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International conference on signal and Information processing (IConSIP)

作者： S. S. Chavan R. S. Holambe Instrumentation Enginering S.G.G.S. Institute of Engineering & Technology Nanded India

ISBN: (纸本)9781509015238

Many wavelet design techniques build adaptive wavelets from existing wavelets. However, most of the techniques do not design wavelet directly of the signal of interest. Conventional signal dependent transforms are Karhunen-Loeve transform and Singular Value Decomposition whose basis function depends on statistics of an input signal. signal-dependent transform is considered to be best among all linear transformations with respect to energy compaction. Likewise, wavelets are best known for image compression, many researchers are interested in using wavelets for detection or recognition. Choosing appropriate wavelet for a given application is a challenging task. Therefore, there is a need of designing wavelet to match a signal shape which increases performance for detection or identification applications. J. Chapa and R. Rao [15] have introduced algorithms for designing wavelet to match the signal shape. The construction of direct wavelet function which will look like the signal and whose family of 2 -p/2 ψ(2 -p t - q) wavelets generate an orthonormal Riesz basis of L 2 (R) are established. In this method, numerical algorithms are used for finding matched wavelet amplitude spectra and group delays using sampled data which are signal dependent wavelets. Orthonormal scaling function (father wavelet function) is obtained by applying orthonormal multiresolution analysis (OMRA) conditions on input signal.

关键词： Multiresolution analysis wavelet transforms signal resolution Delays Algorithm design and analysis Fourier transforms

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Comparison analysis of digital image watermarking using DWT and LSB technique

Comparison analysis of digital image watermarking using DWT ...

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International conference on Communications and signal processing

作者： N. Senthil Kumaran S. Abinaya Department of Computer Science and Applications The Gandhigram Rural Institute - Deemed University Dindigul Department of Computer Science and Applications Deemed University Dindigul

ISBN: (纸本)9781467385497

image security is a relatively very young and fast growing. Security of data or information is very important now a day in this world. In this paper proposed to advantages and that working functionalities. This algorithm is verified on different watermarking images. And it's provide robust and secure results. To measure the effectiveness of this algorithm is provide embedding and extracting images. PSNR and MSE also calculated the embedding watermarking images. In this DWT watermarking embedding result images provide the good, secure and robust. In this paper proposed to how to process LSB technique.

关键词： Watermarking Discrete wavelet transforms Digital images signal processing algorithms Robustness

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Fusion of multispectral images using a new subspace approach

Fusion of multispectral images using a new subspace approach

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IEEE signal processing and Communications applications (SIU)

作者： Sahin Işık Golara Ghorban Dordinejad Kemal Özkan Eskisehir Osmangazi Universitesi Eskisehir TR

Fusion of multispectral images is a major research problem in terms of surveillance, remote sensing, industrial automation, medical and defense applications. The main reason behind multispectral image fusion is that using single channel images does not meet requirements of classification, segmentation and related tasks of remote sensing applications. Therefore, a new solution is needed to combine multispectral images to get more accurate and good visualized image as well as preserving the important details behind them. With this purpose, we have a proposed a new image fusion technique by adopting the Common Vector Approach concept. Upon examining the results, one can observe that using the CVA method for image fusion promises good results when compared with Principal Component Analysis and Singular Value Decomposition.

关键词： image fusion Face Remote sensing Paints Principal component analysis Painting Discrete wavelet transforms

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The role of the coarsest resolution subband in the wavelet-based reconstruction of signals from gradients

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Journal of Physics: conference Series 2017年第1期933卷

作者： Ioana S. Sevcenco Panajotis Agathoklis Electrical and Computer Engineering Department University of Victoria Canada

An efficient wavelet-based algorithm to reconstruct non-square/non-cubic signals from gradient data is proposed. This algorithm is motivated by applications such as image or video processing in the gradient domain. In some earlier approaches, the non-square/non-cubic gradients were extended to enable a square/cubic Haar wavelet decomposition and the coarsest resolution subband was derived from the mean value of the signal. In this paper, a non-square/non-cubic wavelet decomposition is obtained directly without extending the gradient data. The challenge comes from finding the coarsest resolution subband of the wavelet decomposition and an algorithm to compute this is proposed. The performance of the algorithm is evaluated in terms of accuracy and computation time, and is shown to outperform the considered earlier approaches in a number of cases. Further, a closer look on the role of the coarsest resolution subband coefficients reveals a trade-off between errors in reconstruction and visual quality which has interesting implications in image and video processing applications.

