Showing papers on "Segmentation-based object categorization published in 1982"

Image Smoothing and Segmentation by Multiresolution Pixel Linking: Further Experiments and Extensions

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.

Segmentation of Images Having Unimodal Distributions

Abstract: A gradient relaxation method based on maximizing a criterion function is studied and compared to the nonlinear probabilistic relaxation method for the purpose of segmentation of images having unimodal distributions. Although both methods provide comparable segmentation results, the gradient method has the additional advantage of providing control over the relaxation process by choosing three parameters which can be tuned to obtain the desired segmentation results at a faster rate. Examples are given on two different types of scenes.

Recursive region segmentation by analysis of histograms

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.

A contour-texture approach to picture coding

Abstract: A new image coding technique, based on a simplified description of a region-composed image is presented. The regions are extracted from the original image by an adaptive psychovisually oriented segmentation process. The picture elements (pels) inside the regions represent the texture information of the image whereas the region boundaries points represent the contour information. The data-adaptive character of this method as well as the use of some psychovisual characteristics of the human visual system lead to promising results in data compression.

Image segmentation using simple Markov field models

Abstract: By modelling a picture as a two-state Markov field, MAP estimation techniques are used to develop suboptimal but computationally tractable binary segmentation algorithms.

Object Oriented Cell Image Segmentation

Abstract: A correct segmentation of cell images intonucleus, cytoplasm and background is a prerequi- site for a working automatic pre- screening devicefor cervical cytology. This paper presents an al- gorithm for determining the segmentation thres-holds. It is based on a- priori assumptions about a cell's shape and size and works on one object at a time, disregarding everything else in the image.The algorithm is capable of verifying that theisolated object really looks like a cell; an es-sential feature in an automatic system. The nu-cleus and cytoplasm thresholds are decided upon almost independently of each other. The algorithmworks by tracking iso- density contours around theobject to be isolated and its execution time is thus proportional to the length of the contourrather than the area of the image. Some prelimi-nary results are given and the possibility of ef-ficiently implementing the algorithm in hardwareis discussed.INTRODUCTION A lot of effort has been put into the re-search towards an automated cervical pre- screeningdevice based on image processing. One reason whythese attempts so far

Image understanding and graph matching

Abstract: We show that one of the key problems in Image Understanding is the matching of two symbolic structures, a model and the result of a segmentation. These symbolic structures are conveniently represented as labeled graphs and we present on two examples a formalism which can deal with the inexact or fuzzy matching of such structures in a highly parallel fashion.

A cluster analysis program for image segmentation

Abstract: : It is shown how the ordinal image segmentation problem fits into an earlier well-developed model for hierarchical clustering. Certain techniques suggested by this model are investigated and implemented on real data. The results are compared to those achieved by segmentation techniques that involve region mergers based on various notions of scatter. The techniques are examined from both an order theoretic and statistical viewpoint.

Image segmentation or image understanding

Abstract: In Image processing, it is often implicitely admitted that an image is known when it has been segmented in various textures, in accordance with the judgement of the human eye. Two counter examples are presented, in biology and in petrology, showing that image analysis may also consist in synthetizing informations disseminated over a large number of pictures, and in predicting the relationships between physical properties and textures. Then, a formal definition of image understanding is given, which yields a particular approach based on structuring elements, and on a few logical properties. An illustration of the approach is provided by the analysis of a boolean structure.

Differential Based Gradient Contour Segmentation Algorithm

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.