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)

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.

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.

This correspondence describes an improved method of detecting "angles"—i.e., maxima of the difference between successive nonoverlapping average slopes—on a digital curve. The method is similar to, but gives better results than, a method described in an earlier paper.

Regions in a picture that differ texturally from their surroundings can often be extracted by 1) applying a local operation to every point of the picture, 2) averaging the results, and 3) thresholding. This approach will usually not work if there are too many differently textured regions present, since the average for one type of region may then be indistinguishable from the average resulting from a mixture of two other types that adjoin. The amount of averaging that should be used depends on the coarseness of the texture. An approach to choosing this amount automatically was investigated, but the results were not as good as those obtained with a fixed high degree of averaging.

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.