Showing papers on "Image segmentation published in 1988"

A survey of thresholding techniques

Abstract: In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.

Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression

Abstract: A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D Gabor representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations, which provide a complete image description in terms of locally windowed 2-D spectral coordinates embedded within global 2-D spatial coordinates. In the present neural network approach, based on interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights, the network finds coefficients for complete conjoint 2-D Gabor transforms without restrictive conditions. In wavelet expansions based on a biologically inspired log-polar ensemble of dilations, rotations, and translations of a single underlying 2-D Gabor wavelet template, image compression is illustrated with ratios up to 20:1. Also demonstrated is image segmentation based on the clustering of coefficients in the complete 2-D Gabor transform. >

Segmentation through variable-order surface fitting

Abstract: The solution of the segmentation problem requires a mechanism for partitioning the image array into low-level entities based on a model of the underlying image structure. A piecewise-smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete, noiseless image reconstruction based on the extracted functions and regions. Surface curvature sign labeling provides an initial coarse image segmentation, which is refined by an iterative region-growing method based on variable-order surface fitting. Experimental results show the algorithm's performance on six range images and three intensity images. >

A statistical and geometrical edge detector for SAR images

Abstract: A constant-false-alarm-rate (CFAR) edge detector based on the ratio between pixel values is described. The probability distribution of the image obtained by applying the edge detector is derived. Hence, the decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood. For a better and finer detection, the edge detector operates along the four usual directions over windows of increasing sizes. A test performed, for a given direction, on a radar image of an agricultural scene shows good agreement with the theoretical study. The operator is compared with the CFAR edge detectors suitable for radar images. >

An Adaptive Clustering Algorithm For Image Segmentation

Abstract: A generalization of the K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image is proposed. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in an iterative procedure involving averaging over a sliding window whose size decreases as the algorithm progresses. Results with an eight-neighbor Gibbs random field model applied to pictures of industrial objects and a variety of other images show that the algorithm performs better than the K-means algorithm and its nonadaptive extensions. >

A spatial thresholding method for image segmentation

Abstract: Several model-based algorithms for threshold selection are presented, concentrating on the two-population univariate case in which an image contains an object and background. It is shown how the main ideas behind two important nonspatial thresholding algorithms follow from classical discriminant analysis. Novel thresholding algorithms that make use of available local/spatial information are then given. It is found that an algorithm using alternating mean thresholding and median filtering provides an acceptable method when the image is relatively highly contaminated, and seems to depend less on initial values than other procedures. The methods are also applicable to multispectral k-population images. >

Integrating region growing and edge detection

Abstract: The authors present a method that combines region growing and edge detection for image segmentation. They start with a split-and-merge algorithm where the parameters have been set up so that an oversegmented image results. Then region boundaries are eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree, in this case). >

Detecting left ventricular endocardial and epicardial boundaries by digital two-dimensional echocardiography

Abstract: Cardiac function is evaluated using echocardiographic analysis of shape attributes, such as the heart wall thickness or the shape change of the heart wall boundaries. This requires that the complete boundaries of the heart wall be detected from a sequence of two-dimensional ultrasonic images of the heart. The image segmentation process is made difficult since these images are plagued by poor intensity contrast and dropouts caused by the intrinsic limitations of the image formation process. Current studies often require having trained operators manually trace the heart walls. A review of previous work is presented, along with how this problem can be viewed in the context of the computer vision area. A novel algorithm is presented for detecting the boundaries. This algorithm first detects spatially significant features based on the measurement of image intensity variations. Since the detection step suffers from false alarms and missing boundary points, further processing uses high-level knowledge about the heart wall to label the detected features for noise rejection and to fill in the missing points by interpolation. >

