The Experts below are selected from a list of 1545 Experts worldwide ranked by ideXlab platform
George Bebis - One of the best experts on this subject based on the ideXlab platform.
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gender recognition from face images with Dyadic Wavelet Transform and local binary pattern
International Journal on Artificial Intelligence Tools, 2013Co-Authors: Muhammad Hussain, George Bebis, Ghulam Muhammad, Ihsan Ullah, Hatim Aboalsamh, Anwar M MirzaAbstract:Gender recognition from facial images plays an important role in biometric applications. Employing Dyadic Wavelet Transform (DyWT) and Local Binary Pattern (LBP), we propose a new feature descriptor DyWT-LBP for gender recognition. DyWT is a multi-scale image Transformation technique that decomposes an image into a number of sub-bands which separate the features at different scales. DyWT is a kind of translation invariant Wavelet Transform that has a better potential for detection than Discrete Wavelet Transform (DWT). On the other hand, LBP is a texture descriptor and is known to be the best for representing texture micro-patterns, which play a key role in the discrimination of different objects in an image. For DyWT, we used spline Dyadic Wavelets (SDW). There exist many types of SDW; we investigated a number of SDWs for finding the best SDW for gender recognition. The dimension of the feature space generated by DyWT-LBP descriptor becomes excessively high. To tackle this problem, we apply a feature subset selection (FSS) technique that not only reduces the number of features significantly but also improves the recognition accuracy. Through a large number of experiments performed on FERET and Multi-PIE databases, we report for DyWT-LBP descriptor the parameter settings, which result in the best accuracy. The proposed system outperforms the stat of the art gender recognition approaches; it achieved a recognition rate of 99.25% and 99.09% on FERET and Multi-PIE databases, respectively.
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gender recognition from face images with Dyadic Wavelet Transform and local binary pattern
International Symposium on Visual Computing, 2012Co-Authors: Ihsan Ullah, George Bebis, Muhammad Hussain, Ghulam Muhammad, Hatim Aboalsamh, Anwar M MirzaAbstract:Gender recognition from facial images plays an important role in biometric applications. We investigated Dyadic Wavelet Transform (DyWT) and Local Binary Pattern (LBP) for gender recognition in this paper. DyWT is a multi-scale image Transformation technique that decomposes an image into a number of subbands which separate the features at different scales. On the other hand, LBP is a texture descriptor and represents the local information in a better way. Also, DyWT is a kind of translation invariant Wavelet Transform that has better potential for detection than DWT (Discrete Wavelet Transform). Employing both DyWT and LBP, we propose a new technique of face representation that performs better for gender recognition. DyWT is based on spline Wavelets, we investigated a number of spline Wavelets for finding the best spline Wavelets for gender recognition. Through a large number of experiments performed on FERET database, we report the best combination of parameters for DyWT and LBP that results in maximum accuracy. The proposed system outperforms the stat-of-the-art gender recognition approaches; it achieves a recognition rate of 99.25% on FERET database.
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passive copy move image forgery detection using undecimated Dyadic Wavelet Transform
Digital Investigation, 2012Co-Authors: Ghulam Muhammad, Muhammad Hussain, George BebisAbstract:In this paper, a blind copy move image forgery detection method using undecimated Dyadic Wavelet Transform (DyWT) is proposed. DyWT is shift invariant and therefore more suitable than discrete Wavelet Transform (DWT) for data analysis. First, the input image is decomposed into approximation (LL1) and detail (HH1) subbands. Then the LL1 and HH1 subbands are divided into overlapping blocks and the similarity between blocks is calculated. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while that from the HH1 subband should be low due to noise inconsistency in the moved block. Therefore, pairs of blocks are sorted based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, matched pairs are obtained from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.
