In this paper, we propose to combine the shape context (SC) descriptor with quantum genetic algorithms (QGA) to define a new shape matching and retrieval method. The SC matching method is based on finding the best correspondence between two point sets. The proposed method uses the QGA to find the best configuration of sample points in order to achieve the best possible matching between the two shapes. This combination of SC and QGA leads to a better retrieval results based on our tests. The SC is a very powerful discriminative descriptor which is translation and scale invariant, but weak against rotation and flipping. In our proposed quantum shape context algorithm (QSC), we use the QGA to estimate the best orientation of the target shape to ensure the best matching for rotated and flipped shapes. The experimental results showed that our proposed QSC matching method is much powerful than the classic SC method for the retrieval of shapes with orientation changes.

Hand recognition is one of an important biometric recognition technique. In this paper, we have proposed a simple and efficient technique for hand recognition that combines Hu invariant moments and Legendre moments.

We propose some aspects of range image processing (filtering, segmentation) based on multiresolution analysis, where we use discrete coiflet transforms. The interesting property of this kind of wavelets is the interpolating characteristic of their associated scaling functions. This characteristic is due to the fact that both coiflets and their scaling functions moments vanish. We give some definitions of range images, how they are acquired and treated. Then we present a new filtering method for such images, where we outline the importance of the interpolation property of the coiflet scaling function. We also describe an accelerating method for the range image segmentation process which uses region-growing techniques based on functions approximation and the local neighborhood property. Experimental results are given as examples.