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Showing papers on "Segmentation-based object categorization published in 1984"


Journal ArticleDOI
TL;DR: A new solution to the image segmentation problem that is based on the design of a rule-based expert system that dynamically alters the processing strategy is presented.
Abstract: A major problem in robotic vision is the segmentation of images of natural scenes in order to understand their content. This paper presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include metarules that embody inferences about the order in which the knowledge rules are matched. They also incorporate focus of attention rules that determine the path of processing within the image. Furthermore, an additional set of higher level rules dynamically alters the processing strategy. This paper discusses the structure and content of the knowledge and control rules for image segmentation.

380 citations


Journal ArticleDOI
TL;DR: This method does not rely on the existence of modes on the histogram, and the number of free parameters is reduced, which makes this algorithm essentially automatic and not time consuming.
Abstract: A method for image segmentation and compression based on the intrinsic properties of the distribution function of an image is presented. This method does not rely on the existence of modes on the histogram. The number of free parameters is reduced, which makes this algorithm essentially automatic and not time consuming.

97 citations


Journal ArticleDOI
TL;DR: An iterative segmentation method is presented and illustrated on specific examples by combining local and global properties according to a model of the image structure to evaluate adequacy of segmentation.
Abstract: An iterative segmentation method is presented and illustrated on specific examples. Full control of each iteration step is obtained by combining local and global properties according to a model of the image structure. A consistent convergence criterion is derived from additional image structure properties and a test is proposed to evaluate adequacy of segmentation.

90 citations


Journal ArticleDOI
TL;DR: Input of line drawings to computers may be accomplished using digital image processing and pattern recognition methods for automatic digitization and a one-pass algorithm is used for the segmentation of the binary image into primary components.
Abstract: Input of line drawings to computers may be accomplished using digital image processing and pattern recognition methods for automatic digitization. Part of a system for the recognition of electrical schematics is presented. A one-pass algorithm is used for the segmentation of the binary image into primary components. Primary components are simply groups of connected black pixels. The segmentation yields a picture graph representing the binary image. Every node of the graph represents a primary component. Strings of alphanumeric symbols in the drawing are located by computing connected components, i.e., connected subgraphs, and clustering the small connected components. The line elements are computed with two different methods. First, primary components are merged into classified line elements which describe the dominant large lines of the drawing. Second, the details are analyzed within the context of dominant lines using a production system.

62 citations


Proceedings ArticleDOI
19 Mar 1984
TL;DR: Making use of Gibbs distribution models of Markov random fields a dynamic programming based segmentation algorithm is developed and examples are given.
Abstract: This paper presents a new statistical approach to image segmentation. Making use of Gibbs distribution models of Markov random fields a dynamic programming based segmentation algorithm is developed. The algorithm is described in detail and examples are given.

61 citations


Journal ArticleDOI
01 May 1984
TL;DR: An image segmentation algorithm is described that uses an overlapped pyramid to represent an image at multiple levels of resolution to create a forest embedded within the pyramid.
Abstract: An image segmentation algorithm is described that uses an overlapped pyramid to represent an image at multiple levels of resolution. The procedure `lifts' objects to levels of lower and lower resolution until they become `spot' or `streaklike' and are identifiable by local processing (using 3 by 3 operators). They are then `rooted.' The result is a forest embedded within the pyramid, with the single tree rooted at the pyramid apex representing the background regions and the remaining trees representing compact object regions. In addition to the definition of the pyramid linking algorithm, the convergence of the algorithm is proved and optimal rooting rules for binary images are analyzed.

