Enhancing curvature scale space features for robust shape classification

The curvature scale space (CSS) technique, which is also part of the MPEG-7 standard, is a robust method to describe complex shapes. The central idea is to analyze the curvature of a shape and derive features from inflection points. A major drawback of the CSS method is its poor representation of convex segments: Convex objects cannot be represented at all due to missing inflection points. We have extended the CSS approach to generate feature points for concave and convex segments of a shape. This generic approach is applicable to arbitrary objects. In the experimental results, we evaluate as a comprehensive example the automatic recognition of characters in images and videos

A Collaborative Multi-Touch UML Design Tool

The design and development of software projects is usually done in teams today. Collaborative systems based on multi-touch walls or large table-top screens could support these highly interactive tasks. We present a novel collaborative design tool which allows several developers to jointly create complex UML (Unified Modeling Language) diagrams. We have developed new algorithms to recognize the gestures drawn by the users, to create the respective elements of the diagram, to adjust the edges between classes, and to improve the layout of the classes automatically. Auxiliary lines provide the user with means to align classes precisely so a more consistent layout is achieved. Export functionality for XML and Java code skeletons completes the application; the UML diagram can thus be used in further steps of the software design process. User evaluations confirm considerable benefits of our proposed system

Classification of iconic images

Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach with enhanced feature descriptors. Our novel color pyramids feature incorporates color information into the classification scheme. It improves the average F1 measure of the classification by 0:117. The performance of our system is further evaluated under a variety of parameters

Analysis of Disparity Maps for Detecting Saliency in Stereoscopic Video

We present a system for automatically detecting salient image regions in stereoscopic videos. This report extends our previous system and provides additional details about its implementation. Our proposed algorithm considers information based on three dimensions: salient colors in individual frames, salient information derived from camera and object motion, and depth saliency. These three components are dynamically combined into one final saliency map based on the reliability of the individual saliency detectors. Such a combination allows using more efficient algorithms even if the quality of one detector degrades. For example, we use a computationally efficient stereo correspondence algorithm that might cause noisy disparity maps for certain scenarios. In this case, however, a more reliable saliency detection algorithm such as the image saliency is preferred. To evaluate the quality of the saliency detection, we created modified versions of stereoscopic videos with the non-salient regions blurred. Having users rate the quality of these videos, the results show that most users do not detect the blurred regions and that the automatic saliency detection is very reliable

Analyse von Bildmerkmalen zur Identifikation wichtiger Bildregionen

Eine zuverlässige Erkennung wichtiger Bildregionen ist die Grundlage für viele Verfahren im Bereich der Bildverarbeitung wie beispielsweise bei der Bildkompression, bei Verfahren zur Anpassung der Bildauflösung oder beim Einfügen digitaler Wasserzeichen in Bilder. Es wurde ein System entwickelt, das Merkmalspunkte in Bildern identifiziert und diese nutzt, um wichtige Bildbereiche zu identifizieren. Zur Berechnung der Merkmalspunkte wird das SURF-Verfahren (Speeded Up Robust Features) verwendet. Die gefundenen Merkmale werden in einem zweiten Schritt einzelnen Bildregionen zugeordnet. Die Qualität der ermittelten Regionen sowie das Laufzeitverhalten der verschiedenen Verfahren werden anhand einer umfangreichen Bilddatenbank analysiert

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Automatic Lecture Recording

Lecture recording has become a very common tool to provide students with additional media for their examination preparations. While its effort has to stay reasonable, only a very basic way of recording is done in many cases. Therefore, watching the resulting videos can get very boring completely independent of how interesting the original topic or session was. This thesis proposes a new approach to lecture recordings by letting distributed computers emulate the work of a human camera team, which is the natural way of creating attractive recordings. This thesis is structured in six chapters, starting with the examination of the current situation, and taking its constraints into account. The first chapter concludes with a reflection on related work. Chapter two is about the design of our prototype system. It is deduced from a human camera team in the real world which gets transferred into the virtual world. Finally, a detailed overview about all parts necessary for our prototype and their planned functionality is given. In chapter three, the implementation of all parts and tasks and the incidents occurring during implementation are described in detail. Chapter four describes the technical experiences made with the different parts during development, testing and evaluation with a view to functionality, performance, and an proposal towards future work. The evaluation of the whole system with students is presented and discussed in the fifth chapter. Chapter six concludes this thesis by summing up the facts and gives an outlook on future work

