Efficient Video Transport over Lossy Networks

Nowadays, packet video is an important application of the Internet. Unfortunately the capacity of the Internet is still very heterogeneous because it connects high bandwidth ATM networks as well as low bandwidth ISDN dial in lines. The MPEG-2 and MPEG-4 video compression standards provide efficient video encoding for high and low bandwidth media streams. In particular they include two paradigms which make those standards suitable for the transmission of video via heterogeneous networks. Both support layered video streams and MPEG-4 additionally allows the independent coding of video objects. In this paper we discuss those two paradigms, give an overview of the MPEG video compression standards and describe transport protocols for Real Time Media transport over lossy networks. Furthermore, we propose a real-time segmentation approach for extracting video objects in teleteaching scenarios

Motion-based Segmentation and Classification of Video Objects

In this thesis novel algorithms for the segmentation and classification of video objects are developed. The segmentation procedure is based on motion and is able to extract moving objects acquired by either a static or a moving camera. The classification of those objects is performed by matching their outlines gathered from a number of consecutive frames of the video with preprocessed views of prototypical objects stored in a database. This thesis contributes to four areas of image processing and computer vision: motion analysis, implicit active contour models, motion-based segmentation, and object classification. In detail, in the field of motion analysis, the tensor-based motion estimation approach is extended by a non-maximum suppression scheme, which improves the identification of relevant image structures significantly. In order to analyze videos that contain large image displacements, a feature-based motion estimation method is developed. In addition, to include camera operations into the segmentation process, a robust camera motion estimator based on least trimmed squares regression is presented. In the area of implicit active contour models, a model that unifies geometric and geodesic active contours is developed. For this model an efficient numerical implementation based on a new narrow-band method and a semi-implicit discretization is provided. Compared to standard algorithms these optimizations reduce the computational complexity significantly. Integrating the results of the motion analysis into the fast active contour implementation, novel algorithms for motion-based segmentation are developed. In the field of object classification, a shape-based classification approach is extended and adapted to image sequence processing. Finally, a system for video object classification is derived by combining the proposed motion-based segmentation algorithms with the shape-based classification approach

Umweltzone Leipzig: Messtechnische Begleitung der Einführung der Umweltzone Leipzig: Teil 6 / Abschlussbericht: Immissionssituation von 2010 bis 2016 und Wirkung der Umweltzone auf die straßennahe Luftqualität

Mit Einführung der Umweltzone 2011 erfolgte eine beschleunigte Modernisierung der Fahrzeugflotte in Leipzig gegenüber anderen Regionen in Sachsen. Die Belastung durch Verbrennungspartikel aus der modernisierten Fahrzeugflotte reduzierte sich sehr deutlich. Die Reduzierung war im ersten Jahr der Umweltzone am stärksten. Nach sechs Jahren wurde eine Minderung für die Anzahl der Partikel von 30 bis 200 nm um 74 % und für Ruß-Partikel BC um 59 % nachgewiesen. Dies dokumentiert den Erfolg der Partikelfilter in modernen Dieselfahrzeugen beim realen Fahren in der Stadt. Der hochtoxische Feinstaubanteil in der Außenluft und damit das Gesundheitsrisiko der Bevölkerung wurden sehr deutlich gesenkt. Die Umweltzone war damit eine sinnvolle und wirkungsvolle Maßnahme der Stadtverwaltung. Gleichzeitig trat keine Verbesserung für die Stickstoffoxide durch die modernste Fahrzeugflotte Sachsens ein. Die Dieselfahrzeuge gelten als die Hauptverursacher. Der Misserfolg in der Minderung der Stickoxide moderner Diesel-PKW beim realen Fahren in der Stadt trotz verschärfter EURO-Abgasnormen wurde dokumentiert. Über die tatsächlichen Emissionen moderner Diesel-PKW wurden Bürger und Stadtverwaltung von Autoherstellern getäuscht

