Showing papers on "Range segmentation published in 1987"

An optimal region growing algorithm for image segmentation

Abstract: We present a new approach to the segmentation problem by optimizing a criterion which estimates the quality of a segmentation. We use a graph-based description of a partition of an image and a merg...

Image processing apparatus capable of random mosaic and/or oil-painting-like processing

Abstract: An image processing apparatus in which a pixel of first image data is designated, a predetermined pattern is generated on first image data in accordance with the position of the designated pixel, the coordinates of each pixel in the predetermined pattern are transformed to this coordinate values of the predetermined pixel, and image information for the pixels of the first image data is, e.g., given the value of the designated pixel.

Range image segmentation and surface parameter extraction for 3-D object recognition of industrial parts

Abstract: This paper addresses the problem of extracting certain types of surfaces for 3-dimensional object recognition in the presence of partial occlusion and noise using range information. We restrict consideration to man made industrial parts. In this case, we need only work with a small set of surface shapes such as planes, cylinders, and spheres since such shapes are common in industrial parts and it is only necessary to recognize and locate a sufficient set of surface regions to uniquely distinguish the part being observed from all others that might be present. We describe a way of extracting the parameters of these types of surfaces using normal analysis. The use of surface parameters then allows efficient and robust use of a matching process for object recognition and pose determination even though there are many undefined or occluded surface regions.

Segmentation adaptative pour le codage d'images

Abstract: This Ph.D. dissertation analyses the problem of segmenting an image into a set of regions corresponding as much as possible to the real objects of a scene. The goal of this segmentation is to go from a numerical representation of an image to a symbolic one, i.e. the regions and their characteristic features. Starting from the set of picture elements, one reaches a more compact model where region frontiers define the contours of the objects and the signal within each region their texture. Such a representation can find useful applications for scene understanding and image coding. Recent works have shown the potential of a contour-texture model for image coding due to the properties of our human visual system. We have studied here the way of coding a segmented representation of an image when using high order polynomials for approximating the different regions. The proposed segmentation algorithm is adaptive. Given a certain approximation to model the texture within each region, one tries to modify adaptively the region shape and the approximation parameters. To do so, the image is first split into a set of squares of different sires in order to obtain an optimal correspondence between the original signal and its approximation within each square. Then starting from this initial partition, adjacent regions are iteratively merged till one reaches a segmentation with a certain number of regions of any shape. At each step of the merging process, the two most similar regions are associated on the basis of the adequacy of the approximation on the new region. Polynomials of degree 0 to 3 have been used in the approximation process. Once the final segmentation is obtained, frontier information and texture information are coded separately. Performances for redundancy reduction are impressive: acceptable quality images can be obtained with compression ratios of the order of 30 to 1. It is shown how most of the semantics can be preserved with coded pictures at compression ratios ranging from 60 to 1 to 130 to 1. The algorithm has been applied to three different 256x256 natural images quantized with eight bit dynamics.

Improved Edge-Based Image Segmentation

Abstract: An improved segmenter has been developed which partitions a monochrome image into homogeneous regions using local neighborhood operations. Perkin's well-known edge-based segmenting algorithm [1] is used to partition those portions of an image with little detail (low edge density) into regions of uniform intensity. A technique is introduced which segments the remainder of the image to reveal details that were previously lost. Region merging is then performed by removing selected boundary pixels that separate sufficiently similar (e.g., in average intensity) regions subject to the constraint that the boundary pixel quality (e.g. edge strength) is below a selected threshold. Region merging is repeated using less and less restrictive merging criteria until the desired degree of segmentation (e.g. number of regions) is obtained.

Image coding based on segmentation using region growing

Abstract: This paper describes a composite source model for images. An image is segmented into uniform and homogeneous regions using centroid linkage region growing algorithms. The region homogenity is determined by the Student T-statistics. Excessive regions resulting from region growing are merged according to region merging rules. The initially segmented image is then clustered into classes with the help of the K-means algorithm. The image classes are shown in this paper as visual aids in judging the classification procedure. The class numbers and the corresponding pixel counts are also included. Finally, as an application of composite source models, an image is encoded using matrix quantizers with separate codebook for each class of the image.

Polygonal approximation for image segmentation

Abstract: A new learning model for real-time, grey-level image segmentation is presented The model gives excellent results for images with different shapes