A Lvq-Based Temporal Tracking for Semi-Automatic Video Object Segmentation

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmentation of the video object on the following frames thus performing temporal tracking. For LVQ training input, we sampling each pixel of a video frame as a 5-dimensional vector combining 2-dimensional pixel position (X,Y) and 3-dimensional HSV color space. This paper also demonstrates experiments using some MPEG-4 standard test video sequences to evaluate the accuracy of the proposed method

A video object generation tool allowing friendly user interaction

In this paper we describe an interactive video object segmentation tool developed in the framework of the ACTS-AC098 MOMUSYS project. The Video Object Generator with User Environment (VOGUE) combines three different sets of automatic and semi-automatic-tool (spatial segmentation, object tracking and temporal segmentation) with general purpose tools for user interaction. The result is an integrated environment allowing the user-assisted segmentation of any sort of video sequences in a friendly and efficient manner.Peer ReviewedPostprint (published version

Automatic detection of salient objects and spatial relations in videos for a video database system

Cataloged from PDF version of article.Multimedia databases have gained popularity due to rapidly growing quantities of multimedia data and the need to perform efficient indexing, retrieval and analysis of this data. One downside of multimedia databases is the necessity to process the data for feature extraction and labeling prior to storage and querying. Huge amount of data makes it impossible to complete this task manually. We propose a tool for the automatic detection and tracking of salient objects, and derivation of spatio-temporal relations between them in video. Our system aims to reduce the work for manual selection and labeling of objects significantly by detecting and tracking the salient objects, and hence, requiring to enter the label for each object only once within each shot instead of specifying the labels for each object in every frame they appear. This is also required as a first step in a fully-automatic video database management system in which the labeling should also be done automatically. The proposed framework covers a scalable architecture for video processing and stages of shot boundary detection, salient object detection and tracking, and knowledge-base construction for effective spatio-temporal object querying. (c) 2008 Elsevier B.V. All rights reserved

Image sequence analysis for emerging interactive multimedia services - The European COST 211 framework

Cataloged from PDF version of article.Flexibility and efficiency of coding, content extraction, and content-based search are key research topics in the field of interactive multimedia. Ongoing ISO MPEG-4 and MPEG-7 activities are targeting standardization to facilitate such services. European COST Telecommunications activities provide a framework for research collaboration. COST 211bis and COST 211ter activities have been instrumental in the definition and development of the ITU-T H.261 and H.263 standards for videoconferencing over ISDN and videophony over regular phone lines, respectively. The group has also contributed significantly to the ISO MPEG-4 activities. At present a significant effort of the COST 211ter group activities is dedicated toward image and video sequence analysis and segmentation—an important technological aspect for the success of emerging object-based MPEG-4 and MPEG-7 multimedia applications. The current work of COST 211 is centered around the test model, called the Analysis Model (AM). The essential feature of the AM is its ability to fuse information from different sources to achieve a high-quality object segmentation. The current information sources are the intermediate results from frame-based (still) color segmentation, motion vector based segmentation, and changedetection-based segmentation. Motion vectors, which form the basis for the motion vector based intermediate segmentation, are estimated from consecutive frames. A recursive shortest spanning tree (RSST) algorithm is used to obtain intermediate color and motion vector based segmentation results. A rule-based region processor fuses the intermediate results; a postprocessor further refines the final segmentation output. The results of the current AM are satisfactory; it is expected that there will be further improvements of the AM within the COST 211 project

Extracting semantic video objects

Dagan Feng2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Region-based representations of image and video: segmentation tools for multimedia services

This paper discusses region-based representations of image and video that are useful for multimedia services such as those supported by the MPEG-4 and MPEG-7 standards. Classical tools related to the generation of the region-based representations are discussed. After a description of the main processing steps and the corresponding choices in terms of feature spaces, decision spaces, and decision algorithms, the state of the art in segmentation is reviewed. Mainly tools useful in the context of the MPEG-4 and MPEG-7 standards are discussed. The review is structured around the strategies used by the algorithms (transition based or homogeneity based) and the decision spaces (spatial, spatio-temporal, and temporal). The second part of this paper proposes a partition tree representation of images and introduces a processing strategy that involves a similarity estimation step followed by a partition creation step. This strategy tries to find a compromise between what can be done in a systematic and universal way and what has to be application dependent. It is shown in particular how a single partition tree created with an extremely simple similarity feature can support a large number of segmentation applications: spatial segmentation, motion estimation, region-based coding, semantic object extraction, and region-based retrieval.Peer ReviewedPostprint (published version

Learning the dynamics and time-recursive boundary detection of deformable objects

We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object

Dynamic Object Tracking by Partial Shape Matching for Video Surveillance Applications

In this thesis, an algorithm for object tracking through frames of video using a fast partial shape matching technique is proposed. The tracking is divided into two modules: 1) moving object extraction followed by color/edge segmentation, and 2) tracking through frames using partial shape matching. The major challenges of object tracking, such as occlusions, splitting of one object and appearance and disappearance of objects, are effectively resolved. The proposed algorithm is tested on several synthetic and real life video sequences and is shown to be very effective in identifying and tracking moving objects independent of translations, rotations, scale variations and occlusions. The novelty of the proposed algorithm lies in its ability to independently track full and partial objects undergoing split, merge and occlusion scenarios independent of their location and scale in the scene. . The technique assumes that: 1) the video frames are captured at 30 frames per second in order for the object(s) motion (translation, rotation, isometric scale variations) to be well modeled by an affine transformation, 2) the object(s) being tracked are larger than a certain number of pixels to allow for comprehensive shape modeling, and 3) the video camera is kept stationary

Realtime object extraction and tracking with an active camera using image mosaics

[[abstract]]Moving object extraction plays a key role in applications such as object-based videoconference, surveillance, and so on. The dimculties of moving object segmentation lie in the fact that physical objects are normally not homogeneous with to low-level features and it's usually tough to segment them accnrately and efficiently. Object segmentation based on prestored background information has proved to be effective and efficient in several applications such as videophone, video conferencing, and surveillance, etc. The previous works, however, were mainly concentrated on object segmentation with a static camera and in a stationary background. In this paper, we propose a robust and fast segmentation algorithm and a reliable tracking strategy without knowing the shape of the object in advance. The proposed system can real-time extract the foreground from the background and track the moving object with an active (pan-tilt) camera such that the moving object always stays around the center of images.[[fileno]]2030144030033[[department]]電機工程學