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Abstract: Point pattern matching is an important topic in computer vision and pattern recognition, and finds many applications such as image registration, motion detection, object tracking and pose estimation. In this paper, we propose an efficient algorithm for determining correspondence between two planar point sets under transform of translation, rotation and scale. This algorithm randomly selects some points of a set and extracts their neighbor points. It views the selected points and their neighbor points as local point patterns, and finds the local matched patterns in the other set. Point pattern matching is finally achieved by counting the unique point number of those local matched point patterns with the same transform parameters. Many experiments are conducted to validate efficiency of the proposed algorithm. Running time comparisons with a well-known point pattern matching algorithm are also done and the results show that the proposed algorithm is faster than the compared algorithm. [Copyright &y& Elsevier]

Data hiding in encrypted image is a hot topic of data security in recent years. In this paper, we propose a reversible data hiding algorithm with high capacity in encrypted domain by exploiting alpha channel of portable network graphics (PNG) image. Specifically, our algorithm divides secret data into some segments. For each segment, one bit is embedded into the LSB of encrypted pixel and other bits are hidden in the corresponding element of the alpha channel, which is finally encrypted by two chaotic maps. With the use of alpha channel, our algorithm can perfectly recover secret data and reach high embedding capacity and good visual quality. Many experiments with standard benchmark images are carried out to validate efficiency of our algorithm. Comparison shows that our algorithm outperforms Zhang’s algorithm (IEEE SPL 18(2011) 255-258). [ABSTRACT FROM AUTHOR]

Salt and pepper noise removal is an important task in image processing. In this paper, we propose a simple and efficient restoration algorithm with the theory of image inpainting. Our algorithm takes noisy pixels as missing data for inpainting, adaptively selects convolution mask in terms of details of local regions, and achieves restoration by iterative convolutions. Many experiments are conducted to validate efficiency of our algorithm. The results show that our algorithm can efficiently remove noise while preserving image details even at a high noise density, and outperforms some well-known algorithms in terms of peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]