Showing papers on "Image segmentation published in 1990"
TL;DR: A method that combines region growing and edge detection for image segmentation is presented and is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise.
Abstract: A method that combines region growing and edge detection for image segmentation is presented. The authors start with a split-and merge algorithm wherein the parameters have been set up so that an over-segmented image results. Region boundaries are then 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). The algorithms were implemented in the C language on a Sun 3/160 workstation running under the Unix operating system. Simple tool images and aerial photographs were used to test the algorithms. The impression of human observers is that the method is very successful on the tool images and less so on the aerial photograph images. It is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise. >
567 citations
01 Sep 1990-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: Computer simulation results reveal that most algorithms perform consistently well on images with a bimodal histogram, however, all algorithms break down for a certain ratio of population of object and background pixels in an image, which in practice may arise quite frequently.
Abstract: A comparative performance study of five global thresholding algorithms for image segmentation was investigated. An image database with a wide variety of histogram distribution was constructed. The histogram distribution was changed by varying the object size and the mean difference between object and background. The performance of five algorithms was evaluated using the criterion functions such as the probability of error, shape, and uniformity measures Attempts also have been made to evaluate the performance of each algorithm on the noisy image. Computer simulation results reveal that most algorithms perform consistently well on images with a bimodal histogram. However, all algorithms break down for a certain ratio of population of object and background pixels in an image, which in practice may arise quite frequently. Also, our experiments show that the performances of the thresholding algorithms discussed in this paper are data-dependent. Some analysis is presented for each of the five algorithms based on the performance measures.
556 citations
TL;DR: The simulation results indicate that the proposed segmentation algorithm yields the most accurate segmented image on the color coordinate proposed by Ohta et al.
549 citations
01 Sep 1990-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: A clustering algorithm for segmenting the color images of natural scenes that permits utilization of all the color properties for segmentation and inherently recognizes their respective cross correlation.
Abstract: This paperr describes a clustering algorithm for segmenting the color images of natural scenes. The proposed method operates in the 1976 CIE (L∗, a∗, b∗)-uniform color coordinate system. It detects image clusters in some circular-cylindrical decision elements of the color space. This estimates the clusters' color distributions without imposing any constraints on their forms. Surfaces of the decision elements are formed with constant lightness and constant chromaticity loci. Each surface is obtained using only 1D histogramsof the L∗, H°, C∗ cylindrical coordinates of the image data or the extracted feature vector. The Fisher linear discriminant method is then used to project simultaneously the detected color clusters onto a line for 1D thresholding. This permits utilization of all the color properties for segmentation and inherently recognizes their respective cross correlation. In this respect, the proposed algorithm also differs from the multiple histogram-based thresholding schemes.
282 citations
16 Jun 1990
TL;DR: By creating a burst image from the original document image, the processing time of the Hough transform can be reduced by a factor of as much as 7.4 for documents with gray-scale images and interline spacing can be determined more accurately.
Abstract: As part of the development of a document image analysis system, a method, based on the Hough transform, was devised for the detection of document skew and interline spacing-necessary parameters for the automatic segmentation of text from graphics. Because the Hough transform is computationally expensive, the amount of data within a document image is reduced through the computation of its horizontal and vertical black runlengths. Histograms of these runlengths are used to determine whether the document is in portrait or landscape orientation. A gray scale burst image is created from the black runlengths that are perpendicular to the text lines by placing the length of the run in the run's bottom-most pixel. By creating a burst image from the original document image, the processing time of the Hough transform can be reduced by a factor of as much as 7.4 for documents with gray-scale images. Because only small runlengths are input to the Hough transform and because the accumulator array is incremented by the runlength associated with a pixel rather than by a factor of 1, the negative effects of noise, black margins, and figures are avoided. Consequently, interline spacing can be determined more accurately. >
263 citations
TL;DR: Recognition experiments with a prototype system for a variety of complex printed documents shows that the proposed system is capable of reading different types of printed documents at an accuracy rate of 94.8–97.2%.
258 citations
TL;DR: A computer algorithm which segments gray-scale images into regions of interest (objects) has been developed that can provide the basis for scene analysis (including shape-parameter calculation) or surface-based, shaded-graphics display.
