We propose a learning-based approach for automatic detection of fabric defects. Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT). The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics. In addition to being compact and fast to compute, these signatures enable accurate localization of defects. Our defect detection system is based on three main steps. In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation. In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics. Finally, defects are detected during image inspection by testing local patches using the learned BC. Our approach can deal with multiple types of textile fabrics, from simple to more complex ones. Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods.

In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions ( MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT ( SCT) and redundant CT ( RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods.

The achievement of multimedia content authentication by means of digital watermarking, while never easy, is further complicated by the continuing investigation of different ways of generating authentication signatures which survive specific acceptable manipulations. In this paper, we present a novel approach which takes advantage of a multiscale framework and directionality to extract the significant features of an image from its redundant contourlet transform. The introduced redundancy brings simplicity and accuracy for feature calculation. We also describe the applied postprocessing steps with the aim of stabilizing those features under acceptable image manipulations, namely lossy JPEG compression and additive noise corruption. Many experiments and comparative studies are performed to show the effectiveness of our technique in generating content signatures based on invariant image features, as well as to demonstrate its superiority when compared with a redundant wavelet approach

We are interested in the adaptive watermarking approaches and in directional multiresolution image transforms. In this paper, a novel watermarking technique based on a redundant contourlet transform is presented. In contrast to the wavelet transform widely used for image watermarking, to our best knowledge, this is the first time contourlets are applied to this field. The goal of this work is to investigate whether the contourlet with their extra feature of directionality and the explicit introduction of redundancy would provide any significant advantages over the wavelets in terms of watermark robustness and invisibility. Our approach uses directional subbands to generate weighing masks that identify significant image features. The redundancy being introduced brings simplicity and accuracy for mask processing. To prove the validity of our approach, we give experimental results, compare our algorithm to a wavelet-based adaptive watermarking technique and assess watermarking performance in terms of robustness and invisibility.

We have investigated an original motion estimation method that exploits several frequency subbands of wavelet pyramids using a multigrid-multiconstraint strategy. A recursive and iterative solution based on the Wiener approach allows to take into account the reconstruction quality criterion that is crucial to image coding. Experiments show its performances in terms of compression ratio, reconstruction quality, and reduction of implementation complexity compared to a monoresolution case.

Implementation of a set of pyramid transforms in a hierarchical approach for a real television sequence which consists of a 3-D data information set is presented. The investigation is based on several visual and objective comparative criteria, including the relevance to the motion information. Several pyramid transforms for image sequence coding are investigated and compared. The nonorthogonal pyramid transforms are separated from the orthogonal transforms. The nonorthogonal pyramid transforms are easily implemented, permit exact reconstruction, and accept the introduction of an error-compensation method if the different levels are coded but the nonorthogonality of the decomposition causes an increase in the number of transform samples and a high global entropy value (a disadvantage for data compression). The wavelet pyramid transform is more computationally complex but gives the best results for global entropy values. The quincunx pyramid seems to be the most interesting, since it features many advantages of the other including the visual representation of motion information through a pyramid sequence.>