28 citations


Patent
Yoshitake C1, Tsuji Yoshitake1, Ko C, Asai Ko
20 Dec 1984
TL;DR: In this paper, a character sectioning apparatus for segmenting character stream images into individual characters utilizes the projection distribution obtained through a series of character strain images, which includes means for identifying the character lumps and blank spaces from the projected distribution, means for estimating the character pitch, means responsive to the projected projection distribution and character pitch for setting segmentation candidate sections each having a plurality of segmentation position positions.
Abstract: A character sectioning apparatus for segmenting character stream images into individual characters utilizes the projection distribution obtained through a series of character strain images. The apparatus includes means for identifying the character lumps and blank spaces from the projection distribution, means for estimating the character pitch, means responsive to the projection distribution and the character pitch for setting segmentation candidate sections each having a plurality of segmentation candidate positions, means for calculating a distance measurement between each of the segmentation candidate positions within the segmentation candidate sections and segmentation candidate positions within adjacent segmentation candidate sections, means for calculation an appreciation standard and means for selecting an optimum segmentation candidate for each of the plurality of segmentation candidate sections. The appreciation standard is selected as a function of the distance measurements for segmentation candidate positions in each of the plurality of segmentation candidate sections. Each optimum segmentation candidate position minimizes the appreciation standard for the respective segmentation candidate section.

24 citations


Proceedings ArticleDOI
06 Feb 1984
TL;DR: Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data is modelled as one of C non-causal 2-D Markovian Stochastic Processes, designed to operate in real time when implemented on new parallel computer architectures.
Abstract: Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data is modelled as one of C non-causal 2-D Markovian Stochastic Processes. The algorithms are designed to operate in real time when implemented on new parallel computer architectures. A doubly stochastic representation is used in image modelling. Here, an auto-normal (Gaussian) process is used to model textures in visible light and infrared images, and an auto-binary field is used to model apriori information about local image geometry. Image segmentation is realized as true maximum likelihood estimation. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region. The second segmentation algorithm is a relaxation-type algorithm that arises naturally within the context of these non-causal Markovian Processes. It is a simple, true maximum likelihood estimator. The algorithms can be used separately or together. These issues and subtleties concerning the use of the Markovian processes are discussed.© (1984) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

15 citations


Journal ArticleDOI
TL;DR: A set of rules for segmenting images was selected based on a quantitative evaluation of performance using a rule-based system and the results of their application were summarized.

15 citations


Journal ArticleDOI
TL;DR: The maximum number of trees in a forest derived from a quadtree that represents a square of dimension 2k X 2k is 4k-1, i.e., one-fourth the area of the square.
Abstract: Theorem: The maximum number of trees in a forest derived from a quadtree that represents a square of dimension 2k X 2k is 4k-1, i.e., one-fourth the area of the square. Given a forest of quadtrees F, we can easily show how to reconstruct a quadtree. The reconstructed quadtree R(F) consists of real nodes (nodes in the forest) and virtual nodes (nodes that correspond to BAD nodes deleted while creating the forest). Since virtual nodes require no storage they are located by giving their coordinates. We denote the virtual node with coordinates (L, K) by v(L, K). The root of R(F) is either a real node (if the forest has one tree) or the virtual node v(l, 0). In either case we know its location and color in R(F). The offspring of any real node are found by following links. If v(L, K) is any virtual node, then its children in directions D & {NW, NE, SW, SE} have coordinates (L + 1, 4K + D), respectively. It is a simple matter of a table lookup to see if the offspring are real nodes or virtual nodes. Also, we can easily determine the color of a virtual node. It is GRAY if it has a descendant(s) in the table and WHITE otherwise. The check to see if the node has a descendant in the table may be performed efficiently if the table is stored in the left-to-right order produced by FOREST so we can apply the numerical test at the end of Section I. For example, we can easily establish that the virtual nodes v(3, 10) and v(3, 11) of the forest of Fig. 4 are WHITE because they lie between (in left-to-right order) the successive elements (3, 9) and (4, 48) in the table. Since the elements of the table are linearly ordered by this left-to-right order, we may perform a binary search to check on the color of a virtual node. If T represents a picture in a 2n X 2n grid, then such a search requires time 0(1og(number of trees in forest)) = 0(1og 4nl1) (by the theorem) = 0(n). For a real number x, let floor(x) denote the greatest integer less than or equal to x. The father of a virtual node v(L, K) is the virtual node v(L-1, floor(K/4)). If P is a real node, then either its father is the real node given by …

