The Experts below are selected from a list of 5973 Experts worldwide ranked by ideXlab platform
Sudeep D. Thepade - One of the best experts on this subject based on the ideXlab platform.
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extended performance appraise of bayes function lazy rule tree data mining classifier in novel Transformed fractional content based image classification
International Conference on Pervasive Computing, 2015Co-Authors: Sudeep D. Thepade, Madhura M KalbhorAbstract:Image classification has become one of the important research field as hundreds of images are generated everyday which implies the need to build the classification system. To build faster and easy classification system, the visual content of images is used. Accuracy of classification depends upon the feature extraction which is one of the most important step in image classification. The paper shows the performance of additional four orthogonal Transforms using Transformed fractional content as feature for image classificationwhere the Kekre, Hartle, Slant and Haar Transform are used in addition to earlier proposed use of sine, cosine and walsh Transforms. Twelve assorted classifiers across five data mining classifier family (Bayes, Function, Lazy, Rule and Tree) are used. Here 504 number of variations for proposed image classification method are experimented using twelve classifiers, seven orthogonal Transforms and six fractions of Transformed content. The Simple Logistic classifiers with Kekre Transform gives better image classification closely followed by Simple Logistic with sine Transform and Simple Logistic with Hartley Transform.
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image compression using hybrid wavelet Transform with varying proportions of cosine Haar walsh and kekre Transforms with assorted color spaces
International Conference on Control Instrumentation Communication and Computational Technologies, 2014Co-Authors: Sudeep D. Thepade, Jaya H DewanAbstract:Due to rapid growth in technology, billions of images are captured and shared through internet every hour. Lossy image compression techniques minimize the size of the image without degrading the visual quality of the image. Previous work has proved that the hybrid wavelet Transforms (HWT) are better than the respective constituent orthogonal Transforms in image compression when considered in equal and varying proportions [1, 2, 3]. Here the extended performance comparison of HWT for image compression is presented with assorted color spaces, varying the constituent Transforms and the proportions of the constituent Transforms to test the effect on quality of image compression. The experimentation is done on set of 15 images by varying the constituent Transforms, proportion of constituent Transforms, compression ratios (CR) and color spaces. The constituent Transforms used to generate HWT are Cosine Transform, Kekre Transform, Walsh Transform and Haar Transform. Here five proportions of constituent Transforms alias 1:16, 1:4, 1:1, 4:1, 16:1 are considered for generation of HWT. Results prove that 4:1, 1:1, 1:4 proportions of constituent Transforms in HWT gives better performance as compared to all other proportions of constituent Transforms for different compression ratios. For 95% compression ratio, 4:1 ratio of Cosine- Haar constituent Transforms in HWT with LUV color space gives better results. 1:1 ratio of Cosine-Haar constituent Transforms give better results for compression ratios between 75% and 90% with RGB color space. For lower compression ratios, 1:4 proportions of Cosine-Haar constituent Transforms in HWT gives better performance with RGB color space.
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extended performance comparison of hybrid wavelet Transform for image compression with varying proportions of constituent Transforms
International Conference on Advanced Computing, 2013Co-Authors: Sudeep D. Thepade, Jaya H Dewan, Anil T LoharAbstract:The rapid growth of digital imaging applications have increased the need for effective image compression techniques. Image compression minimizes the size of the image without degrading the quality of the image to an unacceptable level. Image compression in Transform domain is one of the most popular techniques. Previous work has proved that the hybrid wavelet Transforms (HWT) are better than the respective constituent orthogonal Transforms in image compression[1, 2]. Here the extended performance comparison of HWT for image compression is presented with varying the constituent Transforms and the proportions of the constituent Transforms to test the effect on quality of image compression. The experimentation is done on set of 20 images by varying the constituent Transforms, proportion of constituent Transforms and compression ratios (CR). The constituent Transforms used to generated HWT are Cosine Transform, Sine Transform, Slant Transform, Kekre Transform, Walsh Transform and Haar Transform. Here five proportions of constituent Transforms alias 1:16, 1:4, 1:1, 4:1, 16:1 are considered for generation of HWT. Results prove that 4:1, 1:1, 1:4 proportions of constituent Transforms in HWT gives better performance as compared to 1:1 proportion of constituent Transforms for different compression ratios. Also for 95% compression ratio, 4:1 ratio of DCT-Haarconstituent Transforms in HWT gives better results. 1:1 ratio of DCT-Haarconstituent Transforms give better results for compression ratios between 70% and 90%. For lower compression ratios, 1:4 proportions of DCT-Haarconstituent Transformsin HWT gives better performance.
