In 1972, Barnea and Silverman presented a new approach to the wide field of template matching, the SSD-algorithm. Further work has been done to adapt the method to gain subpixel accuracy. Intense investigation of the proposed algorithms led to our new approach: by interpolating the template instead of the reference image, and by applying sort of an error-correction to the resulting subpixel-value, both computation time and accuracy can be improved. Exhaustive experiments with a CCD-camera and various kinds of reference images showed that a maximum error of 10% of the pixel period can be expected. Depending on the kind of image, mean square errors range from 0.4% to 4%.
Abstract. In this paper we present a new approach for color texture classification which extends the gray level sum-and difference histogram features [8]. Intra-and inter-plane second order features capture the spatial correlations between color bands. A powerful set of features is obtained by non-linear color space conversion to HSV and thresholding operation to eliminate the influence of sensor noise on color information. We present an evaluation of classification performance using four different image sets.
In optical nondestructive testing, a novel solution is presented for fault detection based on the interpretation of fringe images. These images can be acquired using different optical methods, such as structured lighting or interferometry. We propose a set of eight special features adapted to the problem of surface inspection using structured illumination. These characteristics are combined with six further features specially developed for the classification of faults using interferometric images. We apply two kinds of decision rules: the Bayesian and the nearest neighbor classifiers. The proposed features are evaluated using a noisy and a noise-free image data set. All patterns were obtained by means of structured lighting. Concerning the noisy data set, we obtain better classification rates when all the 14 features are used in combination with a one-nearest-neighbor classifier. In case of a noise-free data set, we show that similar classification rates are obtained when the 14 features or only the 8 specific features are involved. The methods described are designed to address a broad range of optical nondestructive applications involving the interpretation and classification of fringe patterns
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