Antti Ylä-Jääski

Grouping Symmetrical Structures for Object Segmentation and Description

A method is presented for segmenting gray-value images into objects (or their parts) and for recognizing the detected objects. Starting from edge maps, the method extracts axial descriptions of symmetrical shapes. Initially, a piecewise linear approximation of the binary edge map is obtained. From any two of the resulting linear segments, a Linear Segment Pair (LSP) is formed and several of its attributes are computed. These attributes allow the method to reject or select the LSPs through symbolic rules and coarse numeric thresholds. Grouping the LSPs into couples is governed by additional attributes and rules, with the final representation consisting of ordered sets of LSPs. The application to shape description, object recognition, and stereo correspondence is presented. This segmentation method is useful for a broad range of images; it has been used in a robot vision system which is capable of manipulating three-dimensional, overlapping, real-world objects in close to real time.

Segmentation and Symbolic Description of Range Images

This paper describes a method to segment a range image of a scene containing simple objects and to generate a first symbolic description thereof. The most important underlying assumption is that of surface coherence, i.e. that the visible surfaces which make up the objects are piecewise smooth. Each such piece can thus be approximated to any desired degree by analytic functions, e.g. polynomials. The first step consists of clustering of surface normals through the iterative detection of peaks in histograms of surface normal components. The amount of tolerated deviation of normal directions in a cluster is made dependent on the noise level in the range image. The result is a set of patches which correspond to true planar surfaces or to small pieces of curved surfaces. A first region growing recovers points which were discarded because of large normal deviation but which are near to the fitting plane. Now a second order polynomial fit is computed for all patches. Curvature values for the mass centers of the patches are obtained from the polynomial fit surface and are used to recognize the surface type. Further region growing is now performed. Region merging based on surface type and numeric values of attributes is then done. Each region is described and relations between adjacent regions are assembled into an attributed graph. This graph will be used in object recognition.