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IEEE/SP Workshop on Statistical signal processing (SSP)

作者： S. Combrexelle H. Wendt J.-Y. Toumeret P. Abry S. McLaughlin IRIT - ENSEEIHT University of Toulouse Toulouse France Universite Federale Toulouse Midi-Pyrenees Toulouse Occitanie FR CNRS Ecole Normale Supérieure de Lyon Lyon France School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

ISBN: (纸本)9781467378048

Texture analysis can be conducted within the mathematical framework of multifractal analysis (MFA) via the study of the regularity fluctuations of image amplitudes. Successfully used in various applications, however MFA remains limited to the independent analysis of single images while, in an increasing number of applications, data are multi-temporal. The present contribution addresses this limitation and introduces a Bayesian framework that enables the joint estimation of multifractal parameters for multi-temporal images. It builds on a recently proposed Gaussian model for wavelet leaders parameterized by the multifractal attributes of interest. A joint Bayesian model is formulated by assigning a Gaussian prior to the second derivatives of time evolution of the multifractal attributes associated with multi-temporal images. This Gaussian prior ensures that the multifractal parameters have a smooth temporal evolution. The associated Bayesian estimators are then approximated using a Hamiltonian Monte-Carlo algorithm. The benefits of the proposed procedure are illustrated on synthetic data.

关键词： Fractals Bayes methods Estimation wavelet transforms Monte Carlo methods Computational modeling signal processing

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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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Implementation of barrel distortion correction on DSP in real time

Implementation of barrel distortion correction on DSP in rea...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Prajwal G. Awade Ramakrishna Bodhula Nilkantha Chopade Department of Electronics and Telecommunication Pimpri Chinchwad College of Engineering Pune India Wavelet Technologies Private Limited Pune India

ISBN: (纸本)9781509032921

Camera plays a vital role in intelligent embedded image processing systems. But unlike human eyes, it creates distortions in the image captured, most commonly called as lens distortion. The uneven magnification over the lens surface is responsible for lens distortion. Distortion conveys incorrect information about the coordinates in the image frame with respect to real world coordinates and thus decreases the positional accuracy of the processing in the complete system. In video or image processing applications like motion detection, satellite tracking, automatic vehicle parking, ADAS etc. the lens distortion needs to be eliminated. Hence, to improve accuracy of such applications, implementation of a barrel distortion correction algorithm in real time is presented in this work. The algorithm is implemented on the Analog Devices' BF561 Digital signal Processor for real time distortion correction, and the results are discussed. The results shown include the outputs of algorithm on digital signal processor platform.

关键词： Streaming media Lenses Distortion Hardware MATLAB

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A Robust Watermarking Scheme for City image

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INTERNATIONAL JOURNAL OF SECURITY AND ITS applications 2016年第1期10卷 303-314页

作者： Jin, Ruichen Kim, Jongweon Sangmyung Univ Dept Copyright Protect Seoul South Korea Sangmyung Univ Dept Contents & Copyright Seoul South Korea

In this paper, we propose an effective watermarking scheme based on Radon transform for city image. The robustness against rotation attack is achieved using the translation property of the Radon transform. The Radon transform emphasizes and detects the linear characteristic to calculate the angle of image rotation. The watermark is a hologram generated by quantization based on the cover image. We used hologram quantization to spread the watermark information and analyze the cover image detail. The hologram is transformed by a discrete fractional random transform (DFRNT) with a random seed beta. It makes the watermark security. The proposed method uses discrete wavelet transform (DWT) domain. DWT domain watermarking is robust to signal processing attacks. For improving the accuracy of detecting, we fix the center of image and swivel that the pixels of image boundary move 1 notch at a time. The proposed algorithm also robust to the spatial Effect attack. We have performed an intensive simulation to show the robustness in rotation of the proposed method.

关键词： rotation invariant image digital watermarking radon transform hologram discrete wavelet transform discrete fractional random transform image normalization

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Enhanced data concealing technique to secure medical image in telemedicine applications

Enhanced data concealing technique to secure medical image i...

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International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： G. Vallathan G. Gayathri Devi A. Vinoth Kannan Department of Electronics and Communication Engineering IFET College of Engineering Department of Electronics and Communication Engineering IFET College of Engineering India

ISBN: (纸本)9781467393393

Telemedicine application involves in locales where enormous amount of medical data is to be securely transferred over the public network in an effective conduct. Whilst the data is accessed by an illegitimate individual, it may lead to malicious attack or any modification of medical image may result in misdiagnosis. In this paper, both encryption and steganography technique is employed to improve the security of both patient's privacy information and the medical image. The host medical image undergoes Contourlet Transform to provide the innate geometrical structures of an image like curves instead of points and it offers the directionality and anisotropy property. Next, the patient privacy information is embedded over the High frequency components of a transformed image using LSB embedding algorithm. Finally the data concealed image is encrypted using LBG algorithm to ensure security. The superiority of the system is analyzed by some of the critical performance metrics such as MSE (Mean Square Error), PSNR (Peak signal to noise ratio), and SSIM (structural similarity index).