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Copy-move forgery detection using Dyadic Wavelet Transform
Proceedings - 2011 8th International Conference on Computer Graphics Imaging and Visualization CGIV 2011, 2011Co-Authors: Najah Muhammad, Muhammad Hussain, Ghulam Muhammad, George BebisAbstract:The issue of the verification of the authenticity and integrity of digital images is increasingly being important. Copy move forgery is one type of tempering that is commonly used for manipulating the digital contents, in this case, a part of an image is copied and is pasted on another region of the image. The non-intrusive approach for this problem is becoming attractive because it does not need any embedded information, but it is still far from being satisfactory. In this paper, an efficient non-intrusive method for copy-move forgery detection is presented. This method is based on image segmentation and similarity detection using Dyadic Wavelet Transform (DyWT). Copied and pasted regions are structurally similar and this structural similarity is detected using DyWT and statistical measures. The results show that the proposed method outperforms the stat-of-the-art methods.
Teruya Minamoto - One of the best experts on this subject based on the ideXlab platform.
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gait feature extraction using Dyadic Wavelet Transform and structural similarity for gait recognition
2019Co-Authors: Hajime Omura, Teruya MinamotoAbstract:We propose a new gait feature extraction method for gait recognition that uses the Dyadic Wavelet Transform (DYWT) and structural similarity (SSIM). Gait recognition is a person authentication technique that uses the gait features obtained from a monitoring camera video. Most existing gait recognition methods use gait features based on the silhouette image sequence. However, the gait recognition accuracy decreases if the resolution of the silhouette image is low. We developed a new method to extract gait features from silhouette images having a low-resolution gait period sequence that uses the DYWT and SSIM. For our experiment, we prepared two types of silhouette image sequences of 100 subjects for use as the probe and gallery images, respectively. The sizes of the silhouette images were 64 × 44, 32 × 22, and 16 × 11. We describe our proposed method in detail and present our experimental results demonstrating that the extracted gait features are effective.
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detection method of early esophageal cancer from endoscopic image using Dyadic Wavelet Transform and four layer neural network
2018Co-Authors: Hajime Omura, Teruya MinamotoAbstract:We propose a new detection method of early esophageal cancer from endoscopic image by using the Dyadic Wavelet Transform (DYWT) and the four-layered neural network (NN). We prepare 6500 appropriate training images to make a NN classifier for early esophageal cancer. Each training image is converted into HSV and CIEL*a*b* color spaces, and each fusion image is made from the S (saturation), a* (complementary color), and b* (complementary color) components. The fusion image is enhanced contrast so as to emphasize the difference of the pixel values between the normal and abnormal regions, and we use only high pixel values of this image for learning in the neural network. We can obtain the important image features by applying the inverse DYWT to processed image. We describe our proposed method in detail and present experimental results demonstrating that the detection result of the proposed method is superior to that of the deep learning technique utilizing the endoscopic image marked an early esophageal cancer by a doctor.
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computer aided diagnosis method for detecting early esophageal cancer from endoscopic image by using Dyadic Wavelet Transform and fractal dimension
2016Co-Authors: Ryuji Ohura, Hajime Omura, Yasuhisa Sakata, Teruya MinamotoAbstract:We propose a new computer-aided method for diagnosing early esophageal cancer from endoscopic images by using the Dyadic Wavelet Transform (DYWT) and the fractal dimension. In our method, an input image is converted into HSV color space, and a fusion image is made from the S (saturation) and V (value) components based on the DYWT. We apply the contrast enhancement to produce a grayscale image in which the structure of abnormal regions is enhanced. We can obtain binary images composed of multiple layers by low-gradation processing. We visualize abnormal regions by summing these fractal dimensions by computing the complexity of these images. We describe a process for enhancing, detecting and visualizing abnormal regions in detail, and we present experimental results demonstrating that our method gives visualized images in which abnormal regions in endoscopic images can be located and that contain data useful for actual diagnosis of early esophageal cancer.
