Specific features for the analysis of fringe images

Abstract

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

An Image Content Description Technique for the Inspection of Specular Objects

“…Classification rates after the selection of the most relevant features by means of a filter-based CFS method CFS c S,F and a wrapper-based 1-NN method high relevance in terms of stripe pattern characterization. In particular, for the "number of elements" c S (18) and c S (19) stripe features and the Fourier directional c F θ (5) and horizontal c F u (4) features. The goodness of a feature selection process can be evaluated not only by means of the reached classification rates, but also in terms of the number of relevant features.…”

“…As far as the 12 remaining operators whose computation relies on adapted image areas are concerned, the most appropriate size of each area must be defined according to the stripe structures needed to be characterized. Extensive tests have been conducted in this area and are described previously [18]. These investigations show that an optimal set of image areas can be defined.…”

“…Such an optimal set is noted a 1 . In order to validate the tests described in [18], a further "nonoptimal" set a 2 is taken into consideration. In terms of the adapted image areas, the values of set a 2 are defined as the complementary values of a 1 .…”

“…The former are defined within the field of textural analysis and consist of a general approach, the latter are specially defined for the characterization of such structures. This paper is therefore a generalization of the bottom-up method described in [18]. The classification of specular surfaces is addressed by means of fewer stripe features as proposed in this manuscript.…”

Defect detection using windowed Fourier spectrum analysis in diffraction phase microscopy

“…For identifying defects from fringe patterns, fringe tracking and contouring based methods [6,7] were proposed, but they involve tedious and error-prone scanning and iterative operations. Another popular approach has been to use pattern classification [8][9][10] and neural networks for fault detection [11]. However, these methods require extensive training sets and prior data obtained from fringe patterns with different values of defect, noise and loading parameters which limits the performance and practical feasibility of these methods.…”