Shape-based Posture and Gesture Recognition in Videos

The recognition of human postures and gestures is considered to be highly relevant semantic information in videos and surveillance systems. We present a new three-step approach to classifying the posture or gesture of a person based on segmentation, classification, and aggregation. A background image is constructed from succeeding frames using motion compensation and shapes of people are segmented by comparing the background image with each frame. We use a modified curvature scale space (CSS) approach to classify a shape. But a major drawback to this approach is its poor representation of convex segments in shapes: Convex objects cannot be represented at all since there are no inflection points. We have extended the CSS approach to generate feature points for both the concave and convex segments of a shape. The key idea is to reflect each contour pixel and map the original shape to a second one whose curvature is the reverse: Strong convex segments in the original shape are mapped to concave segments in the second one and vice versa. For each shape a CSS image is generated whose feature points characterize the shape of a person very well. The last step aggregates the matching results. A transition matrix is defined that classifies possible transitions be-tween adjacent frames, e.g. a person who is sitting on a chair in one frame cannot be walking in the next. A valid transition requires at least several frames where the posture is classified as standing-up. We present promising results and compare the classification rates of postures and gestures for the standard CSS and our new approach

Shape-based Posture and Gesture Recognition in Videos

The recognition of human postures and gestures is considered to be highly relevant semantic information in videos and surveillance systems. We present a new three-step approach to classifying the posture or gesture of a person based on segmentation, classification, and aggregation. A background image is constructed from succeeding frames using motion compensation and shapes of people are segmented by comparing the background image with each frame. We use a modified curvature scale space (CSS) approach to classify a shape. But a major drawback to this approach is its poor representation of convex segments in shapes: Convex objects cannot be represented at all since there are no inflection points. We have extended the CSS approach to generate feature points for both the concave and convex segments of a shape. The key idea is to reflect each contour pixel and map the original shape to a second one whose curvature is the reverse: Strong convex segments in the original shape are mapped to concave segments in the second one and vice versa. For each shape a CSS image is generated whose feature points characterize the shape of a person very well. The last step aggregates the matching results. A transition matrix is defined that classifies possible transitions between adjacent frames, e.g. a person who is sitting on a chair in one frame cannot be walking in the next. A valid transition requires at least several frames where the posture is classified as standing-up. We present promising results and compare the classification rates of postures and gestures for the standard CSS and our new approach

Forschungsbericht Universität Mannheim, 2004 / 2005

Die Universität Mannheim gibt in dem vorliegenden Forschungsbericht 2004/2005 Rechenschaft über ihre Leistungen auf dem Gebiet der Forschung. Erstmals folgt diese Dokumentation einer neuen Gliederung, die auf einen Beschluss des Forschungsrates der Universität Mannheim zurückgeht. Wie gewohnt erhalten Sie einen Überblick über die Publikationen und Forschungsprojekte der Lehrstühle, Professuren und zentralen Forschungseinrichtungen. Diese werden ergänzt um Angaben zur Organisation von Forschungsveranstaltungen, der Mitwirkung in Forschungsausschüssen, einer Übersicht zu den für Forschungszwecke eingeworbenen Drittmitteln, zu den Promotionen und Habilitationen, zu Preisen und Ehrungen und zu Förderern der Universität Mannheim. Abgerundet werden diese Daten durch zusammenfassende Darstellungen der Forschungsschwerpunkte und des Forschungsprofils der Fakultäten