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Showing papers on "Image segmentation published in 1978"


Journal ArticleDOI
TL;DR: This paper presents a review of a variety of techniques for automatic threshold selection, including global, local, and dynamic methods, which have been proposed for automatic thresholds selection in image segmentation.

565 citations


Journal ArticleDOI
TL;DR: A general segmentation method which can be applied to many different types of scenes and the potential performance of other segmentation techniques on general scenes is discussed.

540 citations


Journal ArticleDOI
01 Apr 1978
TL;DR: An overview of present computer techniques of partitioning continuous-tone images into meaningful segments and of characterizing these segments by sets of "features" is presented.
Abstract: An overview of present computer techniques of partitioning continuous-tone images into meaningful segments and of characterizing these segments by sets of "features" is presented. Segmentation often consists of two methods:boundary detection and texture analysis. Both of these are discussed. The design of the segmenter and feature extractor are intimately related to the design of the rest of the image analysis system?particularly the preprocessor and the classifier. Toward aiding this design, a few guidelines and illustrative examples are included.

265 citations


Journal ArticleDOI
01 Aug 1978
TL;DR: The problem of threshold evaluation is addressed, and two methods are proposed for measuring the "goodness" of a thresholded image, one based on a busyness criterion and the otherbased on a discrepancy or error criterion.
Abstract: Threshold selection techniques have been used as a basic tool in image segmentation, but little work has been done on the problem of evaluating a threshold of an image. The problem of threshold evaluation is addressed, and two methods are proposed for measuring the "goodness" of a thresholded image, one based on a busyness criterion and the other based on a discrepancy or error criterion. These evaluation techniques are applied to a set of infrared images and are shown to be useful in facilitating threshold selection. In fact, both methods usually result in similar or identical thresholds which yield good segmentations of the images.

234 citations


Journal ArticleDOI
Panda1, Rosenfeld
TL;DR: A two-dimensional feature set consisting of gray level and edge value is used and the structure of this feature space is analyzed, and several plausible decision surfaces are suggested.
Abstract: Image segmentation can be treated as a pixel classification problem. This classification may be done by measuring a set of features at each point and defining a decision surface in the feature space. This method is commonly used in multispectral images, but there is no obvious choice for the feature set in the case of monochromatic images. Ihis paper makes use of a two-dimensional feature set consisting of gray level and edge value. Ihe structure of this feature space is analyzed, and several plausible decision surfaces are suggested. These are compared experimentally on a set of FLIR test images.

96 citations


Journal ArticleDOI
TL;DR: Applications of iterative methods in image analysis, which include histogram modification, noise cleaning, edge and curve detection, thinning, angle detection, template matching, and region labelling, are described.

91 citations


Journal ArticleDOI
TL;DR: The author empirically illustrates how to identify penetrable market segments in terms of image-direct measurement influences and discusses the implications of this type of information in forming a realistic product positioning strategy.
Abstract: Product positioning studies for both new and established products generally involve consumers’ perception of the current product space in terms of the salient product attributes. Perceptions per br...

84 citations


Journal ArticleDOI
05 Oct 1978-Nature
TL;DR: Experimental results on human texture discrimination are reported which argue against the possibility that textures which differ markedly in their Fourier spectrum are not always readily discriminable.
Abstract: NEUROPHYSIOLOGICAL experiments have shown that many cells in the visual cortex of the cat and monkey respond well only if the retinal image contains a line stimulus of appropriate orientation and width (spatial frequency)1,2. Different cells are sensitive to different orientation/spatial frequency combinations and judging from numerous corroborative psychophysical experiments, it seems highly probable that similar populations of cells exist in man also3. However, the function of these cells is not known. One suggestion is that they decompose the retinal image into its two-dimensional Fourier components and that processes such as object recognition, region finding and image segmentation then operate on this Fourier description4–7. We report here experimental results on human texture discrimination which argue against this possibility by showing that textures which differ markedly in their Fourier spectrum are not always readily discriminable.

