In this paper, a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA, is introduced. This algorithm computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Two major techniques are used sequentially in a real-time fashion in order to obtain the most efficient and independent components that describe a whole set of human faces database. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and independent component analysis (ICA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to others.

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CAPTCHA is a security technique to allow a computer application to distinguish between computer and human access. Most of now a day CAPTCHA words have their characters connected together which makes them very difficult to be segmented and recognized. In this paper we present an efficient technique that first benefits from the intersections between characters in a word and then segment it based on the recognition of each segmented character. Edge corners (ECs) are used in both segmentation and recognition phases. A novel fuzzy logic‐based scheme is proposed to match characters using their ECs. Experimental results show the efficiency of the proposed scheme in terms of success rate using a large set of CAPTCHA's words. Copyright © 2015 John Wiley & Sons, Ltd.

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This paper presents a new algorithm for image co-registration using dominant corners located on the image's edges under the assumption that the deformation between the successive images is modeled by an affine transformation. This assumption is guaranteed when the time interval between acquired images is small like in a video sequence. Therefore, it is mainly dedicated for motion analysis. The method detects first straight edges of an object contour then classifies their intersection points as contour's corners characterized by their angles and adjacent segments lengths. The suppressor starts then to eliminate iteratively the weak corners until reaching a set of dominant ones called "Dominant Corners". These dominant corners are shown to be very repeatable under affinity transformation. A Primitive is constructed by four consecutive dominant corners located on the same contour. The invariant measure that characterizes each primitive is the ratio of areas of two triangles constructed by two triplets selected from these four corners and the corners directions difference. The primitives are formed in the two studied images from an image sequence. All primitives are used to vote for the best affine model that relates the two images. The method used in primitive construction will lead to an important enhancement over existing methods in the voting process in time and accuracy. This method is tested on real images and good results are reported.