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Oriented filters for feature extraction in digital Images : Application to corners detection, Contours evaluation and color Steganalysis

Abstract : Interpretation of image contents is very important objective in image processing and computer vision. Wherefore, it has received much attention of researchers. An image contains a lot of information which can be studied such as color, shapes, edges, corners, size, and orientation. Moreover, contours include the most important structures in the image. In order to extract features contour of an object, we must detect the edges of that object. Edge detection results, remains a key point and very important step in wide range of applications such as: image restoration, enhancement, steganography, watermarking, image retrieval, recognition, compression, and etc. An efficient boundary detection method should create a contour image containing edges at their correct locations with a minimum of misclassified pixels. However, the performance evaluationof the edge detection results is still a challenging problem. The digital images are sometimes modify by a legal or illegal data in order to send special or secret data. These changes modify slight coefficient values of the image. In order to be less visible, most of the steganography methods modify the pixel values in the edge/texture image areas. Therefore, it is important to detect the presence of hidden data in digital images. This thesis is divided mainly into two main parts. The first part, deals with filtering edge detection, contours evaluation and corners detection methods. More deeply, there are five contributions are presented in this part: first, proposed a new normalized supervised edge map quality measure. The strategy to normalize the evaluation enables to consider a score close to 0 as a good edge map, whereas a score 1 translates a poor segmentation. Second, proposed a new technique to evaluate filtering edge detection methods involving the minimum score of the considerate measures. Moreover, build a new ground truth edge map labelled in semi-automatic way in real images. Third, proposed a new measure takes into account the distances of false positive points to evaluate an edge detector in an objective way. Finally, proposed a new approach for corner detection based on the combination of directional derivative and homogeneity kernels. The proposed approach remains more stable and robust to noise than ten famous corner detection methods. The second part, deals with color image steganalysis, based on a machine learning classification. More deeply, there are three contributionsare presented in this part: first, proposed a new color image steganalysis method based on extract color features from correlations between the gradients of red, green and blue channels. Since these features give the cosine of angles between gradients. Second, proposed a new color steganalysis method based on geometric measures obtained by the sine and cosine of gradient angles between all the color channels. Finally, proposed a new approach for color image steganalysisbased on steerable Gaussian filters Bank.All the three proposed methods in this part, provide interesting and promising results by outperforming the state-of-art color image steganalysis.
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Submitted on : Friday, December 7, 2018 - 4:43:10 PM
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  • HAL Id : tel-01948473, version 1



Hasan Abdulrahman. Oriented filters for feature extraction in digital Images : Application to corners detection, Contours evaluation and color Steganalysis. Image Processing [eess.IV]. Université Montpellier, 2017. English. ⟨NNT : 2017MONTS077⟩. ⟨tel-01948473⟩



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