81 research outputs found
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
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
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
Semi-automatic video object segmentation for multimedia applications
A semi-automatic video object segmentation tool is presented for segmenting both still pictures and image sequences. The approach comprises both automatic segmentation algorithms and manual user interaction. The still image segmentation component is comprised of a conventional spatial segmentation algorithm (Recursive Shortest Spanning Tree (RSST)), a hierarchical segmentation representation method (Binary Partition Tree (BPT)), and user interaction. An initial segmentation partition of homogeneous regions is created using RSST. The BPT technique is then used to merge these regions and hierarchically represent the segmentation in a binary tree. The semantic objects are then manually built by selectively clicking on image regions. A video object-tracking component enables image sequence segmentation, and this subsystem is based on motion estimation, spatial segmentation, object projection, region classification, and user interaction. The motion between the previous frame and the current frame is estimated, and the previous object is then projected onto the current partition. A region classification technique is used to determine which regions in the current partition belong to the projected object. User interaction is allowed for object re-initialisation when the segmentation results become inaccurate. The combination of all these components enables offline video sequence segmentation. The results presented on standard test sequences illustrate the potential use of this system for object-based coding and representation of multimedia
Video object segmentation and tracking.
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2005One of the more complex video processing problems currently vexing researchers is that of
object segmentation. This involves identifying semantically meaningful objects in a scene and
separating them from the background. While the human visual system is capable of performing
this task with minimal effort, development and research in machine vision is yet to yield
techniques that perform the task as effectively and efficiently. The problem is not only difficult
due to the complexity of the mechanisms involved but also because it is an ill-posed problem.
No unique segmentation of a scene exists as what is of interest as a segmented object depends
very much on the application and the scene content. In most situations a priori knowledge of the
nature of the problem is required, often depending on the specific application in which the
segmentation tool is to be used.
This research presents an automatic method of segmenting objects from a video sequence. The
intent is to extract and maintain both the shape and contour information as the object changes
dynamically over time in the sequence. A priori information is incorporated by requesting the
user to tune a set of input parameters prior to execution of the algorithm.
Motion is used as a semantic for video object extraction subject to the assumption that there is
only one moving object in the scene and the only motion in the video sequence is that of the
object of interest. It is further assumed that there is constant illumination and no occlusion of the
object.
A change detection mask is used to detect the moving object followed by morphological
operators to refine the result. The change detection mask yields a model of the moving
components; this is then compared to a contour map of the frame to extract a more accurate
contour of the moving object and this is then used to extract the object of interest itself. Since
the video object is moving as the sequence progresses, it is necessary to update the object over
time. To accomplish this, an object tracker has been implemented based on the Hausdorff objectmatching
algorithm.
The dissertation begins with an overview of segmentation techniques and a discussion of the
approach used in this research. This is followed by a detailed description of the algorithm
covering initial segmentation, object tracking across frames and video object extraction. Finally,
the semantic object extraction results for a variety of video sequences are presented and
evaluated
Reconstruction of 3D rigid body motion in a virtual environment from a 2D image sequence
This research presents a procedure for interactive segmentation and automatic tracking of moving objects in a video sequence. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI. The refined mask is used to model a spline template of the object to be tracked. The tracking algorithm then employs a motion model to track the template through a sequence of frames and gathers the 3D affine motion parameters of the object from each frame. The extracted template is compared with a previously stored library of 3D shapes to determine the closest 3D object. If the extracted template is completely new, it is used to model a new 3D object which is added to the library. To recreate the motion, the motion parameters are applied to the 3D object in a virtual environment. The procedure described here can be applied to industrial problems such as traffic management and material flow congestion analysis
Video object segmentation.