A rule based approach for visual pattern inspection

Abstract: The authors investigate the use of a priori knowledge about a scene to coordinate and control bilevel image segmentation, interpretation, and shape inspection of different objects in the scene. The approach is composed of two main steps. The first step consists of proper segmentation and labeling of individual regions in the image for subsequent ease in interpretation. General as well as scene-specific knowledge is used to improve the segmentation and interpretation processes. Once every region in the image has been identified, the second step proceeds by testing different regions to ensure they meet the design requirements, which are formalized by a set of rules. Morphological techniques are used to extract certain features from the previously processed image for rule verification purposes. As a specific example, results for detecting defects in printed circuit boards are presented. >

Autonomous robotic vehicle road following

Abstract: A description is given of the system architecture of an autonomous vehicle and its real-time adaptive vision system for road-following. The vehicle is a 10-ton armored personnel carrier modified for robotic control. A color transformation that best discriminates road and nonroad regions is derived from labeled data samples. A maximum-likelihood pixel classification technique is then used to classify pixels in the transformed color image. The vision system adapts itself to road changes in two ways; color transformation parameters are updated infrequently to accommodate significant road color changes, and classifier parameters are updated every processing cycle to deal with gradual color and intensity changes. To reduce unnecessary computation, only the most likely road region in the segmented image is selected, and a polygonal representation of the detected road region boundary is transformed from the image coordinate system to the local vehicle coordinate system based on a flat-earth assumption. >

A new method for image segmentation

Abstract: A method is presented for finding a threshold surface which involves the ideas used in other methods but attempts to overcome some of their disadvantages. The method uses the gradient map of the image to point at well-defined portions of object boundaries in it. Both the location and gray levels at these boundary points make them good choices for local thresholds. These point values are then interpolated, yielding the threshold surface. A method for fitting a surface to this set of points, which are scattered in a manner unknown in advance, becomes necessary. Several possible approaches are discussed, and the implementation of one of them is described in detail. Two versions of the C.K. Chow and T. Kaneko algorithm (1972) and the present algorithm are applied to a few images, and the latter is shown to give better results, matching human performance quite well. >

Application Of Recurrent Iterated Function Systems To Images

Abstract: A new fractal technique for the analysis and compression of digital images is presented. It is shown that a family of contours extracted from an image can be modelled geometrically as a single entity, based on the theory of recurrent iterated function systems (RIFS). RIFS structures are a rich source for deterministic images, including curves which cannot be generated by standard techniques. Control and stability properties are investigated. We state a control theorem - the recurrent collage theorem - and show how to use it to constrain a recurrent IFS structure so that its attractor is close to a given family of contours. This closeness is not only perceptual; it is measured by means of a min-max distance, for which shape and geometry is important but slight shifts are not. It is therefore the right distance to use for geometric modeling. We show how a very intricate geometric structure, at all scales, is inherently encoded in a few parameters that describe entirely the recurrent structures. Very high data compression ratios can be obtained. The decoding phase is achieved through a simple and fast reconstruction algorithm. Finally, we suggest how higher dimensional structures could be designed to model a wide range of digital textures, thus leading our research towards a complete image compression system that will take its input from some low-level image segmenter.

Measuring Image Flow By Tracking Edge-lines

Abstract: This paper describes a technique for measuring the movement of edge-lines in a sequence of images by maintalning an image plane "flow model". Edge-lines are expressed as a set of parameter vectors representing the center-point, orientation and length of a segment. Each parameter vector is composed of an estimate, a temporal derivative, and their covariance matrix. Line segment parameters in the flow model are updated using a Kalman filter. The eorrespondance of observed edge-lines segments to segments predicted from the flow model is determined by a linear complexity algorithm using distance normalized by covariance. The existence of segments in the flow model is controlled using a confidence factor. This technique is in everyday use as part of a larger system for building 3-D scene descriptions using a camera mounted on a robot arm. A near video-rate hardware implementation is currently under development

