, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre

The problem of detecting and labeling a moving human body viewed monocularly in a cluttered scene is considered. The task is to decide whether or not one or more people are in the scene (detection), to count them, and to label their visible body parts (labeling). It is assumed that a motion-tracking front end is supplied: a number of moving features, some belonging to the body and some to the background are tracked for two frames and their position and velocity is supplied. It is not guaranteed that all the body parts are visible, nor that the only motion present is the one of the body. Our algorithm is based on Song et al.[9]; we learn a probabilistic model of the position and motion of body features, and we calculate maximum-likelihood labels efficiently using dynamic programming on a triangulated approximation of the probabilistic model. We extend their results by allowing an arbitrary number of body parts to be undetected and by allowing an arbitrary number of noise features to b...

extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM classifiers which capture the articulations of the human bodies in dynamically changing appearance and background. With the part-based model, our approach is able to handle partial occlusions in both

. With an appropriately chosen weighting function, we are able to identify the trajectories of moving objects with high accuracy. In addition, our scheme is able to handle partial object occlusion.

This paper addresses the segmentation problem that frequently occurs in topographic information. This is where one topographic object (such as a road or rail bridge) obscures a segment of an underlying object (such as a river). A serious consequence of occlusions is that the obscured object

Efficient moving object tracking in cases of partial occlusion is a challenging task for the researchers in the fields of computer vision and video processing. In modern coding standards, like MPEG-4 and MPEG-7, the term of video objects is used to define moving objects in a video sequence

When two slanted surfaces are placed in proximity, the perceived slant difference between them is exaggerated. This effect has been called slant contrast. When a partial occluder is presented in front of the gap between them, the perceived slant difference between the surfaces is reduced. We refer

. In the presented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only few of the potential applications: exploitation of background

In this paper, a new approach for matching and recognizing affine-distorted planar shapes is proposed, which allows for partial occlusions. In our approach, each shape is divided into a sequence of ordered affine-invariant segments based on the properties of curvature scale space (CSS) shape

proposed so far, but little tackle the issue of partial occlusion: Blurry objects near the camera are semi-transparent and result in partially visible background objects. This effect is strongly apparent in miniature and macro photography. In this work a DoF method is presented which simulates partial