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a blind digital image watermarking method based on the Dyadic Wavelet Transform and chaos models
International Conference on Information Technology: New Generations, 2014Co-Authors: Teruya Minamoto, Jumpei YamaguchiAbstract:We present a chaos-based watermarking method for digital images using the Dyadic Wavelet Transform (DYWT) and some chaos models. The values of Wavelet filters are expressed in interval representation in order to use fast interval arithmetic techniques in the embedding procedure. Our watermark is a ternary-valued logo that is embedded into the high-frequency components, and the Rossler system and the Lorentz system are employed as chaos models. To increase the degree of security, we use chaos maps to generate a key, and the watermark image is reprocessed with this key. We describe some experimental results demonstrating that our method gives watermarked images that have better quality and is robust against attacks such as addition of Gaussian white noise, addition of salt a pepper noise, marking, and clipping.
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a blind digital image watermarking method based on the Dyadic Wavelet Transform and interval arithmetic
Applied Mathematics and Computation, 2014Co-Authors: Teruya Minamoto, Ryuji OhuraAbstract:We propose a new blind digital image watermarking method based on the Dyadic Wavelet Transform (DYWT) and interval arithmetic (IA). Because the DYWT has a redundant representation, the amount of information that the watermark must contain is greater than in the case of the methods based on the ordinary discrete Wavelet Transforms. Our watermark is a ternary-valued logo that is embedded into the high-frequency components through use of the DYWT and IA. We describe the properties of the DYWT based on IA (IDYWT) and its computational method. We also describe our watermarking procedure in detail and present experimental results demonstrating that our method produces watermarked images that have better quality and are robust with respect to attacks on the following types: marking, clipping, median filtering, contrast tuning (histeq and imadjust commands in the MATLAB Image Processing Toolbox), addition of Gaussian white noise, addition of salt & pepper noise, JPEG and JPEG2000 compressions, rotation, resizing.
Ezzedine Ben Braiek - One of the best experts on this subject based on the ideXlab platform.
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evaluation of a multi resolution Dyadic Wavelet Transform method for usable speech detection
arXiv: Sound, 2013Co-Authors: Wajdi Ghezaiel, Amel Ben Slimane Rahmouni, Ezzedine Ben BraiekAbstract:Many applications of speech communication and speaker identification suffer from the problem of co-channel speech. This paper deals with a multi-resolution Dyadic Wavelet Transform method for usable segments of co-channel speech detection that could be processed by a speaker identification system. Evaluation of this method is performed on TIMIT database referring to the Target to Interferer Ratio measure. Co-channel speech is constructed by mixing all possible gender speakers. Results do not show much difference for different mixtures. For the overall mixtures 95.76% of usable speech is correctly detected with false alarms of 29.65%.
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evaluation of a multi resolution Dyadic Wavelet Transform method for usable speech detection
International Journal of Electronics and Communication Engineering, 2013Co-Authors: Wajdi Ghezaiel, Amel Ben Slimane Rahmouni, Ezzedine Ben BraiekAbstract:Many applications of speech communication and speaker identification suffer from the problem of co-channel speech. This paper deals with a multi-resolution Dyadic Wavelet Transform method for usable segments of co-channel speech detection that could be processed by a speaker identification system. Evaluation of this method is performed on TIMIT database referring to the Target to Interferer Ratio measure. Co-channel speech is constructed by mixing all possible gender speakers. Results do not show much difference for different mixtures. For the overall mixtures 95.76% of usable speech is correctly detected with false alarms of 29.65%. Keywords—Co-channel speech, usable speech, multi-resolution analysis, speaker identification
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evaluation of a multi resolution Dyadic Wavelet Transform method for usable speech
2011Co-Authors: Wajdi Ghezaiel, Amel Ben Slimane Rahmouni, Ezzedine Ben BraiekAbstract:Many applications of speech communication and speaker identification suffer from the problem of co-channel speech. This paper deals with a multi-resolution Dyadic Wavelet Transform method for usable segments of co-channel speech detection that could be processed by a speaker identification system. Evaluation of this method is performed on TIMIT database referring to the Target to Interferer Ratio measure. Co-channel speech is constructed by mixing all possible gender speakers. Results do not show much difference for different mixtures. For the overall mixtures 95.76% of usable speech is correctly detected with false alarms of 29.65%. Keywords—Co-channel speech, usable speech, multi-resolution analysis, speaker identification
Muhammad Hussain - One of the best experts on this subject based on the ideXlab platform.