Optimal Corner Detector

Abstract: A corner is defined as the junction point of two or more straight line edges Corners are special features in a image They are of great use in computing the optical flow and structure from motion In this paper, we report an optimal corner detector which uses a mathematical model for a corner An optimal gray tone corner detector is derived for a restricted case of corners, ie corners made by lines which are symmetric about a horizontal axis The resultant corner detector is described by product of sine in x and exponential in y direction in a portion of the mask and by the product of two sines in x and y directions in the remaining portion of it It is then generalized to include any corner of an arbitrary angle and orientation This results in an approximation of all corners by a total of twelve major types It is observed that all the twelve masks can actually be configured with four smaller sub-masks, and this results in a significant reduction in the computetions The computations are further reduced by using the the separability of masks Results for synthetic and real scenes are reported

Image Segmentation and Reflection Analysis Through Color

Abstract: In this paper, we present an approach to color image understanding that can be used to segment and analyze surfaces with color variations due to highlights and shading. We begin with a theory that relates the reflected light from dielectric materials, such as plastic, to fundamental physical reflection processes, and describes the color of the reflected light as a linear combination of the color of the light due to surface reflection (highlights) and body reflection (object color). This theory is used in an algorithm that separates a color image into two parts: an image of just the highlights, and the original image with the highlights removed. In the past, we have applied this method to hand-segmented images. The current paper shows how to perform automatic segmentation method by applying this theory in stages to identify the object and highlight colors. The result is a combination of segmentation and reflection analysis that is better than traditional heuristic segmentation methods (such as histogram thresholding), and provides important physical information about the surface geometry and material properties at the same time. We also show the importance of modeling the camera properties for this kind of quantitative analysis of color. This line of research cRn lead to physics-based image segmentation methods that are both more reliable and more useful than traditional segmentation methods.

Finite prolate spheroidal sequences and their applications. II. Image feature description and segmentation

Abstract: For pt.I see ibid., vol.9, no.6, p.787 (1987). The problem of uncertainty in image feature description is discussed, and it is shown how finite prolate spheroidal sequences can be used in the construction of feature descriptions that combine spatial and frequency-domain locality in an optimal way. Methods of constructing such optimal feature sets, which are suitable for graphical implementation, are described, and some generalizations of the quadtree concept are presented. These methods are illustrated by examples from image processing applications, including feature extraction and texture description. The problem of image segmentation is discussed, and the importance of scale invariance in overcoming the limitations imposed by uncertainty is demonstrated. A novel texture segmentation algorithm that is based on a combination of the new feature description and multiresolution techniques is described and shown to give accurate segmentations on a range of synthetic and natural textures. >

Segmentation of remotely-sensed images by a split-and-merge process+

Abstract: This paper describes the application of an image segmentation technique to remotely-sensed terrain images used for environmental monitoring. The segmentation is a preprocessing operation which is a...

Traffic Spatial Measurements Using Video Image Processing

Abstract: Algorithms of video traffic image processing, presented below, have been developed by CMM and INRETS using 256x256x6 bits images collected from various scenes covering a 150 m area of a freeway under various weather conditions. First, road detection is automatically performed. The road and traffic lanes images are used to derive relationships between real and image distances and to build image transformations independant from the perspective view. Then, markers of the vehicles are extracted using geometrical adapted filters. These markers are joined to define a single marker for each vehicle. Finally, vehicles trajectories are built and traffic variables are specified.

SURVEY A Survey of Thresholding Techniques

Abstract: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3GI AND Y c CHBN Department of Electrical Engineering, University of Waterloo, Waterloo, Canada N2L 3GI Received July 7,1984; accepted July 9,1987 In digital image processing, thresholding is a well-known technique for image segmentation Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput Vision Graphics 62 Image Process 7,

A Novel Approach To Colour Constancy

Abstract: By approaching colour constancy as a problem of predicting colour appearance, we derive the colour constancy equation, which we use to enumerate those properties of illuminant and surface reflectance required for colour constancy. We then use a physical realisability constraint on surface reflectances to construct the set of illuminants under which the image observed can have arisen. Two distinct algorithms arise from employing this constraint in conjunction with the colour constancy equation: the first corresponds to normalisation according to a coefficient rule, the second is considerably more complex, and allows a large number of parameters in the illuminant to be recovered. The simpler algorithm has been tested extensively on images of real Mondriaan’s, taken under different coloured lights and displays good constancy. The results also indicate that good constancy requires that receptoral gain be controlled.

