Abstract. Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today’s best-performing stereo algorithms.

In the last two decades, many papers have been published, proposing a variety of methods for multi- frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in images with sharp edges.

Abstract—Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences. Index Terms—Multiple-human segmentation, multiple-human tracking, visual surveillance, human shape model, human locomotion model. 1

this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from generic, robust, real-time motion estimation algorithms. The selection of the best motion estimator is still highly dependent on the application. Nevertheless, a broad variety of estimation models, criteria and optimization schemes can be treated in a unified framework presented here, thus allowing a direct comparison and leading to a deeper understanding of the properties of the resulting estimators.

This article provides an overview of current techniques for dense geometric correspondence estimation. We will first formally define geometric correspondence and investigate the different types of image pairs. Then we briefly look at the classic approaches to correspondence estimation, at their feasibility and flaws for simultaneous dense estimation. We will focus on the Bayesian approach, which is suited very well for this task and for which several promising algorithms have recently been developed. After having a look at the future of the Bayesian approaches, we conclude with a discussion.

Many amateur videographers, like home video enthusiasts, may capture videos that are not of a professional quality. Many minor but visually annoying distortions like lighting imbalance and shaking artifacts could be introduced by the unskilled operations of the video camcorder. Since home videos constitute footage of great sentimental value, such videos cannot be summarily discarded. Unlike movies and sitcoms, shot re-takes of important events, such as wedding ceremonies are just not possible. Therefore, such distortions need to be corrected. In this paper, we present a novel method to detect segments of videos that have lighting and shaking artifacts. These segments can then be subjected to a restoration process that can remove these artifacts. We present techniques to correct lighting artifacts by appropriately adjusting the luminance. In order to remove the shaking artifact, image mosaicing is first employed to build a mosaic frame for the segment with the aid of edge blending techniques. Subsequently a Bezier-curve based blending of motion trajectory is employed to perform motion-compensated filtering of the shaking artifact. The restored video is then created by appropriately cropping the mosaic frame based on the compensated motion trajectory. We have implemented the developed techniques and the experimental results on home videos demonstrate the effectiveness of our approach. Detection and removal of artifacts are significant in other videos as well as those obtained from autonomous vehicles, robots and remote sensing.

This article presents our research in the C-BIRD (content-based image retrieval in digital-libraries) project. In addition to the use of common features such as color, texture, shape, and their conjuncts, and the combined content-based and description-based techniques, it is shown that (a) color-channel-normalization enables search by illumination invariance, and (b) feature localization and a three-step matching algorithm (color hypothesis, texture support, shape verification) facilitate search by object model in image and video databases. C # 1999 Academic Press Key Words: color; content-based retrieval; digital library; feature localization; generalized hough transform; image and video databases; modeling and matching; segmentation; shape; texture

We view scenes in the real world by moving our eyes three to four times each second, and integrating information across subsequent fixations (foveation points). By taking advantage of this fact, in this paper we propose an original approach to partitioning of a video into shots based on a foveated representation of the video. More precisely, the shotchange detection method is related to the computation, at each time instant, of a consistency measure of the fixation sequences generated by an ideal observer looking at the video. The proposed scheme aims at detecting both abrupt and gradual transitions between shots using a single technique, rather than a set of dedicated methods. Results on videos of various content types are reported and validate the proposed approach Index Terms--- Attentive Vision, Video Segmentation, Shot Detection, Hard Cuts, Dissolves.

In this paper we present a parallel implementation of an MPEG encoder on the Intel Paragon supercomputer. In our approach, both spatial and temporal parallelism have been exploited. While the Paragon has the computation capacity to encode video sequences faster than real-time, we found that real-time performance cannot be achieved if the input/output techniques are not designed properly. We present several schemes for managing the I/O and show their effect on reducing I/O contention.

Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD read out noise) and system noise (e.g. motion estimation error). Noting that the motion estimation is often considered a bottleneck in terms of SR performance, we introduce the concept of “constrained motions” for enhancing the quality of super-resolved images. We show that using such constraints will enhance the quality of the motion estimation and therefore results in more accurate reconstruction of the HR images. We also justify some practical assumptions that greatly reduce the computational complexity and memory requirements of the proposed methods. We use efficient approximation of the Kalman Filter (KF) and adopt a dynamic point of view to the SR problem. Novel methods for addressing these issues are accompanied by experimental results on real data. 1.