstract:
The face plays an important role both socially and culturally and has been
extensively studied especially in investigations on perception. It is accepted
that an attractive face tends to draw and keep the attention of the observer
for a longer time. Drawing and keeping the attention is an important issue that
can be beneficial in a variety of applications, including advertising,
journalism, and education. In this article, we present a fully automated
process to improve the attractiveness of faces in images and video. Our
approach automatically identifies points of interest on the face whimeasures
the distances between them while adding the use of classifiers to identify the
pattern of points of interest most adequate to improve the attractiveness.
The modified points of interest are projected in real-time in three-dimensional
face mesh to support the consistent transformation of the face in a video
sequence. In addition to the geometric transformation, texture is also
automatically smoothed through a smoothing mask and weighted sum of textures.
The process as a whole enables the improving of attractiveness not only in
images but also in videos in real time.
Pérez Gutierrez, M., Córdova-Cruzatty, A.
Abstract:
During flight conditions, Unmanned Air Vehicles (UAVs) can be in situations of
high risk such as encountering unforeseen obstacles, thus resulting in physical
damage of the device and its equipment. For this reason, an intelligent pilot
system that allows the detection and evasion of obstacles is necessary for
UAVs, protecting the integrity of the system without compromising the mission
or the established flight plan. In order to implement this intelligent pilot
system, a fundamental element was used, a ZED camera that acts in a similar way
to people"s eyes and is integrated with ROS software for depth detection
through an obstacle algorithm.
The interest of this research is focused on the obstacle detection algorithm
using a stereoscopic camera capable of creating 3D images and software that
contributes to develop applications for robots and research platforms. Once the
detection algorithm was implemented, it was tested in two specific stages, the
first stage was established to find the optimum detection distance through the
establishment of distance ranges and a second stage included controlled and
uncontrolled environments in order to carry out the algorithm validation.
Blum,S., Cetin,G., Stuerzlinger,W.
Abstract:
In this work we investigated sensemaking activities on different immersive
platforms. We observed user behaviours during a classification task on a very
large wall-display system (experiment I) and in a modern Virtual Reality
headset (experiment II). In experiment II, we also evaluated a condition with a
VR headset with an extended field of view, through a sparse peripheral display.
We evaluated the results across the two studies by analyzing quantitative and
qualitative data, such as task completion time, number of classifications,
followed strategies, and shape of clusters. The results showed differences in
user behaviours between the different immersive platforms, i.e., the very large
display wall and the VR headsets, and also point to several opportunities for
future research.
POSTERS
Basov,A.Y.,
Budak,V.P., Grimailo,A.V.
Abstract:
In this article, a new mathematical model for multiple reflections calculation
based on the Stokes vector and Mueller matrices is proposed. The global
illumination equation and local estimations method were generalized on the
polarization case. Results of the multiple reflections calculation with using
of local estimations method show the difference of more than 30% between
standard calculation and polarization-accounting one. The way of describing the
surface reflection with polarization account is proposed.
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Monday May 27, 2019 |
Registration: Monday 17:30 - 20:30 |
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Conference office - Room A |
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Tuesday, May 30, 2017 |
Late registration during breaks only |
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8:30 -10:00 |
30' |
10:30 -12:00 |
30' |
12:30-14:00 |
14:00 -15:30 |
30' |
16:00 - 18:00 |
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Session A |
Welcome |
Session B |
Free |
Lunch |
Session D |
Break & |
Session E |
FREE |
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Room B |
Room B |
Room B |
Room B |
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Wednesday, May 31, 2017 |
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8:30 -10:00 |
30' |
10:30 -12:00 |
30' |
12:30-14:00 |
14:00 -15:30 |
30' |
16:00 - 17:30 |
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18:59* |
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Session F |
Break & |
Session G |
Common |
Lunch |
Session K |
Break & |
Session L |
FREE |
DINNER |
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Room B |
Room B |
Room B |
Room B |
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Thursday, June 1, 2017 |
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8:30 -10:00 |
30' |
10:30 -12:00 |
30' |
Plzen City Sightseeing Tour ** |
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Session M |
Break & |
Session N |
Closing session |
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Room B |
Room B |
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* Place to be
announced - ticket 250 CZK/10 EUR (donated) |
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** Visit of
interesting places of the Plzen City+optional beer tasting |
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