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Unsupervised approach for change map generation

Unsupervised approach for change map generation

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International conference on Communications and signal processing

作者： Harshada N. Mahulkar Bhakti Sonawane Computer Engineering Department Shah and Anchor kutcchi College of Engineering Mumbai India

ISBN: (纸本)9781467385497

Change detection is the process of automatically identifying and analyzing region that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable information of the possible transformations a given scene has suffered over time. Change detection is used in several applications (eg. Disaster management, deforestation, urbanization, etc). In the proposed unsupervised method co-registered and radiometrically corrected temporal images are used as input. Using this, absolute valued image and log ratio image is calculated to get difference image. These difference images are fused using Discrete wavelet Transform (DWT). Then, min-mean normalization is applied to the get filtered data. The normalized data is clustered into two groups using K-means clustering algorithm as changed pixels and unchanged pixels. Experiment result is also calculated using two different ways. In first, fused image data is given to Principal Component Analysis (PCA) and clustering is done using K-means algorithm and in second way Fuzzy c-means clustering algorithm is used to cluster image data.

关键词： Discrete wavelet transforms Principal component analysis Clustering algorithms Algorithm design and analysis Change detection algorithms Remote sensing

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Surface reconstruction with wavelet transformation

Surface reconstruction with Wavelet transformation

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IEEE International conference on Intelligent Engineering Systems (INES)

作者： Petra Balla Peter Kocsis György Eigner Ákos Antal Department of Mechatronics Optics and Mechanical Engineering Informatics BUTE Budapest Hungary Research and Innovation Center of Obuda University Obuda University Budapest Hungary

The development in computing power highlights some forgotten algorithms, which were neglected because of their complexity and slowness on early computers. One example is the wavelet-Transformation Profilometry (WTP) of which successful application is demonstrated in the paper. WTP is a high level signal processing method using orthogonal algorithms for huge datasets. The high performance in quality and running speed makes the described method suitable for medical image processing applications.

关键词： wavelet transforms Surface waves Surface treatment Surface reconstruction image reconstruction Computers

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A novel iris recognition technique using monogenic wavelet phase encoding

A novel iris recognition technique using monogenic wavelet p...

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International conference on signal and Information processing (IConSIP)

作者： Parmeshwar Birajadar Pushkaraj Shirvalkar Shubham Gupta Varun Patidar Utkarsh Sharma Ameya Naik Vikram Gadre Indian Institute of Technology Guwahati Guwahati Assam IN Department of Electrical Engineering IIT Bombay Mumbai India Department of Electronic and Telecommunication Engineering KJSCOE Mumbai India

ISBN: (纸本)9781509015238

Encoding the local phase of the iris texture has come out as a promising way of generating extremely efficient feature vectors. Gabor wavelets yield a local phase representation distributed over several scales and orientations. One of the very promising attempts at obtaining a useful phase information is from the field of analytic signals and wavelets. The monogenic signal is an extension of the analytic signal to multiple dimensions. In this paper, we propose a novel technique that utilizes multi-resolution monogenic phase for iris recognition. The motivation behind the proposal has been discussed and supported by experiments. Here we suggest that the distinctive ability of the monogenic wavelets to simultaneously extract local phase and orientation can be exploited for iris recognition applications. In order to test the effectiveness of the proposed method, the reconstructions obtained using multi-resolution monogenic phase are compared with the Gabor and Fourier counterparts. Improvement observed in the structural similarity index (SSIM) value reveals the suitability of the monogenic wavelet phase for the applications involving texture identification. Although monogenic wavelets can be used for general texture identification, here, we specifically consider the iris recognition application. Experimental results obtained on the CASIA version 1 and the version 4 interval databases show that the monogenic wavelets can achieve an accuracy comparable to that of the Gabor wavelets.

