This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.

a b s t r a c tThe fundamental matrix (FM) describes the geometric relations that exist between two images of the same scene. Different error criteria are used for estimating FMs from an input set of correspondences. In this paper, the accuracy and efficiency aspects of the different error criteria are studied. We mathematically and experimentally proved that the most popular error criterion, the symmetric epipolar distance, is biased. It was also shown that despite the similarity between the algebraic expressions of the symmetric epipolar distance and Sampson distance, they have different accuracy properties. In addition, a new error criterion, Kanatani distance, was proposed and proved to be the most effective for use during the outlier removal phase from accuracy and efficiency perspectives. To thoroughly test the accuracy of the different error criteria, we proposed a randomized algorithm for Reprojection Error-based Correspondence Generation (RE-CG). As input, RE-CG takes an FM and a desired reprojection error value d. As output, RE-CG generates a random correspondence having that error value. Mathematical analysis of this algorithm revealed that the success probability for any given trial is 1 À (2/3) 2 at best and is 1 À (6/7) 2 at worst while experiments demonstrated that the algorithm often succeeds after only one trial.

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Intelligent surveillance aims at conceiving reliable and efficient systems that are able to detect and track moving objects in complicated real world scenes. This paper proposes an innovative 3D stationary wavelet-based motion detection technique that fuses spatial and temporal analysis in a single 3D transform. This single transform is composed of applying a 2D transform in the spatial domain followed by 1D transform in the time domain. The results of the proposed technique are compared favorably with those of the recently used stationary wavelet-based technique. In addition of being accurate and has reasonable complexity of O(N2 log N), the proposed technique is robust to real world scene variations, including nonuniform and time-varying illumination.