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palm print identification using fractional coefficient of Transformed edge palm images with cosine Haar and kekre Transform
International Conference on Information and Communication Technologies, 2013Co-Authors: Sudeep D. Thepade, Santwana S GudadheAbstract:Paper presents performance comparison of palm print identification techniques based on fractional coefficients of Transformed palm print edge image using three Transforms namely Cosine, Haar and Kekre. In Transform domain, the energy of image gets concentrated towards low frequency region; this characteristic of image Transforms is used here to reduce the feature vector size of palm print images by selecting these low frequency coefficients in Transformed edge images. Three image Transforms applied on palm print edge image and 7 ways of taking fractional coefficients along with five different edge detection methods give total 105 variation of proposed palm print identification method. Experimentation is done on a test bed of 1000 palm print images (500 left and 500 right).Genuine acceptance rate is considered for performance comparison. The experimental results in Cosine and Haar Transform have shown performance improvement in palm print identification using fractional coefficients of Transformed images. In all edge detection methods, canny is proven to be better. In all Cosine Transform gives best performance having maximum GAR value for canny edge detection method with 0.097% of fractional coefficients considering both left and right palm print images.
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iris recognition using fractional coefficients of cosine walsh Haar slant kekre Transforms and wavelet Transforms
2013Co-Authors: Sudeep D. Thepade, Pooja BidwaiAbstract:The goal of Iris recognition is to recognize human identity through the textural characteristics of one's Iris muscular patterns. Iris recognition has been acknowledged as one of the most accurate biometric modalities because of its high recognition rate. Here performance comparison among various proposed techniques of Iris Recognition using the fractional coefficients of Transformed Iris images is done considering Genuine Acceptance Ratio(GAR).The proposed method presents Iris recognition using Fractional coefficients of Cosine , Walsh, Haar, Hartley, Slant and Kekre Transforms and their Wavelet Transforms. The experiments are done on 384 samples of palacky university dataset. The experiments showed that the fractional coefficient of Transformed iris images gives higher GAR than considering 100% coefficients giving faster and better iris recognition. Results show that Cosine Transform and Cosine Wavelet Transform at 0.10% energy Compaction, Walsh wavelet at 0.10% energy compaction, Haar Transform and Haar Wavelet Transform at 0.10%energy compaction gives the best results as far as other Transforms and wavelet Transforms are considered. It also proves that Wavelet Transforms outperforms Transforms by giving higher GAR at various energy compaction levels.
Alessandro Neri - One of the best experts on this subject based on the ideXlab platform.
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secure annotation for medical images based on reversible watermarking in the integer fibonacci Haar Transform domain
Proceedings of SPIE, 2011Co-Authors: Federica Battisti, Marco Carli, Alessandro NeriAbstract:The increasing use of digital image-based applications is resulting in huge databases that are often difficult to use and prone to misuse and privacy concerns. These issues are especially crucial in medical applications. The most commonly adopted solution is the encryption of both the image and the patient data in separate files that are then linked. This practice results to be inefficient since, in order to retrieve patient data or analysis details, it is necessary to decrypt both files. In this contribution, an alternative solution for secure medical image annotation is presented. The proposed framework is based on the joint use of a key-dependent wavelet Transform, the Integer Fibonacci-Haar Transform, of a secure cryptographic scheme, and of a reversible watermarking scheme. The system allows: i) the insertion of the patient data into the encrypted image without requiring the knowledge of the original image, ii) the encryption of annotated images without causing loss in the embedded information, and iii) due to the complete reversibility of the process, it allows recovering the original image after the mark removal. Experimental results show the effectiveness of the proposed scheme.
