Showing papers on "Range segmentation published in 1982"
01 Sep 1982
TL;DR: This paper investigates several variations on the basic linking process with regard to such factors as initialization, criteria for linking, and iteration scheme used and extends the approach to links based on more than one feature of a pixel, e.g., on color components or local property values.
Abstract: A recently developed method of image smoothing and segmentation makes use of a "pyramid" of images at successively lower resolutions. It establishes links between pixels at successive levels of the pyramid; the subtrees of the pyramid defined by these links yield a segmentation of the image into regions over which the smoothing takes place. This paper investigates several variations on the basic linking process with regard to such factors as initialization, criteria for linking, and iteration scheme used. It also studies generalizations in which the links are weighted rather than forced, and in which interactions among the pixels at a given level are also allowed. Finally, it extends the approach to links based on more than one feature of a pixel, e.g., on color components or local property values.
71 citations
TL;DR: This paper studies some of the problems that arise with linked-pyramid segmentation, and proposes a two-stage segmentation process that overcomes these problems.
67 citations
03 May 1982
TL;DR: Ohlander et al. as discussed by the authors discussed issues of recursive region segmentation in the context of PHOENIX, the newest version of region segmentations, running on a VAX 11/780 under UNIX.
Abstract: Recursive segmentation of an image into regions using histograms is one of the most widely used techniques for image segmentation. At CMU, several versions of a region segmentation program have been developed based on this technique (Ohlander, Price, Shafer and Kanade). Based on these experiences, this paper discusses issues of recursive region segmentation in the context of PHOENIX, the newest version of region segmentation program, running on a VAX 11/780 under UNIX. The issues discussed in this paper include: Image features to be used in histogramming; comparison of the algorithm with other techniques; important improvements made in PHOENIX over its predecessor (Ohlander and Price); and some inherent problems in histogram-based segmentation together with suggestions for minimizing them. PHOENIX is being incorporated into the ARPA Image Understanding Testbed, under construction at SRI International.
43 citations
01 Jan 1982
TL;DR: The methodologies and capabilities of image segmentation techniques are reviewed and single linkage schemes, centroid linkage scheme, histogram mode seeking, spatial clustering, and split and merge schemes are addressed.
Abstract: The methodologies and capabilities of image segmentation techniques are reviewed. Single linkage schemes, hybrid linkage schemes, centroid linkage schemes, histogram mode seeking, spatial clustering, and split and merge schemes are addressed.
42 citations
TL;DR: This ‘tile’-based approach organizes the computation of a segmentation in such a way that parallel processors can readily be applied to cut the time required, an important advantage over previous methods, however, is this method's space-efficiency.
34 citations
TL;DR: This paper addresses the problem of combining range and intensity data for scene analysis by using edge maps of the range image and of the intensity image to place both sources of information in the same form.
7 citations
Proceedings Article•
18 Aug 1982TL;DR: A method for segmenting aerial images using edge information to create regions of similar or smoothly varying intensity is discussed and the results obtained are compared with a traditional region-splitting method.
Abstract: A method for segmenting aerial images using edge information to create regions of similar or smoothly varying intensity is discussed. Region segmentation using edges directly as input cannot be successful because boundaries are seldom perfectly closed. In the present method, we preprocess the edge image to close gaps and create a binary image from which we extract the connected regions. We compare the results obtained with this method and a traditional region-splitting method for 2 different views of an aerial scene.
7 citations
28 Dec 1982
TL;DR: A new technique of image segmentation using pixel differences is discussed, which reduces the amount of clutter generated during segmentation while consistently generating objects of interest.
Abstract: A new technique of image segmentation using pixel differences is discussed. Current IR imaging seekers tend to be noisy and lead to noise-generated clutter. Due to its design, the magnitude contrast segmenter reduces the amount of clutter generated during segmentation while consistently generating objects of interest. The basic steps in the algorithm are magnitude difference, contrast evaluation and edge degapping. The edges generated form a closed boundary without using the iterative processing required by other segmenters. The algorithm also segments the high and low intensity areas of an object into one region and identifies the internal structure separating each. Intermediate results are presented in order to document each step in the algorithm. The final result is a clutter reduced, segmented image of well defined regions. A diverse set of images is presented to demonstrate the effectiveness of this algorithm in handling contrastingly different images.