One of the methods which can produce powerful features for texture classification is Local Binary Patterns, LBP. In this paper we propose a method for defect detection in textile fabrics using these features. In the training stage, at first step LBP operator is applied More
One of the methods which can produce powerful features for texture classification is Local Binary Patterns, LBP. In this paper we propose a method for defect detection in textile fabrics using these features. In the training stage, at first step LBP operator is applied to an image of defect free fabric, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied on each of these windows. Based on comparison to the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is gray scale and shift invariant and can be used for defect detection in patterned and plain fabrics. Due to its simplicity online implementation is possible.
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In many of detection problems the received signals models under two hypotheses, H0 and H1, are the same except that some model parameters have fixed value under H0. These models are so called Nested Models. One of the most important examples is detection of a target wit More
In many of detection problems the received signals models under two hypotheses, H0 and H1, are the same except that some model parameters have fixed value under H0. These models are so called Nested Models. One of the most important examples is detection of a target with unknown amplitude in the clutter. In this problem, one can assume similar models for received signals under H0 and H1 unless the target amplitude is assumed to be zero under H0. If the Bayesian approach used for treating unknown parameters, it can be shown that the likelihood ratio can be calculated as the ratio of the posterior and the prior probability of unknown parameters. Using this method a new detector for detection in Gaussian clutter is presented in this paper. Simulation results show that the proposed detector has much better performance compared with conventional GLRT detectors. It is also shown that a CFAR property is achieved provided that a small modifications in decision rule.
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Multicarrier phase coded signals have been recently introduced to achieve high range resolution in radar systems. As single carrier phase coded radars, the common method for compression of these signals, is using matched filter or computing the auto correlation function More
Multicarrier phase coded signals have been recently introduced to achieve high range resolution in radar systems. As single carrier phase coded radars, the common method for compression of these signals, is using matched filter or computing the auto correlation function directly. In this paper we propose a new method based on fast Fourier transform (FFT) with lower computational load with respect to traditional approach. Furthermore, based on this new approach, a method for estimation of communication channel is introduced that can be used for improving detection performance and target position estimation in tracking mode.
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