Abstract: A computer algorithm which segments gray-scale images into regions of interest (objects) has been developed. These regions can provide the basis for scene analysis (including shape-parameter calculation) or surface-based, shaded-graphics display. The algorithm creates a tree structure for image description by defining a linking relationship between pixels in successively blurred versions of the initial image. The image is described in terms of nested light and dark regions. This algorithm, successfully implemented in one, two, and three dimensions, can theoretically work with any number of dimensions. The interactive postprocessing developed technique selects regions from the descriptive tree for display in several ways: pointing to a branch of the image description tree, specifying by sliders the range of scale and/or intensity of all regions which should be displayed, and pointing (on the original image) to any pixel in the desired region. The algorithm has been applied to approximately 15 computer tomography (CT) images of the abdomen. >
256 citations
TL;DR: Results obtained show that direct feature statistics such as the Bhattacharyya distance are not appropriate evaluation criteria if texture features are used for image segmentation, and that the Haralick, Laws and Unser methods gave best overall results.
228 citations
TL;DR: A two-stage method of image segmentation based on gray level cooccurrence matrices that robustly segments an image into homogeneous areas and generates an edge map is described and extends easily to general edge operators.
Abstract: A two-stage method of image segmentation based on gray level cooccurrence matrices is described. An analysis of the distributions within a cooccurrence matrix defines an initial pixel classification into both region and interior or boundary designations. Local consistency of pixel classification is then implemented by minimizing the entropy of local information, where region information is expressed via conditional probabilities estimated from the cooccurrence matrices, and boundary information via conditional probabilities which are determined a priori. The method robustly segments an image into homogeneous areas and generates an edge map. The technique extends easily to general edge operators. An example is given for the Canny operator. Applications to synthetic and forward-looking infrared (FLIR) images are given. >
228 citations
Book•
01 Jan 1990
TL;DR: System Architecture and Reasoning Scheme in SIGMA and LLVE: Expert System for Top-Down Image Segmentation are presented, which describes the architecture and reasoning scheme used for evidence accumulation and results.
Abstract: 1. Introduction.- 2. System Architecture and Reasoning Scheme in SIGMA.- 3. Algorithms for Evidence Accumulation.- 4. LLVE: Expert System for Top-Down Image Segmentation.- 5. Experimental Results and Performance Evaluation.- 6. Conclusion.- References.
226 citations
04 Dec 1990
TL;DR: The problem of matching range images of human faces for the purpose of establishing a correspondence between similar features of two faces is addressed and a graph matching algorithm is applied to establish the optimal correspondence.
Abstract: The problem of matching range images of human faces for the purpose of establishing a correspondence between similar features of two faces is addressed. Distinct facial features correspond to convex regions of the range image of the face, which is obtained by a segmentation of the range image based on the sign of the mean and Gaussian curvature at each point. Each convex region is represented by its extended Gaussian image, a 1-1 mapping between points of the region and points on the unit sphere that have the same normal. Several issues are examined that are associated with the difficult problem of interpolation of the values of the extended Gaussian image and its representation. A similarity measure between two regions is obtained by correlating their extended Gaussian images. To establish the optimal correspondence, a graph matching algorithm is applied. It uses the correlation matrix between convex regions of the two faces and incorporates additional relational constraints that account for the relative spatial locations of the convex regions in the domain of the range image. >
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TL;DR: A model is described for image segmentation that tries to capture the low-level depth reconstruction exhibited in early human vision, giving an important role to edge terminations, which gives rise to a family of optimal contours, called nonlinear splines, that minimize length and the square of curvature.
Abstract: A model is described for image segmentation that tries to capture the low-level depth reconstruction exhibited in early human vision, giving an important role to edge terminations. The problem is to find a decomposition of the domain D of an image that has a minimum of disrupted edges-junctions of edges, crack tips, corners, and cusps-by creating suitable continuations for the disrupted edges behind occluding regions. The result is a decomposition of D into overlapping regions R/sub 1/ union . . . union R/sub n/ ordered by occlusion, which is called the 2.1-D sketch. Expressed as a minimization problem, the model gives rise to a family of optimal contours, called nonlinear splines, that minimize length and the square of curvature. These are essential in the construction of the 2.1-D sketch of an image, as the continuations of disrupted edges. An algorithm is described that constructs the 2.1-D sketch of an image, and gives results for several example images. The algorithm yields the same interpretations of optical illusions as the human visual system. >
16 Jun 1990
TL;DR: The authors empirically compare three algorithms for segmenting simple, noisy images and conclude that contextual information from MRF models improves segmentation when the number of categories and the degradation model are known and that parameters can be effectively estimated.