A model-fitting approach to cluster validation with application to stochastic model-based image segmentation

Abstract: A model-fitting approach to the cluster validation problem based upon Akaike's information criterion (AIC) is proposed. The explicit evaluation of the AIC for the image segmentation problem is achieved through an approximate maximum-likelihood-estimation algorithm. The efficacy of the proposed approach is demonstrated through experimental results for both synthetic mixture data, where the number of clusters is known, and stochastic model-based image segmentation operating on real-world images, for which the number of clusters is unknown. This approach is shown to correctly identify the known number of clusters in the synthetically generated data and to result in good subjective segmentations in aerial photographs. >

A maximum likelihood approach to segmenting range data

Abstract: The problem of segmenting a range image into homogeneous regions in each of which the range data correspond to a different surface is considered. The segmentation sought is a maximum-likelihood (ML) segmentation. Only planes, cylinders, and spheres are considered as presented in the image. The basic approach to segmentation is to divide the range image into windows, classify each window as a particular surface primitive, and group like windows into surface regions. Mixed windows are detected by testing the hypothesis that a window is homogeneous. Homogeneous windows are classified according to a generalized likelihood ratio test which is computationally simple and incorporates information from adjacent windows. Grouping windows of the same surface types is cast as a weighted ML clustering problem. Finally, mixed windows are segmented using an ML hierarchical segmentation algorithm. A similar approach is taken for segmenting visible-light images of Lambertian objects illuminated by a point source at infinity. >

Relaxation Neural Network For Complete Discrete 2-D Gabor Transforms

Abstract: It is often desirable in image processing to represent image structure in terms of a set of coefficients on a family of expansion functions. For example, familiar approaches to image coding, feature extraction, image segmentation, statistical and spectral analysis, and compression, involve such methods. It has invariably been necessary that the expansion functions employed comprise an orthogonal basis for the image space, because the problem of obtaining the correct coefficients on a non-orthogonal set of expansion functions is usually arduous if not impossible. Oddly enough, image coding in biological visual systems clearly involves non-orthogonal expansion functions. The receptive field profiles of visual neu-rons with linear response properties have large overlaps and large inner products, and are suggestive of a conjoint (spatial and spectral) "2-D Gabor representation" (Daugman 1980, 1985). The 2-D Gabor transform has useful decorrelating properties and provides a conjoint image description resembling a speech spectrogram, in which local 2-D image regions are analyzed for orientation and spatial frequency content, but its expansion functions are non-orthogonal. This paper describes a three-layered relaxation "neural network" that efficiently computes the correct coefficients for this and other, non-orthogonal, image transforms. Examples of applications which are illustrated include: (1) image compression to below 1.0 bit/pixel, and (2) textural image segmentation based upon the statistics of the 2-D Gabor coefficients found by the relaxation network.

Curvilinear feature detection from curvature estimation

Abstract: The DOS/sup +/ method, a particular case of the difference of slopes (DOS) approach, is shown to be effective for estimating curvature in terms of signal detectability. Relationships are established between the DOS/sup +/ parameters and feature parameters to distinguish corner and curve features and to accurately determine the locations of corners, transitions between straight lines and curves, and curve centers. Examples show the results of this method applied to curvilinear features and diagrams. >

A network for multiscale image segmentation

Abstract: Detecting edges of objects in their images is a basic problem in computational vision. The authors present the major ideas behind the use of scale space and anisotropic diffusion for edge detection, show that anisotropic diffusion can enhance edges, suggest a network implementation of anisotropic diffusion, and provide design criteria for obtaining networks performing scale space and edge detection. The results of a software implementation are shown. >