In this paper, we propose a camera calibration method that estimates both intrinsic parameters (perspective and distortion) and extrinsic parameters (rotational and translational). All camera parameters can be determined from one or more images of planar pattern consists of parallelogramatic grid points. As far as the pattern can be visible, the relative relations between camera and patterns are arbitrary. So, we have only to prepare a pattern, and take one or more images changing the relative relation between camera and the pattern, arbitrarily; neither solid object of ground truth nor precise z-stage are required. Moreover, constraint conditions that are imposed on rotational parameters are explicitly satisfied; no intermediate parameter that connected several actual camera parameters are used. Taking account of the conflicting fact that the amount of distortion is small in the neighborhood of the image center, and that small image has poor clues of 3-D information, we adopt iterative procedure. The best parameters are searched changing the size and number of parallelograms selected from grid points. The procedure of the iteration is as follows: The perspective parameters are estimated from the shape of parallelogram by nonlinear optimizations. The rotational parameters are calculated from the shape of parallelogram. The translational parameters are estimated from the size of parallelogram by least squares method. Then, the distortion parameters are estimated using all grid points by least squares method. The computer simulation demonstrates the efficiency of the proposed method. And the results of the implementation using real images are also shown.

We introduce an image contour clustering method based on a multiscale image representation and its application to image compression. Multiscale gradient planes are obtained from the mean squared sum of 2D wavelet transform of an image. The decay on the multiscale gradient planes across scales depends on the Lipshitz exponent. Since the Lipshitz exponent indicates the spatial differentiability of an image, the multiscale gradient planes represent smoothness or sharpness around edges on image contours. We apply vector quatization to the multiscale gradient planes at contours, and cluster the contours in terms of represntative vectors in VQ. Since the multiscale gradient planes indicate the Lipshitz exponents, the image contours are clustered according to its gradients and Lipshitz exponents. Moreover, we present an image recovery algorithm to the multiscale gradient planes, and we achieve the skech-based image compression by the vector quantization on the multiscale gradient planes.

In this coding scheme for delta modulation of color still pictures, either of two reference regions is selected on a pel by pel. In comparison with fixed region scheme, average color differences can be decreased by approximately 30% in exchange for about 0.5 bit/pel overhead caused by selecting codes.

We propose a shape resolution control method applying a tolerance caused by movement to object's shape and texture in order to represent efficiently a textured object that has a detailed structure. It is generally difficult to perceive the error of shape or texture of the object that is moving. Our method applies this error as a tolerance. The efficient object's representation is realized by the shape resolution control that tolerates errors of contour shape and textured surface by the tolerance caused by movement and reduces object's data. It was shown better experimental results of processing time and of the quality of images in comparison with other methods. Thus, it was proved that the method applying the tolerance caused by movement to the object's shape and texture is effective in the representation of textured object that has a detailed structure.