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20 Papers

Occlusion Detection for Face Image Quality Assessment

Topics: Biometrics; Image and Video Analysis and Understanding

Authors: Jacob Carnap 1 , Alexander Kurz 2 , Olaf Henniger 2 and Arjan Kuijper 2 ; 1

Affiliations: 1 Technical University of Darmstadt, Department of Computer Science, Darmstadt, Germany , 2 Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany

Digital Object Identifier: 10.5220/0013168500003905

Proceedings of ICPRAM 2025, ISBN: 978-989-758-730-6

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The accuracy of 2D-image-based face recognition systems depends on the quality of the compared face images. One factor that affects the recognition accuracy is the occlusion of face regions, e.g., by opaque sunglasses or medical face masks. Being able to assess the quality of captured face images can be useful in various scenarios, e.g., in a border entry/exit system. This paper discusses a method for detecting face occlusions and for measuring the percentage of occlusion of a face using face segmentation and face landmark estimation techniques. The method is applicable to arbitrary face images, not only to frontal or nearly frontal face images. The method was evaluated by applying it to publicly available face image data sets and analyzing the results obtained. The evaluation shows that the proposed method enables the effective detection of face occlusions.

NetVisGame: Mobile Gamified Information Visualization of Home Network Traffic Data

Topics: Interactive Visual Interfaces for Visualization; Interface and Interaction Techniques for Visualization; Internet, Web and Security Visualization; Visualization Applications; Information and Scientific Visualization; Human-Centered Aspects of Visualization

Authors: Marija Schufrin 1 , Katharina Kuban 2 , Arjan Kuijper 1 ; 2 and Jörn Kohlhammer 1 ; 2

Affiliations: 1 Fraunhofer IGD, Germany , 2 TU Darmstadt, Germany

Digital Object Identifier: 10.5220/0010778800003124

Proceedings of IVAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

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The awareness of everyday internet users for cyber security becomes ever more important considering the ubiquity of the Internet in everyday life. However, people usually lack the necessary understanding of this topic or the motivation to pay attention to the problem and its possible consequences. In this paper, we present the novel idea of combining visualization of one’s own personal data related to cyber-security literacy with a casual gaming approach. We therefore introduce our prototype, NetVisGame, in which we have implemented the idea for personal network traffic data based on a preliminary user study. The evaluation results of the first iteration of the user-centered design process supports the assumption that this approach is feasible to raise interest for and foster understanding of network traffic data and therefore could be a promising approach for data and technologies related to cyber-security literacy.

Neural Networks for Indoor Localization based on Electric Field Sensing

Topics: Recurrent Neural Network (RNN); Convolutional Neural Networks (CNN); Clustering, Classification and Regression

Authors: Florian Kirchbuchner 1 , Moritz Andres 2 , Julian von Wilmsdorff 1 and Arjan Kuijper 1

Affiliations: 1 Fraunhofer IGD Darmstadt, Fraunhoferstrasse 5, Darmstadt, Germany , 2 Technische Universität Darmstadt, Germany

Digital Object Identifier: 10.5220/0011266300003277

Proceedings of DeLTA 2022, ISBN: 978-989-758-584-5

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In this paper, we will demonstrate a novel approach using artificial neural networks to enhance signal processing for indoor localization based on electric field measurement systems Up to this point, there exist a variety of approaches to localize persons by using wearables, optical sensors, acoustic methods and by using Smart Floors. All capacitive approaches use, to the best of our knowledge, analytic signal processing techniques to calculate the position of a user. While analytic methods can be more transparent in their functionality, they often come with a variety of drawbacks such as delay times, the inability to compensate defect sensor inputs or missing accuracy. We will demonstrate machine learning approaches especially made for capacitive systems resolving these challenges. To train these models, we propose a data labeling system for person localization and the resulting dataset for the supervised machine learning approaches. Our findings show that the novel approach based on artificial neural networks with a time convolutional neural network (TCNN) architecture reduces the Euclidean error by 40% (34.8cm Euclidean error) in respect to the presented analytical approach (57.3cm Euclidean error). This means a more precise determination of the user position of 22.5cm centimeter on average.

Linoc: A Prototyping Platform for Capacitive and Passive Electrical Field Sensing

Topics: Internet of Things; Human-Computer Interfaces; Embedded Systems; Smart Objects

Authors: Julian Von Wilmsdorff 1 , Malte Lenhart 2 , Florian Kirchbuchner 1 and Arjan Kuijper 1

Affiliations: 1 Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany , 2 Technische Universität Darmstadt, Darmstadt, Germany

