Showing papers on "Image segmentation published in 1977"

Histogram modification for threshold selection

Abstract: : A standard approach to threshold selection for image segmentation is based on locating valleys in the image's gray level histogram. Several methods have been proposed that produce a transformed histogram in which the valley is deeper, or is converted into a peak, and is thus easier to detect. The transformed histograms used in these methods can all be obtained by creating (gray level, edge value) scatter plots, and computing various weighted projections of these plots on the gray level axis. Using this unified approach makes it easier to understand how the methods work and to predict when a particular method is likely to be effective. The methods are applied to a set of examples involving both real and synthetic images, and the characteristics of the resulting transformed histograms are discussed. (Author)

A New Edge Detection Technique and Its Implementation

Abstract: A new edge detection technique for digital image processing is presented. The technique is viewed as a low-level operator in the context of the more complex problem, that of scene analysis, and a conceptual comparison with some previous edge detectors is done. The new edge detection technique has been implemented and its results are compared with two other edge detection techniques using the same kind of pictures as input data. The software implementation of this new edge detector written in machine language takes 32 s for an image of size 128 by 128 picture elements. The edge detector can be hardware-implemented and such an implementation, for which the estimated processing time will be about half a second, is given in the Appendix.

Image segmentation technique for locating automotive parts on belt conveyors

Abstract: A simple, model free computer vision program to determine the locations of non-overlapping parts on belt conveyors is described. This program illustrates a simple and effective procedure for segmenting objects from background in instances where simple thresholding of a gray-level image does not suffice. The procedure consists of a unique sequence of standard image enhancement processes. The resultant image exhibits silhouettes of the objects, which contain sufficient information for locating those parts whose orientation can be determined without observation of internal features. The technique has been implemented on a large research computer, as well as a mini-computer coupled to a prototype belt conveyor-robot arm part transfer system. The technique has been validated for a large variety of parts and belt surfaces. It can meet production rates and has the potential for actual production use.

Histogram modification for threshold selection

Abstract: : A standard approach to threshold selection for image segmentation is based on locating valleys in the image's gray level histogram. Several methods have been proposed that produce a transformed histogram in which the valley is deeper, or is converted into a peak, and is thus easier to detect. The transformed histograms used in these methods can all be obtained by creating (gray level, edge value) scatter plots, and computing various weighted projections of these plots on the gray level axis. Using this unified approach makes it easier to understand how the methods work and to predict when a particular method is likely to be effective. The methods are applied to a set of examples involving both real and synthetic images, and the characteristics of the resulting transformed histograms are discussed. (Author)

Syntactic Algorithms for Image Segmentation and a Special Computer Architecture for Image Processing

Abstract: : Several efficient algorithms for image recognition and segmentation and a new computer architecture for image processing are proposed. The algorithms are 'Syntactic' in that they perform structural or spatial analysis rather than statistical analysis, and a 'grammer' is inferred for describing the structures of patterns in an image. Depending on the requirements of the problem, an appropriate grammatical approach is used by the syntactic algorithm. A finite-state string grammar is applied to the image recognition of highways, rivers, bridges, and commercial/industrial areas from LANDSAT images. There are two major methods in the string grammar approach for image recognition; namely, the syntax-directed method and syntax-controlled method. For the syntax directed method, syntactic analysis is performed by a template matching which is directed by the syntactic rules. For the syntax - controlled method an automation which is directly controlled by the syntactic rules is used for the syntactic analysis. A tree grammar is applied to the image segmentation of terrain and tactical targets from LANDSAT and infrared images respectively. The tree grammar approach utilizes a tree automation to extract the boundaries of the homogeneous region segments of the image. The homogeneity of the region segment is obtained through texture measurements of the image. The computer architecture proposed is a special purpose system in that it can perform an image processing task on several picture-points of an image at the same time, and thus takes advantage of the fact that image processing tasks usually exhibit 'parallelism'. This architecture uses a distributed computing approach.

Interactive Image Segmentation: Line, Region and Semantic Structure

Abstract: This paper presents a number of ideas concerned with the problem of generalized image segmentation; in particular, the proposal of combining line, region and semantic structure as a powerful approach to a working solution is examined in detail. The interactive system for image processing, pattern recognition and graphics utilized for this work is briefly described. The graph theoretic representation of image segmentation structure is explored and the thorny question of visual texture analysis (in relation to the segmentation problem) is commented on. Some general remarks are made about the role of high level semantics in facilitating meaningful segmentation for images from well known environments, perhaps at the cost of generality. Preliminary implementation results for several fragments of the total proposal are given for a number of simple scenes.