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a commutative digital image watermarking and encryption method in the tree structured Haar Transform domain
Signal Processing-image Communication, 2011Co-Authors: Michela Cancellaro, Francesco G.b. De Natale, Federica Battisti, Marco Carli, Giulia Boato, Alessandro NeriAbstract:In this paper a commutative watermarking and ciphering scheme for digital images is presented. The commutative property of the proposed method allows to cipher a watermarked image without interfering with the embedded signal or to watermark an encrypted image still allowing a perfect deciphering. Both operations are performed on a parametric Transform domain: the Tree Structured Haar Transform. The key dependence of the adopted Transform domain increases the security of the overall system. In fact, without the knowledge of the generating key it is not possible to extract any useful information from the ciphered-watermarked image. Experimental results show the effectiveness of the proposed scheme.
Dinggang Shen - One of the best experts on this subject based on the ideXlab platform.
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block extraction and Haar Transform based linear singularity representation for image enhancement
Mathematical Problems in Engineering, 2019Co-Authors: Yingkun Hou, Guanghai Liu, Seongwhan Lee, Dinggang ShenAbstract:In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar Transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar Transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet Transform (NSCT), and guided image filtering.
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enhancement of perivascular spaces in 7 t mr image using Haar Transform of non local cubes and block matching filtering
Scientific Reports, 2017Co-Authors: Yingkun Hou, Dinggang Shen, Sanghyun Park, Qian Wang, Jun Zhang, X Zong, Weili LinAbstract:Perivascular spaces (PVSs) in brain have a close relationship with typical neurological diseases. The quantitative studies of PVSs are meaningful but usually difficult, due to their thin and weak signals and also background noise in the 7 T brain magnetic resonance images (MRI). To clearly distinguish the PVSs in the 7 T MRI, we propose a novel PVS enhancement method based on the Haar Transform of non-local cubes. Specifically, we extract a certain number of cubes from a small neighbor to form a cube group, and then perform Haar Transform on each cube group. The Haar Transform coefficients are processed using a nonlinear function to amplify the weak signals relevant to the PVSs and to suppress the noise. The enhanced image is reconstructed using the inverse Haar Transform of the processed coefficients. Finally, we perform a block-matching 4D filtering on the enhanced image to further remove any remaining noise, and thus obtain an enhanced and denoised 7 T MRI for PVS segmentation. We apply two existing methods to complete PVS segmentation, i.e., (1) vesselness-thresholding and (2) random forest classification. The experimental results show that the PVS segmentation performances can be significantly improved by using the enhanced and denoised 7 T MRI.
Kinpong Chan - One of the best experts on this subject based on the ideXlab platform.
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efficient time series matching by wavelets
International Conference on Data Engineering, 1999Co-Authors: Kinpong ChanAbstract:Time series stored as feature vectors can be indexed by multidimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, Transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different Transformations like Discrete Fourier Transform (DFT) Discrete Wavelet Transform (DWT), Karhunen-Loeve (KL) Transform or Singular Value Decomposition (SVD) can be applied. While the use of DFT and K-L Transform or SVD have been studied on the literature, to our knowledge, there is no in-depth study on the application of DWT. In this paper we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar Transformed domain and no false dismissal will occur, (2) we show that Haar Transform can outperform DFT through experiments, (3) a new similarity model is suggested to accommodate vertical shift of time series, and (4) a two-phase method is proposed for efficient n-nearest neighbor query in time series databases.
Michela Cancellaro - One of the best experts on this subject based on the ideXlab platform.
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a commutative digital image watermarking and encryption method in the tree structured Haar Transform domain
Signal Processing-image Communication, 2011Co-Authors: Michela Cancellaro, Francesco G.b. De Natale, Federica Battisti, Marco Carli, Giulia Boato, Alessandro NeriAbstract:In this paper a commutative watermarking and ciphering scheme for digital images is presented. The commutative property of the proposed method allows to cipher a watermarked image without interfering with the embedded signal or to watermark an encrypted image still allowing a perfect deciphering. Both operations are performed on a parametric Transform domain: the Tree Structured Haar Transform. The key dependence of the adopted Transform domain increases the security of the overall system. In fact, without the knowledge of the generating key it is not possible to extract any useful information from the ciphered-watermarked image. Experimental results show the effectiveness of the proposed scheme.