Abstract: The authors empirically compare three algorithms for segmenting simple, noisy images: simulated annealing (SA), iterated conditional modes (ICM), and maximizer of the posterior marginals (MPM). All use Markov random field (MRF) models to include prior contextual information. The comparison is based on artificial binary images which are degraded by Gaussian noise. Robustness is tested with correlated noise and with object and background textured. The ICM algorithm is evaluated when the degradation and model parameters must be estimated, in both supervised and unsupervised modes and on two real images. The results are assessed by visual inspection and through a numerical criterion. It is concluded that contextual information from MRF models improves segmentation when the number of categories and the degradation model are known and that parameters can be effectively estimated. None of the three algorithms is consistently best, but the ICM algorithm is the most robust. The energy of the a posteriori distribution is not always minimized at the best segmentation. >
16 Jun 1990
TL;DR: A rule-based system for automatically segmenting a document image into regions of text and nontext is presented and allows easy fine tuning of the algorithmic steps to produce robust rules, to incorporate additional tools (as they become available), and to handle special segmentation needs.
Abstract: A rule-based system for automatically segmenting a document image into regions of text and nontext is presented. The initial stages of the system perform image enhancement functions such as adaptive thresholding, morphological processing, and skew detection and correction. The image segmentation process consists of smearing the original image via the run length smoothing algorithm, calculating the connected components locations and statistics, and filtering (segmenting) the image based on these statistics. The text regions can be converted (via an optical character reader) to a computer-searchable form, and the nontext regions can be extracted and preserved. The rule-based structure allows easy fine tuning of the algorithmic steps to produce robust rules, to incorporate additional tools (as they become available), and to handle special segmentation needs. >
01 Sep 1990
TL;DR: A new and flexible implementation of the watershed transformation based on a progressive flooding of the picture and it works for n-dimensional images, and its extension to general graphs is straightforward.
Abstract: The watershed transformation is a very powerful image analysis tool provided by mathematical morphology However, most existing watershed algorithms are either too time consuming or insufficiently accurate The purpose of this paper is to introduce a new and flexible implementation of this transformation It is based on a progressive flooding of the picture and it works for n-dimensional images Pixels are first sorted in the increasing order of their gray values Then, the successive gray levels are processed in order to simulate the flooding propagation A distributive sorting technique combined with breadth-first scannings of each gray level allow an extremely fast computation Furthermore, the present algorithm is very general since it deals with any kind of digital grid and its extension to general graphs is straightforward Its interest with respect to image segmentation is illustrated by the extraction of geometrical shapes from a noisy image, the separation of 3-dimensional overlapping particles and by the segmentation of a digital elevation model using watersheds on images and graphs
16 Jun 1990
TL;DR: A technique for image segmentation using shape-directed covers is described and applied to the fully automatic analysis of complex printed-page layouts, which for some tasks is superior to strategies currently emphasized in the literature, including bottom-up and top-down.
Abstract: A technique for image segmentation using shape-directed covers is described and applied to the fully automatic analysis of complex printed-page layouts. The structure of the background (white space) is analyzed, assisted by an enumeration of all maximal white rectangles. For this enumeration, the most computationally expensive step, an algorithm has been developed that, aside from a sort, achieves an expected runtime linear in the number of black connected components. The crucial engineering decision is the specification of a partial order on white rectangles to express domain-specific knowledge of preferred shapes and sizes. This order determines a sequence of partial covers of the background, and thus, a sequence of nested page segmentations. In experimental trials on Manhattan layouts, good segmentations often occur early in this sequence, using a simple and uniform shape-direction rule. This is a global-to-local strategy, which for some tasks is superior to strategies currently emphasized in the literature, including bottom-up and top-down. >
01 Sep 1990
TL;DR: A fractal image coding technique based on a mathematical theory of iterated transformations which encompasses deterministic fractal geometry and is a blockcoding technique which relies on the assumption that image redundancy can be efficiently exploited through blockwise selfiransformabiliiy.
Abstract: The notion of fractal image compression arises from the fact that the iteration of simple deterministic mathematical procedures can generate images with infinitely intricate geometries known as fracial images [2]. The purpose of research on fractalbased digital image coding is to solve the inverse problem of constraining this complexity to match the realworld complexity of realworld images. In this. paper we propose a fractal image coding technique based on a mathematical theory of iterated transformations [1 2 3 7] which encompasses deterministic fractal geometry. Initial results were reported in [7 8]. The main characteristics of this technique are that (i) it is fractal in the sense that it approximates an original image by a fractal image and (ii) it is a blockcoding technique which relies on the assumption that image redundancy can be efficiently exploited through blockwise selfiransformabiliiy. We therefore refer to it as fracial blockcoding. The codingdecoding system is based on the construction for each original image given to encode of a specific image transformation which when iterated on any initial image produces a sequence of images which converges to a fractal approximation of the original. We show how to design such coders and thoroughly describe the implementation of a system for monochrome still images. Extremely promising coding results were obtained.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
TL;DR: A model-fitting approach to the cluster validation problem based on Akaike's information criterion is proposed, and its efficacy and robustness are demonstrated through experimental results for synthetic mixture data and image data.
