range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. Compared with retrieval based on individual regions, the overall similarity approach 1) reduces the adverse effect

corresponding to objects, which are characterized by features reflecting color, texture, shape, and location properties. The IRM measure for evaluating overall similarity between images incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based

We describe an algorithm for detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far away from each other. This camera arrangement results in multiple wide-baseline camera systems. We segment each image, and then, for each pair we compare regions

Abstract — W hen transmitted over error-prone networks, compressed video sequences may be received with errors. In this paper we propose a priority-ranked region-matching algorithm to recover the “lost ” area of the decoded frames, in which both temporal and spatial correlations of the video

lead to a ”region-matching ” ME scheme [5]. Bayesian segmentation has been operated, with the same model, in the wavelet domain and has shown an interesting gain in segmentation speed [6]. In the present work we have synthesized both approaches to demonstrate a new scheme of region-matching ME which

scene change detection method consists of two steps: detection and tracking of semantic objects of interest specified by the user, and an unsupervised method for detection of cuts, and edit effects. Object detection and tracking is achieved using a region matching scheme, where the region of interest

Abstract. In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single

developed to evaluate overall similaritybetween images that incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, our overall similarity approach (a) reduces the inuence of inaccurate segmentation, (b) helps to clarify

in at least one of the views. In 82.4% of the cases a correct link could be established between the TP detections in both views. In future work, we will use the framework presented here to develop a context dependent region matching scheme, which takes the number and likelihood of possible alternatives

, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques