Showing papers on "Image segmentation published in 1983"

Digital image smoothing and the sigma filter

Abstract: A conceptually simple but effective noise smoothing algorithm is described. This filter is motivated by the sigma probability of the Gaussian distribution, and it smooths the image noise by averaging only those neighborhood pixels which have the intensities within a fixed sigma range of the center pixel. Consequently, image edges are preserved, and subtle details and thin lines such as roads are retained. The characteristics of this smoothing algorithm are analyzed and compared with several other known filtering algorithms by their ability to retain subtle details, preserving edge shapes, sharpening ramp edges, etc. The comparison also indicates that the sigma filter is the most computationally efficient filter among those evaluated. The filter can be easily extended into several forms which can be used in contrast enhancement, image segmentation, and smoothing signal-dependent noisy images. Several test images 128 × 128 and 256 × 256 pixels in size are used to substantiate its characteristics. The algorithm can be easily extended to 3-D image smoothing.

Volumetric Descriptions of Objects from Multiple Views

Abstract: Occluding contours from an image sequence with view-point specifications determine a bounding volume approximating the object generating the contours. The initial creation and continual refinement of the approximation requires a volumetric representation that facilitates modification yet is descriptive of surface detail. The ``volume segment'' representation presented in this paper is one such representation.

Image sequence processing and dynamic scene analysis

Abstract: I Overview.- Overview on Image Sequence Analysis.- Dynamic Scene Analysis.- II Image Sequence Coding.- Recursive Motion Compensation: A Review.- The Differential Method for Image Motion Estimation.- Edges in Visual Scenes and Sequences: Application to Filtering, Sampling and Adaptive DPCM Coding.- Movement-Compensated Interframe Prediction for NTSC Color TV Signals.- Coding of Colour TV Signals with 34 MBit/s Transmission Rate.- Analysis of Different Displacement Estimation Algorithms for Digital Television Signals.- An Adaptive Gradient Approach to Displacement Estimation.- Motion Parameter Estimation in TV-Pictures.- Image Sequence Coding Using Scene Analysis and Spatio-Temporal Interpolation.- Two Motion Adaptive Interframe Coding Techniques for Air to Ground Video Signals.- Motion Estimation in a Sequence of Television Pictures.- Comparative Study Between Intra- and Inter-Frame Prediction Schemes.- A Narrow-Band Video Communication System for the Transmission of Sign Language Over Ordinary Telephone Lines.- Classification and Block Coding of the Frame Difference Signal.- Histograms of Image Sequence Spectra.- III Scene Analysis and Industrial Applications.- Determining 3-D Motion and Structure of a Rigid Body Using Straight Line Correspondences.- Comparison of Feature Operators for use in Matching Image Pairs.- Displacement Estimation for Objects on Moving Backgroud.- Linear Filtering in Image Sequences.- Photometric Stereo For Moving Objects.- On the Selection of Critical Points and Local Curvature Extrema of Region Boundaries for Interframe Matching.- Image Segmentation Considering Properties of the Human Visual System.- A Fast Edge Detection Algorithm Matching Visual Contour Perception.- Image Sequence Analysis for Target Tracking.- Track Acquisition of Sub-Pixel Targets.- A Pre-Processor for the Real-Time Interpretation of Dynamic Scenes.- Control of an Unstable Plant by Computer Vision.- Real-time Processing of Rasterscan Images.- 3-D Kalman Filtering of Image Sequences.- Atmospheric Disturbances Tracking in Satellite Images.- Aspects of Dynamic Scene Analysis in Meteorology.- IV Biomedical Applications.- Processing and Analysis of Radiographic Image Sequences.- Image Sequence Processing and Pattern Recognition of Biomedical Pictures.- A Rule-Based System for Characterizing Blood Cell Motion.- Three Dimensional Imaging from Computed Tomograms.- Model Based Analysis of Scintigraphic Image Sequences of the Human Heart.

Direct Computation of the Focus of Expansion

Abstract: Optical flow carries valuable information about the nature and depth of surfaces and the relative motion between observer and objects. In the extraction of this information, the focus of expansion plays a vital role. In contrast to the current approaches, this paper presents a method for the direct computation of the focus of expansion using an optimization approach. The optical flow can then be computed using the focus of expansion.

An estimation-theoretic approach to terrain image segmentation

Abstract: A method for modeling images of natural terrain is developed and applied to the segmentation of aerial photographic data. An underlying stochastic structure based on linear filtering concepts provides a means of modeling the terrain in local areas of the image. Superimposed on this is a Markov random field that describes transitions from regions of one terrain type to another. Maximum likelihood and maximum a posteriori estimation are applied to estimate regions of similar terrain. Results of application to digitized aerial photographs are presented and discussed.

Application of the Conditional Population-Mixture Model to Image Segmentation

Abstract: The problem of image segmentation is considered in the context of a mixture of probability distributions. The segments fall into classes. A probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. A numerical example is given. Choice of the number of classes, using Akaike's information criterion (AIC) for model identification, is illustrated.

Efficient 3-D Object Representations for Industrial Vision Systems

Abstract: The representation of 3-D objects is an important step in solving many problems in scene analysis. One of the most successful techniques is that based on the surfaces of objects. We describe several methods for obtaining such surface representations from various types of intrinsic images. In particular, previous work is reviewed and an algorithm based on region growing is investigated in terms of its efficiency in segmenting a set of points in 3-D space into planar faces. Information on the neighborhood structure of the points in the form of a spatial proximity graph is used to direct the segmentation. Applications to industrial objects are demonstrated.

Gray scale image processor

Abstract: A method for determining the average gray value of a plurality of regions within a digitized electronic video image and for determining the color of the region from the average gray value. Initial image segmentation is accomplished by thresholding a multibit digital value into a one bit black and white representation of a picture element of the region. The gray values of the picture elements within either a black or a white region can then be analyzed to determine the average gray value by constructing a histogram of each region and eliminating from the histogram the picture elements not associated with the actual color or shading of the region of interest. The remaining picture elements are averaged to obtain the average gray value for the region.

Determining velocity maps by spatio-temporal neighborhoods from image sequences

Abstract: Velocity information is not only important for determining velocities and trajectories of objects but also important as a cue for image segmentation. This paper presents a method of computing velocity information from a sequence of images. It uses velocity estimates of the prominent feature points as reliable initial estimates and propagates them efficiently to other points using a constraint relation between neighboring points. The result obtained from the first two frames is used for efficient analysis of subsequent frames. The presented method allows objects to translate and rotate in the 3-D world and may occlude one another. It is assumed, however, that objects are rigid and their motion is smooth. Experimental results arc shown for several synthetic and real world images.

Using Pyramids to Define Local Thresholds for Blob Detection

Abstract: A method of detecting blobs in images is described. The method involves building a succession of lower resolution images and looking for spots in these images. A spot in a low resolution image corresponds to a distinguished compact region in a known position in the original image. Further, it is possible to calculate thresholds in the low resolution image, using very simple methods, and to apply those thresholds to the region of the original image corresponding to the spot. Examples are shown in which variations of the technique are applied to several images.

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.

The structure of the human motion detection system

Abstract: Unlike technical pattern recognizers, humans are adept at the detection of regions of coherent movements in changing images. Possible physiological mechanisms for this ability are discussed in terms of simple mechanistic models, and the results of psychophysical experiments are presented. These results are compatible with two different mechanistic interpretations. The main result is that the human movement detectors are tuned, and a whole ensemble of mechanisms, tuned to different velocities, reside at any location in the visual field. Thus an observer may easily see two velocity vectors simultaneously at a given place. Segregation occurs when different detectors are stimulated at each side of a border. The spatiotemporal parameters that characterize the units limit the resolution in time and space, whereas sensitivity depends on the number of units that participate in a detection. This number may range between a few (perhaps one) to a thousand or more. Apparently, resolution can be traded against noise immunity. It is argued that technical systems developed on a similar basis might be useful as preprocessors of sequences of images in order to detect features of interest (coherent regions) and to suggest a first rough image segmentation.

Optimal Quadtrees for Image Segments

Abstract: Quadtrees are compact hierarchical representations of images. In this paper, we define the efficiency of quadtrees in representing image segments and derive the relationship between the size of the enclosing rectangle of an image segment and its optimal quadtree. We show that if an image segment has an enclosing rectangle having sides of lengths x and y, such that 2N-1 × max (x, y) ? 2N, then the optimal quadtree may be the one representing an image of size 2N × 2N or 2N+1 × 2N+1. It is shown that in some situations the quadtree corresponding to the larger image has fewer nodes. Also, some necessary conditions are derived to identify segments for which the larger image size results in a quadtree which is no more expensive than the quadtree for the smaller image size.

