Automatic Moving Object Segmentation from Video Sequences Using Alternate Flashing System

Abstract

A novel algorithm to extract moving objects from video sequences is proposed in this paper. The proposed algorithm employs a flashing system to obtain an alternate series of lit and unlit frames from a single camera. For each unlit frame, the proposed algorithm synthesizes the corresponding lit frame using a motion-compensated interpolation scheme. Then, by comparing the unlit frame with the lit frame, we construct the sensitivity map, which provides depth cues. In addition to the sensitivity term, color, coherence, and smoothness terms are employed to define an energy function, which is minimized to yield segmentation results. Moreover, we develop a faster version of the proposed algorithm, which reduces the computational complexity significantly at the cost of slight performance degradation. Experiments on various test sequences show that the proposed algorithm provides high-quality segmentation results.

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

“…In this regard, segmentation is essentially the first step toward many image analysis and computer vision problems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. With the recent advances in several new multimedia applications, there is the need to develop segmentation algorithms running on efficient hardware platforms [16][17][18].…”