Digital Object Identifier: 10.5220/0010336400490058

Proceedings of SENSORNETS 2021, ISBN: 978-989-758-489-3

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In this paper the Linoc prototyping toolkit is presented. It is a sensor toolkit that focuses on fast prototyping of sensor systems, especially on capacitive ones. The toolkit is built around two capacitive and two Electric Potential Sensing (EPS) groups providing unobtrusive proximity detection in the field of Human Computer Interface (HCI). The toolkits focus lies on its usability and connectivity in order to be adapted in future research and novel use cases. A common obstacle in the beginning of a project is the time required to familiarize with present tools and systems, before the actual project can be attended to. Another obstacle while tackling new tasks is the actual physical connection of sensors to the processing unit. This situation can be even worse due to dependencies on previous work, most of the times not fully documented and missing knowledge even if the the original designer is involved. Good toolkits can help to overcome this problem by providing a layer of abstraction and allowing to work on a higher level. If the toolkit however requires too much time to familiarize or behaves too restrictive, its goal has been missed and no benefits are generated. To assess the quality of the Linoc prototyping toolkit, it was evaluated in terms of three different aspects: demonstration, usage and technical performance. The usage study found good reception, a fast learning curve and an interest to use the toolkit in the future. Technical benchmarks for the capacitive sensors show a detectable range equal to its predecessors and several operational prototypes prove that the toolkit can actually be used in projects.

Visual Interactive Creation of Geo-located Networks

Topics: Graph Visualization; Uncertainty Visualization

Authors: Felix Brodkorb 1 , Manuel Kopp 1 , Arjan Kuijper 2 and Tatiana von Landesberger 1

Affiliations: 1 TU Darmstadt, Germany , 2 Fraunhofer IGD, Germany

Digital Object Identifier: 10.5220/0006176302830293

Proceedings of IVAPP 2017/VISIGRAPP, ISBN: 978-989-758-228-8

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Nodes in real world networks often have a geographic position. In many cases such as for simulation or optimization, there is a need for non-trivial synthetic geo-located networks. As synthetic datasets are required to have specific properties such as connectivity and geographic distribution, often networks need to be generated. However, their creation is cumbersome if done purely by hand, and inflexible if done fully automated. Here, we present a framework for creating artificial geographic located networks in a visually interactive way. We designed our framework with the what-you-see-is-what-you-get principle in mind, i.e. showing the (intermediate) results of the interactive creation process at any time and allowing the user to adjust the network iteratively. This design allows our system to be also used as a simple viewer for networks that have incomplete location information. Our approach consists of two steps: (1) Creating the network topology and (2) assigning locations to its nodes. Our half automatic system enables the user to easily set the location of the nodes to predefined areas like countries, states, and urban regions, while still being able to flexibly and interactively control the creation process. We show the utility of our system by creating a real-world-like geo-located network.
Collapse Button Related Ontology Subjects/Areas/Topics (5)
Abstract Data Visualization

Graph Visualization

Uncertainty Visualization
Computer Vision, Visualization and Computer Graphics

Spatial Data Visualization

Efficient Image Distribution on the Web - Instant Texturing for Collaborative Visualization of Virtual Environments

Topics: Real-Time Rendering; Augmented, Mixed and Virtual Environments; Augmented, Mixed and Virtual Environments; Collaborative Augmented, Mixed and Virtual Environments; Distributed Augmented, Mixed and Virtual Reality; Real-Time Graphics; Interactive 3D Graphics for Mobile Devices Like Smartphones, PDAs and UMPCs; Integration and Interoperation Between 3D Documents and Web/Multimedia Technologies, Including the Semantic Web

Authors: Michael Englert , Yvonne Jung , Jonas Etzold , Marcel Klomann and Paul Grimm

Affiliation: Fulda University of Applied Sciences, Germany

Digital Object Identifier: 10.5220/0005345704780485

Proceedings of GRAPP 2015/VISIGRAPP, ISBN: 978-989-758-087-1

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In this paper, we present a browser-based Web 3D application that allows an instant distribution of image data even over mobile networks as well as textured rendering of large image collections on mobile devices with restricted processing power. Applications utilizing a lot of image data require an adaptive technology to build responsive user interfaces. This applies especially for the use in mobile networks. Furthermore the up- and download of the massive amount of image data should be transmitted in a progressive manner to get an instant feedback. While people are used to instant reaction of web applications and do not care about the amount of data that has to be transferred, the instantaneous display of imperfect content that gets continuously refined is state of the art for many application areas on the web. However, standard 2D image transmission technologies are usually inappropriate within a 3D context. In 2D, image size as well as resolution are often set during the authoring phase, whereas in 3D applications size and displayed resolution of textured 3D objects depend on the virtual camera. Our GPUII approach (GPU-based Image Interlacing) follows a client-server architecture, which allows an instant distribution of new data while also reducing the CPU load and network traffic.
Collapse Button Related Ontology Subjects/Areas/Topics (9)
Augmented, Mixed and Virtual Environments

Integration and Interoperation Between 3D Documents and Web/Multimedia Technologies, Including the Semantic Web

