Correlation based methods are a common tool for the correspondence problem. They are able to perform a dense point-to-point matching between two images, but post-processing is often necessary to improve the results. In this paper we propose a new correlation measure using the gradient vector fields of the images. We compare our method to classical correlation measures based on the grey levels. The new method gives better results than others when it is applied on random dot stereograms and on real images.

This paper shows how to estimate the 3D planar motion of point coordinates from uncalibrated sequence images, irrespective of the camera motion. Assuming that four stationary reference points with known scene coordinates are seen, the reconstruction problem is solved through recursive coordinates estimations in the 3D reference plane while tracking is done in a space of invariant projective parameters instead of affine or Euclidean parameters. The invariant perspective coordinates plane-axis correspond to cross-ratios defining the relative position of a point with respect to four reference points-supplies a matching space invariant for any homography or camera motion. According to the extended Kalman filter formalism, we discuss the discriminatory power of the invariant perspective coordinates and describe geometrical constraints in order to reduce false matches. Experiments are done with a reference rectangle of known Euclidean coordinates. Results show the interest of this method yielding to a high reconstruction accuracy.

The authors present a 3-D measurement method using a projected grid that permits an optimal geometrical exploitation of the calculated 3-D data. They propose a reliable method for solving the correspondence that establishes the link between the imaged grid and the original grid curve per curve without any ambiguity. The imaged grid is extracted by an efficient method that describes the distorted grid as a network of curves rather than a graph, and hence provides regular and precise 2-D information. The 3-D projected grid is calculated per curve by using the triangulation principle. Thus, two independent families of 3-D curves that parameterize the object surfaces can be used for a surface geometrical study.>

The authors deduce the first and second directional derivative operators for 2-D and 3-D images for a symmetric exponential filter and realize them by a first-order recursive algorithm. The edges are detected according to the gradient maxima or the zero crossing of the second derivative along the gradient direction. Experimental results show that the new methods are less sensitive to noise and have a much better precision of edge localization. Theoretical and experimental studies comparing the performances of the filters were performed, showing that the exponential filter is superior to other current filters. >

The magnitude-distance and maximum-value-distance filters are analyzed, and fast realizations are proposed to implement a DRF cascade for edge detection. It is shown that the maximum-value-distance filter is well adapted to parallel processing and works two times as fast as the magnitude-distance filter. It is shown also that the adaptive gradient is an optimal estimate for edge-pixel verification after zero-crossing detection. The efficiency of the proposed algorithms is confirmed by experimental results.>

In stereo vision, it is important to find the correspondence between the stereopair images. In the present paper, we propose a method based on the geometric feature analysis to find the correspondence between the straight lines and the planes in the stereopair images. We suppose that the stereopair images have been processed separately and some polygons are thus obtained as composed of their edges. We propose the necessary and sufficient conditions which the edge parameters of two corresponding polygons respectively in the two images should satisfy, and note that these conditions are independent of the camera parameters and of the distance between the cameras.

In a previous paper, we proposed a photometric method for the first order specular surfaces. In the present paper, we propose a photometric method for n-order specular surfaces (where n describes the sharpness of the specular property of the surface), when the incident light intensity and direction are unknown. The image is considered as a perspective projection and the Image-Surface Orientation Equation (ISEO) is proposed. One can calculate the surface normal from the ISEO. The constraint is that the surface is rather planar in a small region. Our algorithm is implemented and tested for artificial data created by computer and the results, which are given in the paper, are encouraging.

In structural pattern recognition, the errors in primitive recognition have an important effect on the recognition rate of object. This paper proposes a method using models with fuzzy primitives. It provides a simple and effective approach which can, by utilizing the knowledge about the universe and the context of the primitives, correct possible errors in primitive recognition and therefore improve the ability of the recognition system. In order to apply this method to the on line recognition of handwritten chinese ideographs, a method for feature extraction of graphisms, i.e. MLG method, is developed. Our system is implemented on a micro-computer Apple-II, the experiments for 200 chinese ideographs give satisfactory results.