The Experts below are selected from a list of 1491 Experts worldwide ranked by ideXlab platform
Ji Qi - One of the best experts on this subject based on the ideXlab platform.
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a robust content based digital image watermarking scheme
Signal Processing, 2007Co-Authors: Xiaojun Qi, Ji QiAbstract:This paper presents a content-based digital image-watermarking scheme, which is robust against a variety of common image-processing attacks and geometric distortions. The image content is represented by important feature points obtained by our image-texture-based adaptive Harris corner detector. These important feature points are geometrically significant and therefore are capable of determining the possible geometric attacks with the aid of the Delaunay-tessellation-based triangle matching method. The watermark is encoded by both the error correcting codes and the spread spectrum technique to improve the detection accuracy and ensure a large measure of security against unintentional or intentional attacks. An image-content-based adaptive embedding scheme is applied in discrete Fourier transform (DFT) domain of each perceptually high textured subimage to ensure better visual quality and more robustness. The watermark detection decision is based on the number of matched bits between the recovered and embedded watermarks in embedding Subimages. The experimental results demonstrate the robustness of the proposed method against any combination of the geometric distortions and various common image-processing operations such as JPEG compression, filtering, enhancement, and quantization. Our proposed system also yields a better performance as compared with some peer systems in the literature.
Wai C. Chu - One of the best experts on this subject based on the ideXlab platform.
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DCT-based image watermarking using subsampling
IEEE Transactions on Multimedia, 2003Co-Authors: Wai C. ChuAbstract:A DCT-based image watermarking algorithm is described, where the original image is not required for watermark recovery, and is achieved by inserting the watermark in Subimages obtained through subsampling.
Xiaojun Qi - One of the best experts on this subject based on the ideXlab platform.
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a robust content based digital image watermarking scheme
Signal Processing, 2007Co-Authors: Xiaojun Qi, Ji QiAbstract:This paper presents a content-based digital image-watermarking scheme, which is robust against a variety of common image-processing attacks and geometric distortions. The image content is represented by important feature points obtained by our image-texture-based adaptive Harris corner detector. These important feature points are geometrically significant and therefore are capable of determining the possible geometric attacks with the aid of the Delaunay-tessellation-based triangle matching method. The watermark is encoded by both the error correcting codes and the spread spectrum technique to improve the detection accuracy and ensure a large measure of security against unintentional or intentional attacks. An image-content-based adaptive embedding scheme is applied in discrete Fourier transform (DFT) domain of each perceptually high textured subimage to ensure better visual quality and more robustness. The watermark detection decision is based on the number of matched bits between the recovered and embedded watermarks in embedding Subimages. The experimental results demonstrate the robustness of the proposed method against any combination of the geometric distortions and various common image-processing operations such as JPEG compression, filtering, enhancement, and quantization. Our proposed system also yields a better performance as compared with some peer systems in the literature.
Dipak Kumar Basu - One of the best experts on this subject based on the ideXlab platform.
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Research Article A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern
2016Co-Authors: Debotosh Bhattacharjee, Ayan Seal, Suranjan Ganguly, Mita Nasipuri, Dipak Kumar BasuAbstract:Copyright © 2012 Debotosh Bhattacharjee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands Subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of Subimages, each of size 8 × 8 pixels. For each such Subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database. 1
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comparative study of human thermal face recognition based on haar wavelet transform and local binary pattern
Computational Intelligence and Neuroscience, 2012Co-Authors: Debotosh Bhattacharjee, Ayan Seal, Suranjan Ganguly, Mita Nasipuri, Dipak Kumar BasuAbstract:Thermal infrared (IR) images focus on changes of temperature distribution on facialmuscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands Subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of Subimages, each of size 8 × 8 pixels. For each such Subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database.
G Calvagno - One of the best experts on this subject based on the ideXlab platform.
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image coding by block prediction of multiresolution Subimages
IEEE Transactions on Image Processing, 1995Co-Authors: R Rinaldo, G CalvagnoAbstract:The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG. >