Real-Time Graphics

Computer Vision, Visualization and Computer Graphics

Interactive 3D Graphics for Mobile Devices Like Smartphones, PDAs and UMPCs

Real-Time Rendering

Distributed Augmented, Mixed and Virtual Reality

Interactive Environments

Rendering

Optimization-based Automatic Segmentation of Organic Objects of Similar Types

Topic: Segmentation and Grouping

Authors: Enrico Gutzeit , Martin Radolko , Arjan Kuijper and Uwe von Lukas

Affiliation: Fraunhofer Institute for Computer Research IGD, Germany

Digital Object Identifier: 10.5220/0005314905910598

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-089-5

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For the segmentation of multiple objects on unknown background in images, some approaches for specific objects exist. However, no approach is general enough to segment an arbitrary group of organic objects of similar type, like wood logs, apples, or tomatoes. Each approach contains restrictions in the object shape, texture, color or in the image background. Many methods are based on probabilistic inference on Markov Random Fields – summarized in this work as optimization based segmentation. In this paper, we address the automatic segmentation of organic objects of similar types by using optimization based methods. Based on the result of object detection, a fore- and background model is created enabling an automatic segmentation of images. Our novel and more general approach for organic objects is a first and important step in a measuring or inspection system. We evaluate and compare our approaches on images with different organic objects on very different backgrounds, which vary in color and texture. We show that the results are very accurate.
Collapse Button Related Ontology Subjects/Areas/Topics (3)
Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Segmentation and Grouping

Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images

Topics: Color and Texture Analyses; Shape Representation and Matching; Categorization and Scene Understanding; Document Imaging in Business

Authors: Matthias Vahl , Uwe von Lukas , Bodo Urban and Arjan Kuijper

Affiliation: Fraunhofer IGD, Germany

Digital Object Identifier: 10.5220/0005301501000107

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-091-8

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Geo-referenced aerial images are available in very high resolution. The automated production and updating of electronic nautical charts (ENC), as well as other products (e.g. thematic maps), from aerial images is a current challenge for hydrographic organizations. Often standard vision algorithms are not reliable enough for robust object detection in natural images. We thus propose a procedure that combines processing steps on three levels, from pixel (low-level) via segments (mid-level) to semantic information (high level). We combine simple linear iterative clustering (SLIC) as an efficient low-level algorithm with a classification based on texture features by supported vector machine (SVM) and a generalized Hough transformation (GHT) for detecting shapes on mid-level. Finally, we show how semantic information can be used to improve results from the earlier processing steps in the high-level step. As standard vision methods are typically much too slow for such huge-sized images and additionally geographical references must be maintained over the complete procedure, we present a solution to overcome these problems.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Applications and Services

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Color and Texture Analyses

Document Imaging in Business

Shape Representation and Matching

Converting Underwater Imaging into Imaging in Air

Topics: Stereo Vision and Structure from Motion; Image Formation, Acquisition Devices and Sensors; Device Calibration, Characterization and Modeling

Authors: Tim Dolereit 1 and Arjan Kuijper 2

Affiliations: 1 Fraunhofer Institute for Computer Graphics Research IGD and University of Rostock, Germany , 2 Fraunhofer Institute for Computer Graphics Research IGD, Germany

Digital Object Identifier: 10.5220/0004685600960103

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-003-1

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The application of imaging devices in underwater environments has become a common practice. Protecting the camera’s constituent electric parts against water leads to refractive effects emanating from the water-glass-air transition of light rays. These non-linear distortions can not be modeled by the pinhole camera model. For our new approach we focus on flat interface systems. By handling refractive effects properly, we are able to convert the problem to imaging conditions in air. We show that based on the location of virtual object points in water, virtual parameters of a camera following the pinhole camera model can be computed per image ray. This enables us to image the same object as if it was situated in air. Our novel approach works for an arbitrary camera orientation to the refractive interface. We show experimentally that our adopted physical methods can be used for the computation of 3D object points by a stereo camera system with much higher precision than with a naive in-situ calibration.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Computer Vision, Visualization and Computer Graphics

Image Formation and Preprocessing

Motion, Tracking and Stereo Vision

Device Calibration, Characterization and Modeling

Image Formation, Acquisition Devices and Sensors

Stereo Vision and Structure from Motion

Classification of Text and Image Areas in Digitized Documents for Mobile Devices

Topics: Features Extraction; Object Detection and Localization; Document Imaging in Business; Pervasive Smart Cameras; Image Generation Pipeline: Algorithms and Techniques; Mobile Imaging

Authors: Anne-Sophie Ettl 1 , Ralf Köster 2 , Axel Zeilner 2 and Arjan Kuijper 3

Affiliations: 1 Technische Universität Darmstadt and FINARX GmbH, Germany , 2 FINARX GmbH, Germany , 3 Technische Universität Darmstadt and Fraunhofer IGD, Germany

Digital Object Identifier: 10.5220/0004273600880091

Proceedings of VISAPP 2013/VISIGRAPP, ISBN: 978-989-8565-48-8

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Post processing and automatic interpretation of images plays an increasingly important role in the mobile area. Both for the efficient compression and for the automatic evaluation of text, it is useful to store text content as textual information rather than as graphics information. For this purpose pictures from magazines are recorded with the camera of a smartphone and classified according to text and image areas. In this work established desktop procedures are presented and analyzed in terms of their applications on mobile devices. Based on these methods, an approach for image segmentation and classification on mobile devices is developed, taking into account the limited resources of these mobile devices.
Collapse Button Related Ontology Subjects/Areas/Topics (9)
Applications and Services

Features Extraction

Image Generation Pipeline: Algorithms and Techniques

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Mobile Imaging

Document Imaging in Business

Image Formation and Preprocessing

Pervasive Smart Cameras

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