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mammogram enhancement using lifting Dyadic Wavelet Transform and normalized tsallis entropy
Journal of Computer Science and Technology, 2014Co-Authors: Muhammad HussainAbstract:In this paper, we present a new technique for mammogram enhancement using fast Dyadic Wavelet Transform (FDyWT) based on lifted spline Dyadic Wavelets and normalized Tsallis entropy. First, a mammogram image is decomposed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the Wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law Transformation to suppress background. Though FDyWT is shift-invariant and has better potential for detecting singularities like edges, its performance depends on the choice of Dyadic Wavelets. On the other hand, the number of vanishing moments is an important characteristic of Dyadic Wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting Dyadic schemes, we construct lifted spline Dyadic Wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these Wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS (Mammographic Image Analysis Society) database, having various background tissue types and containing different abnormalities. The comparison with the state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.
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gender recognition from face images with Dyadic Wavelet Transform and local binary pattern
International Journal on Artificial Intelligence Tools, 2013Co-Authors: Muhammad Hussain, George Bebis, Ghulam Muhammad, Ihsan Ullah, Hatim Aboalsamh, Anwar M MirzaAbstract:Gender recognition from facial images plays an important role in biometric applications. Employing Dyadic Wavelet Transform (DyWT) and Local Binary Pattern (LBP), we propose a new feature descriptor DyWT-LBP for gender recognition. DyWT is a multi-scale image Transformation technique that decomposes an image into a number of sub-bands which separate the features at different scales. DyWT is a kind of translation invariant Wavelet Transform that has a better potential for detection than Discrete Wavelet Transform (DWT). On the other hand, LBP is a texture descriptor and is known to be the best for representing texture micro-patterns, which play a key role in the discrimination of different objects in an image. For DyWT, we used spline Dyadic Wavelets (SDW). There exist many types of SDW; we investigated a number of SDWs for finding the best SDW for gender recognition. The dimension of the feature space generated by DyWT-LBP descriptor becomes excessively high. To tackle this problem, we apply a feature subset selection (FSS) technique that not only reduces the number of features significantly but also improves the recognition accuracy. Through a large number of experiments performed on FERET and Multi-PIE databases, we report for DyWT-LBP descriptor the parameter settings, which result in the best accuracy. The proposed system outperforms the stat of the art gender recognition approaches; it achieved a recognition rate of 99.25% and 99.09% on FERET and Multi-PIE databases, respectively.
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gender recognition from face images with Dyadic Wavelet Transform and local binary pattern
International Symposium on Visual Computing, 2012Co-Authors: Ihsan Ullah, George Bebis, Muhammad Hussain, Ghulam Muhammad, Hatim Aboalsamh, Anwar M MirzaAbstract:Gender recognition from facial images plays an important role in biometric applications. We investigated Dyadic Wavelet Transform (DyWT) and Local Binary Pattern (LBP) for gender recognition in this paper. DyWT is a multi-scale image Transformation technique that decomposes an image into a number of subbands which separate the features at different scales. On the other hand, LBP is a texture descriptor and represents the local information in a better way. Also, DyWT is a kind of translation invariant Wavelet Transform that has better potential for detection than DWT (Discrete Wavelet Transform). Employing both DyWT and LBP, we propose a new technique of face representation that performs better for gender recognition. DyWT is based on spline Wavelets, we investigated a number of spline Wavelets for finding the best spline Wavelets for gender recognition. Through a large number of experiments performed on FERET database, we report the best combination of parameters for DyWT and LBP that results in maximum accuracy. The proposed system outperforms the stat-of-the-art gender recognition approaches; it achieves a recognition rate of 99.25% on FERET database.