Multiple-Window Parallel Adaptive Boundary Finding in Computer Vision

Abstract: The problem considered in this paper is the estimation of highly variable object boundaries in noisy images. Boundaries may be those of a tank in an IR image, a spinal canal in a CAT scan, a cloud in a visible light image, etc. Or they may be internal to an object such as the boundary between a spherical surface and a cylindrical surface in a manufactured object. The focus of the paper is on parallel multiple-window boundary estimation algorithms. Here the image field is parti-tioned into an array of rectangular windows, and boundary finders are run simultaneously within the windows. The boundary segments found within the windows are then seamed together to obtain meaningful global boundaries. The entire procedure is treated within a maximum likelihood estimation framework that we have developed for boundary finding. Although our multiple-window estimation approach can be used with a number of local boundary finding algorithms, we concen-trate on one which is based on dynamic programming and will produce the true maximum likelihood boundary. Some theoretical considera-tions for boundary model design and boundary-finding runtime are covered. Included is the use of a low computational cost F-test for test-ing whether a window contains a boundary, and an analytical treatment which shows that use of coarse pixels with a chi-square test or an F-test improves the probability of correctly recognizing whether a boundary is present in a window.

An Iterative Approach to Region Growing Using Associative Memories

Abstract: The postulate is made that ``any computation which can be performed recursively can be performed easily and efficiently by iteration coupled with association.'' The ``easily and efficiently'' part of that postulate is nontrivial to prove, and is shown by examples in this paper. The use of association leads directly to potential implementation by content-addressable memories. The example addressed is region growing, often given as a classical example of the use of recursive control structures in image processing. Recursive control structures, however, are somewhat awkward to build in hardware, where the intent is to segment an image at raster scan rates. This paper describes an algorithm and hardware structure capable of per-forming region labeling iteratively at scan rates. Every pixel is individually labeled with an identifier signifying to which region it belongs. The difficulties which often justify recursion (``U''- and ``N''-shaped regions, etc.) are handled by maintaining an equivalence table in hardware, transparent to the computer, which reads the labeled pixels. The mechanism for updating the region map is explained in detail. Furthermore, simulation of the associative memory has been demon-strated to be an effective implementation of region growing in a serial computer.

Quadtrees and Pyramids: Hierarchical Representation of Images

Abstract: This paper reviews methods of variable-resolution representation or approximation of digital images based on the use of trees of degree 4 (“quadtrees”). It also discusses the multi-resolution representation of an image by an exponentially tapering “pyramid” of arrays, each half the size of the preceding. Basic properties of these representations, and their uses in image segmentation and property measurement, are summarized.

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...

Generating Object Descriptions for Model Retrieval

Abstract: A computer vision system is proposed, in which the recogni-tion of an object involves two interacting processes: model retrieval and model verification. The goal of the model retrieval process is to generate a proper structural description of the object in the input image, and use the description to retrieve candidate object models from the associative memory of the vision system. The present study explores one way of deriving such an object shape description from a single image. Regularity constraints and a preference rule are used to restrict the solutions to a preferred interpretation of geometric contours. Local interpretation is then propagated to neighboring regions. Through a proper control on the interaction between constraints and consistency checking, a rough object description in terms of visible surface orientations can be gener-ated. A computer vision system using this approach has been imple-mented and it is described in some details.

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.

Robot Vision For Machine Part Recognition

Abstract: Most industrial applications of computer vision can be categorized into two groups. They are (1) visual inspection and (2) machine parts recognition. There are several review articles for automatic visual inspection [1,30,31]. This paper gives a brief review of robot vision system for machine part recognition. A robot vision system for machine parts recognition contains four sub-systems: (1) sensing, (2) segmentation, (3) description, and (4) recognition. A block diagram of such a system is shown in Fig. 1.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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.

Elimination of redundant operations for a fast Sobel operator

Abstract: Automatic image segmentation by edge detection techniques often involves recursive operations that incorporate redundant computations. Two methods for implementation of a fast Sobel operator are proposed, which eliminate a considerable amount of redundancy at the expense of increased storage capacity. Comparisons between the standard method and the proposed fast methods are included.

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.