Abstract: A clustering scheme is used for model parameter estimation. Most of the existing clustering procedures require prior knowledge of the number of classes, which is often, as in unsupervised image segmentation, unavailable and must be estimated. This problem is known as the cluster validation problem. For unsupervised image segmentation the solution of this problem directly affects the quality of the segmentation. A model-fitting approach to the cluster validation problem based on Akaike's information criterion is proposed, and its efficacy and robustness are demonstrated through experimental results for synthetic mixture data and image data. >
01 Sep 1990
TL;DR: A coding algorithm for the vectorfield combining contour coding of regions of similar displacement with predictive coding of the vectors inside each region proves efficient and allows the estimator to work with decreased blocksize and to supply a distinctly improved displacement-compensation without spending more datarate for displacement-Compensation than the DFDestimator.
Abstract: STRACT A new approach to motion-estimation for hybrid''image sequence coding is presented. Instead of minimizing the displaced frame difference (DFD) the estimator introduced in this paper maximizes the probability to determine the ''true'' physical motion of the scene. The probability expression is derived from two models one for the statistics of the prediction error image and one for the interdependency of vectors in a vectorfield. The physical vectorfield is smoother than the vectorfield of a DFD-estimator and stronger statistical bindings between vectors exist. Therefore a coding algorithm for the vectorfield combining contour coding of regions of similar displacement with predictive coding of the vectors inside each region proves efficient. This allows the estimator to work with decreased blocksize and (even in DFD sense) to supply a distinctly improved displacement-compensation without spending more datarate for displacement-compensation than the DFDestimator.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
09 Apr 1990
TL;DR: In this article, the authors described two separate SAR image change detection schemes: (1) model based image segmentation and (2) image object identification using neural networks, which performed slightly better than the model based scheme in terms of both time and detection rates.
Abstract: The author describes two separate SAR (synthetic aperture radar) image change detection schemes: (1) model based image segmentation (2) image object identification using neural networks. The neural network approach performs slightly better than the model based scheme in terms of both time and detection rates. >
TL;DR: In this paper, an efficient method to delineate topographic basins from digital elevation models is presented, which is based upon mathematical morphology and it consists of two major steps: removing all the pits within the model by using an original morphological mapping, and then delineating topographical basins by using morphological thinnings with specific structuring elements.
16 Jun 1990
TL;DR: The Markov random field formalism is considered, a special case of the Bayesian approach, in which the probability distributions are specified by an energy function, and simple modifications to the energy can give a direct relation to robust statistics or can encourage hysteresis and nonmaximum suppression.
Abstract: An attempt is made to unify several approaches to image segmentation in early vision under a common framework. The energy function, or Markov random field, formalism is very attractive since it enables the assumptions used to be explicitly stated in the energy functions, and it can be extended to deal with many other problems in vision. It is shown that the specified discrete formulations for the energy function are closely related to the continuous formulation. When the mean field theory approach is used, several previous attempts to solve these energy functions are effectively equivalent. By varying the parameters of the energy functions, one can obtain a class of solutions and several nonlinear diffusion approaches to image segmentation, but it can be applied equally well to image or surface reconstruction (where the data are sparse). >
01 Mar 1990-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: Algorithms for raster-to-vector conversion capable of recognizing and fitting straight line segments, circles, arcs, and conic sections are presented along with results obtained from images of machine drawings.
Abstract: This paper presents the methods for preprocessing and vectorizing scan digitized images of engineering drawings for transferring the resulting data to commercially available CAD/CAM systems. The preprocessing steps include noise removal, void filling, image segmentation, line thinning, and contour extraction. Algorithms for raster-to-vector conversion capable of recognizing and fitting straight line segments, circles, arcs, and conic sections are presented along with results obtained from images of machine drawings.
04 Nov 1990
TL;DR: In this article, a model called B-snakes is presented, which can be used in areas such as edge detection, motion tracking, and stereo matching and more generally to solve problems of matching a deformable model to an image by means of energy minimization.