Fast Methods for Implicit Active Contour Models

Implicit active contour models belong to the most popular level set methods in computer vision. Typical implementations, however, suffer from poor efficiency. In this chapter we survey an efficient algorithm that is based on an additive operator splitting (AOS). It is suitable for geometric and geodesic active contour models as well as for mean curvature motion. It uses harmonic averaging and does not require to compute the distance function in each iteration step. We prove that the scheme satisfies a discrete maximum-minimum principle which implies unconditional stability if no balloon forces are present. Moreover, it possesses all typical advantages of AOS schemes: simple implementation, equal treatment of all axes, suitability for parallel computing, and straightforward generalization to higher dimensions. Experiments show that one can gain a speed up by one order of magnitude compared to the widely used explicit time discretization

Fast methods for implicit active contour models

Implicit active contour models belong to the most popular level set methods in computer vision. Typical implementations, however, suffer from poor efficiency. In this paper we survey an efficient algorithm that is based on an additive operator splitting (AOS). It is suitable for geometric and geodesic active contour models as well as for mean curvature motion. It uses harmonic averaging and does not require to compute the distance function in each iteration step. We prove that the scheme satisfies a discrete maximum-minimum principle which implies unconditional stability if no balloon forces are present. Moreover, it possesses all typical advantages of AOS schemes: simple implementation, equal treatment of all axes, suitability for parallel computing, and straightforward generalization to higher dimensions. Experiments show that one can gain a speed up by one order of magnitude compared to the widely used explicit time discretization

Abstract Interactive Segmentation and Visualization of Volume Data Sets

A two-stage methodology for interactive segmentation of volume data sets is presented in this paper. In the first stage (presegmentation) a 3D watershed transformation is used for segmenting the data in different small regions. According to the neighboring relationships between this regions a region adjacency graph (RAG) is constructed. During the second stage (interactive information extraction), a 3D region growing algorithm driven by user controlled parameters is applied to the RAG until a satisfactory segmentation result is achieved. The presegmentation step greatly reduces the complexity of the segmentation problem. On account of that, a fast information extraction is possible in the second stage. In combination with our realtime volume rendering hardware VIRIM, the response time of the information extraction stage performed on a medical data set with 256 ¡ 256 ¡ 128 voxels is less than a second

Motion-based Segmentation and Contour-based Classification of Video Objects

The segmentation of objects in video sequences constitutes a prerequisite for numerous applications ranging from computer vision tasks to second-generation video coding. We propose an approach for segmenting video objects based on motion cues. To estimate motion we employ the 3D structure tensor, an operator that provides reliable results by integrating information from a number of consecutive video frames. We present a new hierarchical algorithm, embedding the structure tensor into a multiresolution framework to allow the estimation of large velocities. The motion estimates are included as an external force into a geodesic active contour model, thus stopping the evolving curve at the moving object's boundary. A level set-based implementation allows the simultaneous segmentation of several objects. As an application based on our object segmentation approach we provide a video object classification system. Curvature features of the object contour are matched by means of a curvature scale space technique to a database containing preprocessed views of prototypical objects. We provide encouraging experimental results calculated on synthetic and real-world video sequences to demonstrate the performance of our algorithms

�ÓÒØÓÙÖ ��×� � �Ð�××� ¬ �Ø�ÓÒ Ó � Î���Ó Ç�� � Ø×

The recognition of objects that appear in a video sequence is an essential aspect of any video content analysis system. We present an approach which classifies a segmented video object based on its appearance (object views) in successive video frames. The classification is performed by matching curvature features of the contours of these object views to a database containing preprocessed views of prototypical objects using a modified curvature scale space technique. By integrating the results of a number of successive frames and by using the modified curvature scale space technique as an efficient representation of object contours, our approach enables the robust, tolerant and rapid object classification of video objects

A tensor-driven active contour model for moving object segmentation

In this paper we propose an approach to the segmentation of video objects based on motion cues. Motion analysis is performed by estimating local orientations in the spatio-temporal domain using the three-dimensional structure tensor. These estimates are integrated as an external force into an active contour model, thus stopping the evolving curve when it reaches the moving object's boundary. To enable simultaneous detection of several objects, we reformulate the tensor-based active contour model using the level-set technique. In addition, a contour refinement technique has been developed to better approximate the real boundary of the moving object. We provide promising experimental results calculated on real-world video sequences widely used within the computer vision community