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In this paper, we present a new motion estimation algorithm which determines an initial motion vector and defines a search window adaptively using the motion information of spatio-temporal neighborhood blocks. First, an initial motion vector based on spatio-temporal correlation of motion is predicted. Next, we define a search window adaptively using the Gibbs/Markov random field model. The proposed algorithm particularly deals with the fast moving objects with only a small amount of computation in motion estimation, and is applied to constant and adaptive search windows respectively. The experimental results show the effectiveness of the proposed method.

We propose the new notion of self-certified identity information that applies the notion of self-certified public key to the case in which the public key is just the identity information. Using the notion of self-certified identity information, we propose a truly "paradoxical" identity-based identification scheme, the corresponding signature scheme which any user can choose his(her) own secret key though it is an identity-based method (that is, it is not a certification-based method). Also as an application of self-certified self-certified information, we propose a practical group signature which was proposed by Chaum (1991). The security of our schemes is based on the difficulty of the /spl gamma//sup th/-residuosity problem and discrete logarithm problem simultaneously. Also our schemes are in the level 2 of trust or in the level 3 of trust. In particular, our schemes are almost as efficient as the Schnorr's (1989) scheme.

In this paper, the morphological shape decompositions using shape directivity and shape function are proposed for the purpose of pattern recognition and image noise compression. To consider the horizontal and vertical directivity of shape, we use the ellipsoid and rectangle as primitive elements in the method using shape function, a structuring element with geometrical characteristics more similar to the shape function is preselected. The shape is decomposed into the primitive elements corresponding to the structuring elements. To prove the efficiency of the proposed algorithms, we compared them with conventional algorithms with respect to description error and coding efficiency.

Variable step size LMS(VS-LMS) algorithms improve the performance of the LMS algorithm by means of varying the step size. This paper presents a new VS-LMS algorithm using normalized absolute estimation error. Normalizing the estimation error to the expected value of the desired signal, we determined the step size using the relative size of estimation error. Because the parameter and computational load is less, our algorithm is easy to implement in hardware. The performance of the proposed algorithm is analyzed theoretically and estimated through simulations. Based on the theoretical analysis and computer simulations, the proposed algorithm is shown to be effective compared to conventional VS-LMS algorithms.