A threshold selection method from gray level histograms

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Data clustering: a review

A comparative study of texture measures with classification based on featured distributions

In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.

Statistical and structural approaches to texture

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

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Survey over image thresholding techniques and quantitative performance evaluation

Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively. Feature sets of these types, all designed analogously, were used to classify two sets of terrain samples. It was found that the Fourier features generally performed more poorly, while the other feature sets all performned comparably.

A Comparative Study of Texture Measures for Terrain Classification

A survey of threshold selection techniques

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.

Threshold Evaluation Techniques

Threshold selection for picture segmentation is relatively easy when the frequency distribution of gray levels in the picture is strongly bimodal, with the two peaks comparable insize and separated by a deep valley. This report describes a method of handling cases in which the peaks are very unequal in size and the valley is broad. A Laplacian operation is applied to the picture to determine points that lie on or near the edges of objects. Threshold selection becomes easier when the frequency distribution of gray levels of these points is used.

A Threshold Selection Technique

: 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)

Joan S. Weszka

Papers

A Comparative Study of Texture Measures for Terrain Classification

A survey of threshold selection techniques

Threshold Evaluation Techniques

A Threshold Selection Technique

Histogram modification for threshold selection