31 citations


Journal ArticleDOI
TL;DR: An image segmentation technique is proposed which uses a texture measure that counts the number of local extrema in a window centered at each picture point to derive number of segments in which to divide the original image.

23 citations


Journal ArticleDOI
01 Oct 1978
TL;DR: Application of the algorithm to the problem of classifying 107 windows of SMS-1 infrared satellite data resulted in a classification accuracy of approximately 95 percent.
Abstract: Cloud-type classification is an important component of meteorological and hydrological programs which require estimates of parameters such as solar radiation, rainfall, moisture, and sea-surface temperature. The accuracy of automatic cloud-type classification systems has been limited by ambiguities in multispectral cloud type signatures. Attempts to resolve these ambiguities by the addition of textural features to brightness and temperature features have not produced a significant reduction in misclassification errors. The algorithm described presents a cloud-type classification system which resolves ambiguities in infrared cloud-type signatures by a comparison of textural measures on known and unknown cloud-type segments. The algorithm consists of two parts: 1) the segmentation procedure and 2) the classification procedure. The segmentation part of the algorithm provides a generalized method for partitioning a window of image data into objects characterized by nonoverlapping intervals of gray-level values. The classification part of the algorithm is problem specific, i.e., selection of window and segment features and decision rules will vary with the application. Application of the algorithm to the problem of classifying 107 windows of SMS-1 infrared satellite data resulted in a classification accuracy of approximately 95 percent.

21 citations


Journal ArticleDOI
TL;DR: Properties of the extended transform are examined under a statistical model of "grass-like" texture to achieve maximum scattering of the transformed texture points while preserving clustering properties for the sought after curve.
Abstract: The transform method for curve detection is applied to curves against a textured background. The objective is maximum scattering of the transformed texture points while preserving clustering properties for the sought after curve. Properties of the extended transform are examined under a statistical model of "grass-like" texture.

Journal Article
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.

Journal ArticleDOI
Harry Wechsler1
TL;DR: In this paper, a low-level procedure for image segmentation is described, which transforms a digital picture to a matrix of square arrays which can be identified as being either homogeneous or nonhomogenous.

01 May 1978
TL;DR: The programs which will be discussed here transform a large spatial array of pixels (picture elements) into a more compact representation through the exploitation of visual features, e.g., intensity, color, texture, etc.
Abstract: : The focus of this paper is on image segmentation processes, collectively referred to as a 'low-level' vision system. The programs which will be discussed here transform a large spatial array of pixels (picture elements) into a more compact representation through the exploitation of visual features, e.g., intensity, color, texture, etc. The goal is to detect a relative feature invariance across an area of the image and then to label all the pixels in any such area as belonging to the same region. Regions can be detected through global analyses (e.g., histogram clustering) which find interesting areas by ignoring the local textural configurations of the data, in conjunction with local anlayses (e.g., relaxation) which act as a fine-tuning mechanisms both to resolve global ambiguities and to accurately delimit region boundaries. (Author)


Proceedings ArticleDOI
Raj Aggarwal1
31 Aug 1978
TL;DR: An image segmentation technique using prototype similarity is described here and some a priori information about the scene is used to infer meaning of each cell in this symbolic image.
Abstract: An image segmentation technique using prototype similarity is described here. The prototype similarity is a method for transforming an attribute image into a set of symbols, each of which represents the relationship of a local region to other parts of the image. It consists of two main steps: (1) prototype generation and (2) inference. Generating prototypes is equivalent to finding a maximal set of mutually dissimilar regions using a similarity relation. A similarity relation is a symmetric, reflexive binary relation and not an equivalence relation. It is not bound by metric properties. The generated set of prototypes is used to transform the attribute image into a symbolic image. Some a priori information about the scene is used to infer meaning of each cell in this symbolic image. The segmentation results of this technique on FLIR images are included.