|Ph.D Student||Roman Goldenberg|
|Subject||Motion Based Recognition|
|Department||Department of Computer Science||Supervisors||Full Professor Rivlin Ehud|
|Full Professor Kimmel Ron|
In this work we explore how the motion based visual information can be used to solve a number of well known computer vision problems such as segmentation, tracking, object recognition and classification and event detection. We consider three special cases for which the methods used are quite different: the rigid, non-rigid and articulated objects. For rigid objects we address the problem taken from the traffic domain and show how the relative velocity of nearby vehicles can be estimated from a video sequence taken by a camera installed on a moving car. For non-rigid objects we present a novel geometric variational framework for image segmentation using the active contour approach. The method is successfully used for moving non-rigid targets segmentation and tracking in color movies, as well as for a number of other applications such as cortical layer segmentation in 3-D MRI brain images, segmentation of defects in VLSI circuits on electronic microscope images, analysis of bullet traces in metals, and others. Relying on the high accuracy results of segmentation and tracking obtained by the fast geodesic contour approach, we present a framework for moving object classification based on the eigen-decomposition of the normalized binary silhouette sequence. We demonstrate the ability of the system to distinguish between various object classes by their static appearance and dynamic behavior. Finally we show how the observed articulated object motion can be used as a cue for the segmentation and detection of independently moving parts. The method is based on the analysis of normal flow vectors computed in color space and also relies on a number of geometric heuristics for edge segments grouping.