journal = {Signal Processing}, publisher = {Elsevier}, volume = {31}, number = {2}, year = {1993}, pages = {165--180}} This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by the authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. document created on: December 20, 2006 created from file: sp93cdcoverpage.tex cover page automatically created with CoverPage.sty (available at your favourite CTAN mirror) L

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

We present a statistical method to detect regions whose apparent motion in the image is not conforming to the dominant motion of the background resulting from the camera movement. Alternatively, the same scheme can be used to track a particular region of interest of the scene. The apparent motion induced by the camera motion is represented by a 2D parametric motion model, and compensated for using the values of the motion model parameters estimated by a multiresolution robust statistical technique. Then, regions whose motion cannot be described by this global model estimated over the entire image, are extracted. The detection of these non conforming regions is achieved through a statistical regularization approach based on multiscale Markov random #eld #MRF# models. We have paid a particular attention to the de#nition of the energy function involved and to the observations taken into account. To gain robustness, information is integrated over time. This method has been validated by experiments carried out on many real image sequences.

In this paper we address two important problems in motion analysis: the detection of moving objects and their localization. A statistical approach is adopted in order to formulate these problems. For the first, the inter-frame difference is modelized by a mixture of two zero-mean generalized Gaussian distributions, and a Gibbs random field is used for describing the label set. A new method to determine the regularization parameter is proposed, based on a voting technique. This method is also modelized using a statistical framework. The solution of the second problem is based on the observation of only two successive frames. Using the results of change detection an adaptive statistical model for the couple of image intensities is identified. For each problem two different multiscale algorithms are evaluated, and the labeling problem is solved using either ICM (Iterated Conditional Modes) or HCF (Highest Confidence First) algorithms. For illustrating the efficiency of the proposed approach, experimental results are provided using synthetic and real video sequences.

Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In the second generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized. Keywords: Image/video segmentation, optical flow, motion estimation, multimedia 1.

inter-camera separation.

This paper describes a motion-analysis system, applied to the problem of vehicle tracking in real-world highway scenes. The system is structured in two stages. In the first one, a motion-detection algorithm performs a figure/ground segmentation, providing binary masks of the moving objects. In the second stage, vehicles are tracked for the rest of the sequence, by using Kalman filters on two state vectors, which represent each target's position and velocity. A vehicle's motion is represented by an affine model, taking into account translations and scale changes. Three types of features have been used for the vehicle's description state vectors. Two of them are contour-based: the bounding box and the centroid of the convex polygon approximating the vehicles contour. The third one is region-based and consists of the 2-D pattern of the vehicle in the image. For each of these features, the performance of the tracking algorithm has been tested, in terms of the position error, stability of t...

Multiple views of a scene, obtained from cameras positioned at distinct viewpoints, can provide a viewer with the benefits of added realism, selective viewing, and improved scene understanding. The importance of these signals is evidenced by the recently proposed Multi-View Profile (MVP) extension to the MPEG-2 video compression standard, and their explicit incorporation into the future MPEG-4 standard. However, multi-view compression implementations typically rely on single-view image sequence model assumptions. We hypothesize (and demonstrate) that impressive system bandwidth reduction can be achieved by utilizing displacement vector field and image intensity models tuned to the special characteristics of multi-view video signals. This thesis focuses on the predictive coding of non-periodic, i.e., arbitrary, multi-view video signals for the applications of simulated motion parallax and viewer-specified degree of stereoscopy. To facilitate their practical use, we desire algorithms tha...

Compression and interpolation each require, given part of an image, or part of a collection or stream of images, being able to predict other parts. Compression is achieved by transmitting part of the imagery along with instructions for predicting the rest of it; of course, the instructions are usually much shorter than the unsent data. Interpolation is just a matter of predicting part of the way between two extreme images; however, whereas in compression the original image is known at the encoder, and thus the residual can be calculated, compressed, and transmitted, in interpolation the actual intermediate image is not known, so it is not possible to improve the final image quality by adding back the residual image. Practical 3D-video compression methods typically use a system with four modules: (1) coding one of the streams (the main stream) using a conventional method (e.g., MPEG), (2) calculating the disparity map(s) between corresponding points in the main stream and the auxiliary ...

A software package realizes real-time video processing on a commercially available programmable device 1. The software implements a motion estimator and a picture rate convertor to provide judder-free display of movie material broadcast in 2–3 pull-down mode. A new object-based true-motion estimation algorithm efficiently uses the VLIW core of the processor. It permits quasi-simultaneous motion estimation/segmentation for a fixed maximum number of objects. 1