Wei Wei.Thesis submitted in: December 2005.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 112-122).Abstracts in English and Chinese.Abstract --- p.IIList of Abbreviations --- p.IVChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of Content-based Video Standard --- p.1Chapter 1.2 --- Video Object Segmentation --- p.4Chapter 1.2.1 --- Video Object Plane (VOP) --- p.4Chapter 1.2.2 --- Object Segmentation --- p.5Chapter 1.3 --- Problems of Video Object Segmentation --- p.6Chapter 1.4 --- Objective of the research work --- p.7Chapter 1.5 --- Organization of This Thesis --- p.8Chapter 1.6 --- Notes on Publication --- p.8Chapter Chapter 2 --- Literature Review --- p.10Chapter 2.1 --- What is segmentation? --- p.10Chapter 2.1.1 --- Manual Segmentation --- p.10Chapter 2.1.2 --- Automatic Segmentation --- p.11Chapter 2.1.3 --- Semi-automatic segmentation --- p.12Chapter 2.2 --- Segmentation Strategy --- p.14Chapter 2.3 --- Segmentation of Moving Objects --- p.17Chapter 2.3.1 --- Motion --- p.18Chapter 2.3.2 --- Motion Field Representation --- p.19Chapter 2.3.3 --- Video Object Segmentation --- p.25Chapter 2.4 --- Summary --- p.35Chapter Chapter 3 --- Automatic Video Object Segmentation Algorithm --- p.37Chapter 3.1 --- Spatial Segmentation --- p.38Chapter 3.1.1 --- k:-Medians Clustering Algorithm --- p.39Chapter 3.1.2 --- Cluster Number Estimation --- p.41Chapter 3.1.2 --- Region Merging --- p.46Chapter 3.2 --- Foreground Detection --- p.48Chapter 3.2.1 --- Global Motion Estimation --- p.49Chapter 3.2.2 --- Detection of Moving Objects --- p.50Chapter 3.3 --- Object Tracking and Extracting --- p.50Chapter 3.3.1 --- Binary Model Tracking --- p.51Chapter 3.3.1.2 --- Initial Model Extraction --- p.53Chapter 3.3.2 --- Region Descriptor Tracking --- p.59Chapter 3.4 --- Results and Discussions --- p.65Chapter 3.4.1 --- Objective Evaluation --- p.65Chapter 3.4.2 --- Subjective Evaluation --- p.66Chapter 3.5 --- Conclusion --- p.74Chapter Chapter 4 --- Disparity Estimation and its Application in Video Object Segmentation --- p.76Chapter 4.1 --- Disparity Estimation --- p.79Chapter 4.1.1. --- Seed Selection --- p.80Chapter 4.1.2. --- Edge-based Matching by Propagation --- p.82Chapter 4.2 --- Remedy Matching Sparseness by Interpolation --- p.84Chapter 4.2 --- Disparity Applications in Video Conference Segmentation --- p.92Chapter 4.3 --- Conclusion --- p.106Chapter Chapter 5 --- Conclusion and Future Work --- p.108Chapter 5.1 --- Conclusion and Contribution --- p.108Chapter 5.2 --- Future work --- p.109Reference --- p.11
Video object tracking by label propagation and backward projection
This paper presents an approach dedicated to the tracking of one or several semantic objects in a video shot.
A state of the art on spatio-temporal segmentation techniques allows us to introduce our own approach. It combines
three different steps: label prediction based on partition projection, local segmentation associated with a label
propagation, and classification by backward projection.
Experimental results highlight the visual quality obtained with this method. Different kinds of objects can be accurately
tracked in different kinds of video sequences.Cet article présente nos travaux sur le suivi d'objets dans un plan séquence.
Un état de l'art sur les techniques de segmentation spatio-temporelle nous permet d'introduire notre propre
méthode de suivi temporel d'objets. Elle est constituée de trois phases distinctes : une prédiction d'étiquettes
par projection de partition, une segmentation locale associée à une propagation d'étiquettes, et une
classification par rétro-projection. L'association de ces trois étapes cumule les avantages de chaque approche
pour un suivi rigoureux d'objets et réduit le temps de traitement de chaque image.
La qualité visuelle des résultats obtenus par cette méthode est illustrée en fin d'article. Pour cela nous avons
considéré le suivi d'objets ayant des caractéristiques différentes au niveau de leur composition et de leur
déplacement
Very High Resolution (VHR) Satellite Imagery: Processing and Applications
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing
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