11 citations


Proceedings ArticleDOI
Yoshitake Tsuji1, Ko Asai1
04 Dec 1984
TL;DR: Two new methods for character segmentation under more general conditions based on least square error function and a dynamic programing method with the minimum variance for separation between candidate positions in a line image are described.
Abstract: In the Optical Character Reader (OCR) system design, the character segmentation technique is important. For example, the Automatic Mail Address Reader is required to manage printed characters of many font types and poor print quality. In this case, OCR performance will be affected by character segmentation technique. This paper describes two new methods for character segmentation under more general conditions. The character segmentation problem can be formulated and classified as a pitch estimation problem and a character sectioning decision problem. These problems are resolved by using a statistical analysis method based on least square error function and a dynamic programing method with the minimum variance for separation between candidate positions in a line image. The effectiveness of the proposed methods has been evaluated through actual mail address segmentation experiments.

Proceedings ArticleDOI
01 Mar 1984
TL;DR: An algorithm is described which operates on a digitized television frame or digital infrared image to rapidly locate tightly clustered objects which occupy less than half the field of view and which can be enclosed by rectangles.
Abstract: An algorithm is described which operates on a digitized television frame or digital infrared image to rapidly locate tightly clustered objects which occupy less than half the field of view and which can be enclosed by rectangles. The algorithm uses a maximum entropy image and projections in place of arbitrary heuristics to guide the location and segmentation process.

Proceedings ArticleDOI
01 Jan 1984
TL;DR: The problem of subdividing an ocean area into coherent subregions is investigated and the region segmentation applied is the Global-Local Edge Coincidence algorithm (GLEC).
Abstract: The problem of subdividing an ocean area into coherent subregions is investigated. The subdivision is based on the image processing technique of region segmentation. The region segmentation applied is the Global-Local Edge Coincidence algorithm (GLEC). GLEC requires the application of several image processing operations including clustering, gradient analysis, region labelling, and others. The data bases selected for this analysis consist of the Nimbus-7 Satellite CZCS sensor derived phytoplankton concentration and temperature images. The interpretation of the resulting ocean area subdivision is also discussed.

Journal ArticleDOI
TL;DR: A model-based approach to grey-tone image segmentation is presented, in which a variety of image models can be accommodated and some particularly relevant models are described in detail and illustrated by means of experimental results obtained with real-world images.

Proceedings ArticleDOI
01 Mar 1984
TL;DR: The use of spatial linear prediction to detect region borders in aerial photographs and thus segment the image into regions of differing natural terrain is discussed.
Abstract: This paper discusses the use of spatial linear prediction to detect region borders in aerial photographs and thus segment the image into regions of differing natural terrain. The algorithm is motivated by a significance test which under certain assumptions and approximations can be expressed as the weighted sum of error residuals of two-dimensional (2-D) linear prediction. Examples are presented to illustrate the performance of the algorithm.

Journal ArticleDOI
TL;DR: In this paper, a line-oriented method produces hints (hypotheses) at specific objects, and the strength and the type of a hint determines application of a region-oriented procedure or a lineoriented procedure under modified conditions.

Proceedings ArticleDOI
04 Dec 1984
TL;DR: In this paper, a Mealy machine has been designed to remove background nonhomogeneity and a Moore machine was designed to isolate the welding defects from the background, and the results of segmentation of several radiograph images using these finite state machines are given.
Abstract: The problem of image segmentation is approached via finite state machines. Finite state machines are excellent tools in processing various vision tasks. In this paper, segmentation of radiograph images of welding scenes are considered. A Mealy machine has been designed to remove background nonhomogeneity and a Moore machine has been designed to isolate the welding defects from the background. Result of segmentation of several radiograph images using these finite state machines are given.

Journal ArticleDOI
TL;DR: The chosen methods for image segmentation refer to an iterative binarization (level slicing) and to a region growing (local grey level evaluation) algorithm which generate segments characterized by features and attributes which represent a symbolic description and are the basis for the comparison of successive frames.