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passive copy move image forgery detection using undecimated Dyadic Wavelet Transform
Digital Investigation, 2012Co-Authors: Ghulam Muhammad, Muhammad Hussain, George BebisAbstract:In this paper, a blind copy move image forgery detection method using undecimated Dyadic Wavelet Transform (DyWT) is proposed. DyWT is shift invariant and therefore more suitable than discrete Wavelet Transform (DWT) for data analysis. First, the input image is decomposed into approximation (LL1) and detail (HH1) subbands. Then the LL1 and HH1 subbands are divided into overlapping blocks and the similarity between blocks is calculated. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while that from the HH1 subband should be low due to noise inconsistency in the moved block. Therefore, pairs of blocks are sorted based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, matched pairs are obtained from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.
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Copy-move forgery detection using Dyadic Wavelet Transform
Proceedings - 2011 8th International Conference on Computer Graphics Imaging and Visualization CGIV 2011, 2011Co-Authors: Najah Muhammad, Muhammad Hussain, Ghulam Muhammad, George BebisAbstract:The issue of the verification of the authenticity and integrity of digital images is increasingly being important. Copy move forgery is one type of tempering that is commonly used for manipulating the digital contents, in this case, a part of an image is copied and is pasted on another region of the image. The non-intrusive approach for this problem is becoming attractive because it does not need any embedded information, but it is still far from being satisfactory. In this paper, an efficient non-intrusive method for copy-move forgery detection is presented. This method is based on image segmentation and similarity detection using Dyadic Wavelet Transform (DyWT). Copied and pasted regions are structurally similar and this structural similarity is detected using DyWT and statistical measures. The results show that the proposed method outperforms the stat-of-the-art methods.
Mohamed M. Bayoumi - One of the best experts on this subject based on the ideXlab platform.
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A Dyadic Wavelet affine invariant function for 2D shape recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001Co-Authors: Mahmoud I. Khalil, Mohamed M. BayoumiAbstract:Dyadic Wavelet Transform has been used to derive an affine invariant function. First, an invariant function using two Dyadic levels is derived. Then, this invariant function is used to derive another invariant function using six Dyadic levels. We introduce the Wavelet based conic equation. The invariant function is based on analyzing the object boundary using the Dyadic Wavelet Transform. Experimental results on both synthetic and real data are used to demonstrate the discriminating power of the proposed invariant function. It has also been compared with some traditional methods. The stability of the proposed invariant function is examined. In addition, the stability under large perspective Transformation is tested
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perspective invariant function using Dyadic Wavelet Transform
Proceedings of SPIE the International Society for Optical Engineering, 2000Co-Authors: Mahmoud I. Khalil, Mohamed M. BayoumiAbstract:A perspective invariant function has been derived. It is based on analyzing the object boundary using the Dyadic Wavelet Transform. Five Dyadic Wavelet transfer levels are used to define the invariant function. The selection of the Dyadic levels is discussed. Experimental results test the recognition power of the proposed invariant function. In addition, the stability of the invariant function is examined.
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Affine invariant object recognition using Dyadic Wavelet Transform
2000 Canadian Conference on Electrical and Computer Engineering. Conference Proceedings. Navigating to a New Era (Cat. No.00TH8492), 2000Co-Authors: Mahmoud I. Khalil, Mohamed M. BayoumiAbstract:This paper presents an efficient technique of recognizing planar objects under affine Transformation. This technique is based on analyzing the object boundary using the Dyadic Wavelet Transform. Two invariant functions are derived using different Dyadic levels. Experimental results show that these invariant functions are more efficient than some traditional methods. In addition, these invariant functions are stable to perspective Transformation.
