Showing papers on "Segmentation-based object categorization published in 1983"

Pyramid linking is a special case of ISODATA

Abstract: It is shown that the `pyramid linking' method of image segmentation can be regarded as a special case of the ISODATA clustering algorithm and hence is guaranteed to converge.

Segmentation of cell nuclei in tissue section analysis.

Abstract: Image segmentation is a critical step in digital picture analysis, especially for that of tissue sections. As the morphology of the cell nuclei provides important biological information, their segmentation is of particular interest. The known segmentation methods are not adequate for segmenting cell nuclei of tissue sections; the reason for this lies in the optical properties of their images. We have developed new gradient methods of segmentation of previously presegmented images by taking these properties into account and by using the approximately circular shape of the cell nuclei as a priori information. In our first technique, the segment method, the images of the nuclei are divided into eight segments, special gradient filters being defined for each segment. This has enabled us to improve the gradient image. After searching for local maxima, the contours of nuclei can be found. In the second method, the method of transformation into the polar coordinate system (PCS), the a priori information serves t...

Image Segmentation By Edge Tracing

Abstract: This paper describes an image segmentation technique that has detection and classification applications in autonomous image analysis systems and compares it with current edge and region-based segmentation techniques. The technique, referred to as directed edge tracing, uses both edge magnitude and direction information to reduce segmentation problems commonly associated with segmenters based on edge thresholding. The edge tracing is carried out by examining pixels in a neighborhood of high probability boundary pixels, making the method locally adaptive to contrast changes along a boundary. No a priori knowledge of the number of segments expected or of the level of contrast between segments is required.

Integrating non-semantic knowledge into image segmentation processes

Abstract: This dissertation develops several techniques for automatically segmenting images into regions. The basic approach involves the integration of different types of non-semantic knowledge into the segmentation process such that the knowledge can be used when and where it is useful. These processes are intended to produce initial segmentations of complex images which are faithful with respect to fine image detail, balanced by a computational need to limit the segmentations to a fairly small number of regions. Natural scenes often contain intensity gradients, shadows, highlights, texture, and small objects with fine geometric structure, all of which make the calculation and evaluation of reasonable segmentations for natural scenes extremely difficult. The approach taken by this dissertation is to integrate specialized knowledge into the segmentation process for each kind of image event that can be shown to adversely affect the performance of the process. At the center of our segmentation system is an algorithm which labels pixels in localized subimages with the feature histogram cluster to which they correspond, followed by a relaxation labeling process. However, this algorithm has a tendency to undersegment by failing to find clusters corresponding to small objects; it may also oversegment by splitting intensity gradients into multiple clusters, by finding clusters for "mixed pixel" regions, and by finding clusters corresponding to microtexture elements. In addition, the relaxation process often destroys fine structure in the image. Finally, the artificial subimage partitions introduce the problem of inconsistent cluster sets and the need to recombine the segmentations of the separate subsimages into a consistent whole. This dissertation addresses each of these problems by adding and deleting clusters based on image space information, by merging regions, and by defining different compatibility coefficients in the relaxation so as to preserve fine structures. The result is a segmentation algorithm which is more reliable over a broader range of images than the simple clustering algorithm. Solutions to the same segmentation problems were examined via the integration of different segmentation algorithms (including edge, region, and thresholding algorithms) to produce a consistent segmentation. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI

The Perceptual Organization of Visual Images: Segmentation as a Basis for Recognition,

Abstract: : Evidence is presented showing that bottom-up grouping of image features is usually prerequisite to the recognition and interpretation of images. The authors describe three functions of these groupings: segmentation, three-dimensional interpretation, and stable descriptions for accessing object models. Several unifying principles are hypothesized for determining which image relations should be formed: relations are significant to the extent that they are unlikely to have arisen by accident from the surrounding distribution of features, relations can only be formed where there are few alternatives within the same proximity, and relations must be based on properties which are invariant over a range of imaging conditions. Using these principles we develop an algorithm for curve segmentation which detects significant structure at multiple resolutions, including the linking of segments on the basis of curvilinearity. The algorithm is able to detect structures which no single-resolution algorithm could detect. Its performance is demonstrated on synthetic and natural image data. (Author)

Segmentation Of Images Using Minimum Spanning Trees

Abstract: A new image segmentation technique based on minimum spanning trees is proposed. The motivation for using minimum spanning trees is their apparent ability of Gestalt clustering, thus relating the segmentation algorithm to Gestalt principles of perceptual organization. Several examples of segmentation using the new algorithm demonstrate the closeness between the results and human perception. The new algorithm is extremely flexible in accomodating different objectives and criteria of segmentation.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Two-dimensional linear predictive analysis of arbitrarily-shaped regions

Abstract: This paper is concerned with the use of 2-D linear prediction for image segmentation. It begins with a brief summary of the mathematics involved in 2-D linear predictive analysis of arbitrarily-shaped regions. Then, it introduces a 2-D LPC distance measure based on the error residual of 2-D linear prediction. Finally, it describes how the above results can be applied to image segmentation using a simple cluster seeking algorithm. The results indicate that arbitrarily-shaped image regions can be well identified and clustered using as features their 2-D LPC parameters.

Classification and tracking using local optimization

Abstract: When a heuristic function is available to evaluate classification, a special search procedure is applied to find a classification optimizing this function. A specific application to image segmentation is presented, including several examples. The major difference between this approach and previous optimization attempts is the use of deterministic rather than probabilistic classifications. The approach is also applied to object tracking in image sequences.

Iterative Process For Colour Image Segmentation Using A Convexity Criterion

Abstract: In image processing, the segmentation is a very important step, mainly because of its influence on the feature evaluation. During the segmentation process, problems occur either due to possible ambiguities in the pixel labelling or due to frequent contacts between different components. This paper describes an iterative method, designed to segment convex poorly contrasted or touching objects which is based on the association of two notions, one related to the object colour and the other to its shape, in terms of convexity. Starting from an initial connected set of points, pixels are iteratively aggregated to that set using a colour distance. The processing stops when convexity of the iterated set is reached. Application to a particular class of convex objects, such as blood and bone marrow cells is presented.

Segmentation of Digital Images Using a Priori Information about the Expected Image Contents

Abstract: Various image segmentation algorithms are known from literature where images are segmented according to a general common property denoted “Uniformity predicate P” (Fu et al, 1981). In our concept we establish an additional dependency of the predicate P of the imaged objects which we expect and from which we assume to know some general properties, such as shape, surface proportions and so on.

The Histogram Optimization And Histogram Compression Segmentations

Abstract: Two methods of segmentation called the Histogram Optimization Segmentation and the Histogram Compression Segmentation are presented. These algorithms use local histograms in a recursive manner to arrive at a self-consistent segmentation. The first two sections explain the specific operation of the algorithms and the last section examines the validity of one of the segmentation criteria. Examples of the segmentations are also given.

Image Segmentation Considering Properties of the Human Visual System

Abstract: Basic algorithms for image segmentation like contour detection and texture discrimination have mostly been derived from optimizations of mathematically defined quality criteria. This paper describes new approaches to the development of algorithms for image segmentation based on properties of the human visual system.