Feature-based active contour model and occluding object detection

This paper presents a method for image segmentation and object detection. The proposed strategy consists of two major stages. The first one corresponds to image segmentation, which is based on the active contour model (ACM) algorithm, using an automatic selection of the best candidate features among gradient, polarity, and depth, coupled with a combination of them by the kernel support vector machine (KSVM). Although existing techniques, such as the ones based on ACM, perform well in the single-object case and non-noisy environments, these techniques fail when the scene consists of multiple occluding objects, with possibly similar colors. Thus, the second stage corresponds to the identification of salient and occluded objects based on the fuzzy C-mean algorithm (FCM). In this stage, the depth is included as another clue that allows us to estimate the cluster number and to make the clustering process more robust. In particular, complex occlusions can be handled this way, and the objects can be properly segmented and identified. Experimental results on real images and on several standard datasets have shown the success and effectiveness of the proposed method.