Abstract: A brief overview of deformable contour models, called snakes, is presented. These energy-minimizing curves are constrained by internal continuity forces (tension and bending) and guided by external image forces toward features. Such a tool can be used in areas such as edge detection, motion tracking, and stereo matching and more generally to solve problems of matching a deformable model to an image by means of energy minimization. The convergence speed has been improved by using parametric B-spline approximations of the curves; the model is called B-snakes. As this approximation can control the degree of continuity at the joints between adjacent curve segments, the B-snakes can have breakpoints and include corners. B-snakes have been applied to the extraction of building tops from aerial images, once a first estimate is obtained from a traditional stereo process. >
01 Nov 1990
TL;DR: In this paper, a segmentation algorithm using residual analysis to detect edges, then a region growing technique is used to obtain the final segmented image, which can then be used to instruct the robot to grip the object and move it to the required position.
Abstract: A new segmentation algorithm that can be used for robot applications is presented. The input images are dense range data of industrial parts. The image is segmented into a number of surfaces. The segmentation algorithm uses residual analysis to detect edges, then a region-growing technique is used to obtain the final segmented image. The use of the segmentation output for determining the best holdsite position and orientation of objects is studied. As compared to techniques based on intensity images, the use of range images simplifies the holdsite determination. This information can then be used to instruct the robot to grip the object and move it to the required position. The performance of the algorithm on a number of range images is presented. >
TL;DR: A totally automatic non-parametric clustering algorithm and its application to unsupervised image segmentation and systematic methods for automatic selection of an appropriate histogram cell size are developed and discussed.
TL;DR: In this article, a unified framework for color-constancy based segmentation of multispectral images is presented. But it assumes that the visual stimulus (image field) from a uniformly colored object is the sum of a small number of terms, each term being the product of a spatial and a spectral part.
Abstract: A unifying framework is presented for algorithms that use the bands of a multispectral image to segment the image at material (i.e., reflectance) boundaries while ignoring spatial inhomogeneities incurred by accidents of lighting and viewing geometry. The framework assumes that the visual stimulus (image field) from a uniformly colored object is the sum of a small number of terms, each term being the product of a spatial and a spectral part. Based on this assumption, several quantities depending on the reflected light can be computed that are spatially invariant within object boundaries. For an image field either from two light sources on a matte surface or from a single light source on a dielectric surface with highlights, the invariants are the components of the unit normal to the plane in color space spanned by the pixels from the object. In some limited cases the normal to the plane can be used to estimate spectral-reflectance parameters of the object. However, in general the connection of color-constancy theories with image segmentation by object color is a difficult problem. The concomitant constraints on segmentation and color-constancy algorithms are discussed in light of this fact.
04 Dec 1990
TL;DR: A novel framework for cooperative robust estimation is used to estimate descriptions that locally provide the most savings in encoding an image and a modified Hopfield-Tank networks finds the subset of these descriptions which best describes an entire scene.
Abstract: The authors formulate the segmentation task as a search for a set of descriptions which minimally encodes a scene. A novel framework for cooperative robust estimation is used to estimate descriptions that locally provide the most savings in encoding an image. A modified Hopfield-Tank networks finds the subset of these descriptions which best describes an entire scene, accounting for occlusion and transparent overlap among individual descriptions. Using a part-based 3-D shape model the authors have implemented a system that is able to successfully segment images into their constituent structure. >
16 Jun 1990
TL;DR: The authors use the dichromatic model for dielectric materials and develop a metric space of intensity, hue, and saturation in order to interpret various light-surface interactions better.
Abstract: A computational model for color image segmentation is proposed based on the physical properties of sensors, illumination lights, and surface reflectances. For image segmentation to depend only on the change of material surface, variations of surface reflection due to illumination, shading, shadows, and highlights should be discounted from a measured image. The authors use the dichromatic model for dielectric materials and develop a metric space of intensity, hue, and saturation in order to interpret various light-surface interactions better. Using the established model for surface reflection, the authors perform color image segmentation based on the material change with detection of highlights and small interreflections between adjacent objects. A reference plate is used to whiten global illumination. The detected interreflections represent local variation of illumination. >
Patent•
30 Mar 1990TL;DR: In this paper, an adaptive segmentation system that utilizes a genetic algorithm in image segmentation is presented, which can adapt to changes appearing in the images being segmented, caused by variations of such factors as time and weather.
Abstract: An adaptive segmentation system that utilizes a genetic algorithm in image segmentation. The system incorporates a closed-loop feedback mechanism in the segmentation/learning cycle. The system can adapt to changes appearing in the images being segmented, caused by variations of such factors as time and weather. Adaptation is achieved with a measure based on differences of analyzed past imagery and current imagery and on the criteria for segmentation quality. The invention is not dependent on any particular segmentation algorithm or specific sensor type.