关键词： Iris recognition wavelet transforms image reconstruction Encoding image segmentation Iris

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Performance comparison of JPEG, JPEG2000 & JPEG XR image compression standards

Performance comparison of JPEG, JPEG2000 & JPEG XR image com...

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IEEE signal processing and Communications applications (SIU)

作者： Emir Öztürk Altan Mesut Aydın Carus Bilgisayar Mühendisliği Bölümü Trakya Üniversitesi Edirne Türkiye

In this study, the performances of JPEG (the most widely used lossy image compression standard until it was published in 1992), JPEG2000 (designed to provide superior image quality at low bit rates) and JPEG XR (aimed to reach the speed of JPEG and the quality of JPEG2000) are evaluated with an application developed in C# language which is able to use different codecs. The results show that recently developed JPEG standard (JPEG XR) is able to compress images with the same quality as JPEG2000, but not the same speed as JPEG.

关键词： Transform coding image coding Codecs Media Standards Discrete cosine transforms Discrete wavelet transforms

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wavelet based new hybrid image denoising

Wavelet based new hybrid image denoising

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International conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： M. B. Meenavathi M. Easwara Dept. of E & CE CBIT Kolar India Dept. of E & I E BIT Bengaluru India

ISBN: (纸本)9781509046980

In the modern age, the transmission of digital images is one of the major processes of communication system. Meanwhile, images are often corrupted by the noise. In many image processing applications the reconstruction of high quality image is an essential fact. To achieve this, we proposed a wavelet based hybrid image denoising. In which, after wavelet decomposition of a noisy image both the approximation and the detail coefficients are incorporated into modification. Approximation coefficients are processed by the Cloud Model (CM) and estimated by the fuzzy mean without omitting the contribution of an individual. The detail parts undergo sub band based adaptive modified firm thresholding. Finally, the image is treated with an Adaptive Wiener filter, to further improve its visual quality. Evaluation indicates that our proposed algorithm outperforms over wavelet based Weighted High Frequency Kernel (WHFC) and median methods in terms of Peak signal to Noise Ratio (PSNR).

关键词： Discrete wavelet transforms Wiener filters Filter banks image denoising image reconstruction Kernel Noise measurement

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Enhancement of latent fingerprints on banknotes using monogenic wavelets

Enhancement of latent fingerprints on banknotes using monoge...

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International conference on signal processing and Communications, SPCOM

作者： Parmeshwar Birajadar Varun Patidar Pushkaraj Shirvalkar Shubham Gupta Vikram Gadre Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9781509017478

Latent fingerprints on banknotes provide valuable information in forensic applications to identify suspects of robbery cases and counterfeiters. The image quality of latent fingerprints is poor due to the complex background of banknotes compared to live-scan images acquired in a controlled environment. An effective orientation field estimation algorithm is necessary for enhancing poor quality latents in the presence of background noise for reliable extraction of minutia features. There have been attempts in the literature to characterize a fingerprint using an AM-FM model. Motivated by the ability of the monogenic signal representation to demodulate such a signal to extract its features, this paper proposes a novel algorithm of latent fingerprint multiresolution orientation estimation with a modified Simoncelli isotropic wavelet design and by an appropriate use of the multiresolution orientation field. Experimental results on synthetic noisy images and real latents from our created latent fingerprint database on banknotes show that the enhanced image quality by using the proposed algorithm is better than the other existing methods for estimating the fingerprint orientation.

关键词： Estimation image resolution signal resolution Two dimensional displays Feature extraction Noise measurement signal representation

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