Parametric improvement of lateral interaction in accumulative computation in motion-based segmentation

Segmentation of moving objects is an essential component of any vision system. However, its accomplishment is hard due to some challenges such as the occlusion treatment or the detection of objects with deformable appearance. In this paper an artificial neuronal network approach for moving object segmentation, called lateral interaction in accumulative computation (LIAC), which uses accumulative computation and recurrent lateral interaction is revisited. Although the results reported for this approach so far may be considered relevant, the problems faced each time (environment, objects of interest, etc.) make that the system outcome varies. Hence, our aim is to improve segmentation provided by LIAC in a double sense: by removing the detected objects not matching some size or compactness constraints, and by learning suitable parameters that improve the segmentation behavior through a genetic algorithm

Parametric model-based motion segmentation using surface selection criterion

Copyright © 2006 Elsevier Inc. All rights reserved.This paper presents a new framework for the motion segmentation task, which includes an algorithm capable of addressing the important issue of the inter-relationships between data segmentation, model selection, and noise scale estimation. In this algorithm, we have incorporated our newly proposed model selection criterion named Surface Selection Criterion. The presented algorithm simultaneously selects the correct motion model, while finding the scale of the noise and performing the segmentation task. As a result, the estimated motion parameters and the final segmentation results are accurate. The algorithm is tested for motion segmentation of synthetic and real video data containing multiple objects undergoing different types of motion. Our results also show that the proposed algorithm is capable of detecting occlusion and degeneracy. © 2006 Elsevier Inc. All rights reserved.Niloofar Gheissari, Alireza Bab-Hadiashar and David Sute