关键词： Biomedical imaging Computed tomography Encryption Magnetic resonance imaging wavelet transforms

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wavelet domain dictionary learning-based single image superresolution

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signal image AND VIDEO processing 2015年第7期9卷 1491-1501页

作者： Nazzal, M. Ozkaramanli, H. Eastern Mediterranean Univ TRNC Dept Elect & Elect Engn Gazimagusa Turkey

Recently sparse representations over learned dictionaries have been proven to be a very successful representation method for many image processing applications. This paper proposes a new approach for increasing the resolution from a single low-resolution image. This approach is based on learned dictionaries in the wavelet domain. The proposed method combines many desired properties of wavelet-based representations such as compactness, directionality and analysis in many scales with the flexibility of redundant sparse representations. Such an approach serves for two main purposes. First, it sparsifies the training set, and second, it allows the design of structured dictionaries. Structured dictionaries better capture intrinsic image characteristics. Furthermore, the design of multiple structured dictionaries serves to reduce the number of dictionary atoms and consequently reduces the computational complexity. Three couples of wavelet subband dictionaries are designed using the K-SVD algorithm: three for the low-resolution and three for the high-resolution wavelet subband images. The image patch size and dictionary redundancy issues are empirically investigated in this work. Extensive tests indicate that a patch size of and a dictionary width of 216 is a good compromise between computational complexity and representation quality. The proposed algorithm is shown to be superior to the leading spatial domain sparse representation techniques both visually and quantitatively with an average PSNR increase of 1.71 dB as tested over the Kodak data set. This result is also validated in terms of SSIM as a perceptual quality metric. It is shown that the proposed approach better restores the lost high-frequency details in the three wavelet detail subbands. Furthermore, the proposed algorithm is shown to significantly reduce the dictionary learning and sparse coding computational complexity.

关键词： Sparse representation wavelet domain dictionary learning Redundant dictionaries Single image superresolution

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Sparse coding based compression of spectrally uncorrelated hyperspectral data using Haar wavelet transform

Sparse coding based compression of spectrally uncorrelated h...

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IEEE signal processing and Communications applications (SIU)

作者： Jülide G. Alaydın B. Uğur Töreyin Elektrik-Elektronik Mühendisliği Bölümü Boğaziçi Üniversitesi Turkey Bilişim Enstitüsü İstanbul Teknik Üniversitesi Turkey

Sparse coding based compression of hyperspectral imagery yields better rate-distortion performance especially for low bit-rates when compared with other state-of-the-art methods in the literature. In this paper, an on-line dictionary learning based lossy compression method is proposed yielding even a better rate-distortion performance, thanks to the spectral decorrelation achieved by the Haar wavelet transform. The hyperspectral data is decorrelated in the spectral dimension using a single-level Haar transform which is followed by a dictionary learning step over the low-subband data. The higher subband is further compressed in a lossless manner using JPEG2000. Rate-distortion results are obtaind for AVIRIS hyperspectral data. Results indicate that the spectral decorrelation coupled with sparse dictionary learning of low-subband images yield superior performance over existing hyperspectral data compression schemes.

关键词： Hyperspectral imaging image coding Dictionaries Decorrelation wavelet transforms

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Objective Smoothness Metric for Just Noticeable Blur Estimation

Objective Smoothness Metric for Just Noticeable Blur Estimat...

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International Congress on image and signal processing, BioMedical Engineering and Informatics

作者： Mong-Shu Lee Kuan-Chih Chiu Department of Computer Science and Engineering National Taiwan Ocean University

ISBN: (纸本)9781509037117

An objective blur measure is crucial for a variety of image processing applications. Traditional researches concentrate on a model estimating the amount of spatial high frequency. However, human vision detects blurriness might be influenced by the texture of image contents. To address the important issue, this paper presents a new objective metric designed as both measuring the inherent smooth texture and predicting the difference induced by blur distortion. The approach is based on the wavelet characterization of Besov function spaces and just noticeable difference of human vision. Then, the just noticeable blur is estimated from the derived psychometric function using the proposed metric and subjective scores. Experimental comparisons with state-of-the-arts quality metrics for public databases show that the proposed metric achieved high correlation with the general objective quality or subjective metrics.

关键词： blur distortion just noticeable difference psychometric function Besov space image quality assessment

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Real time watermarking of grayscale images using integer DWT transform

Real time watermarking of grayscale images using integer DWT...

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International Conference on VLSI Systems, Architecture, Technology and applications (VLSI-SATA)

作者： S. M. Sakthivel A Ravi Sankar Chennai Campus SENSE VIT University Chennai India VIT University Vellore Tamil Nadu IN

ISBN: (纸本)9781509000371

In this paper, 2D integer wavelet transform based watermarking is carried out for the grayscale image with its VLSI architectural implementations. In the 2D integer wavelet transformation the lifting scheme is adopted and the watermarking operation is carried out in the LL2 frequency subbands. The entire watermark embedding process and extraction process are modeled in MATLAB and analyzed against the signal processing attacks like compression, salt & pepper noise, rotation and Intensity transformation attacks. Finally the same algorithm is modeled using Verilog HDL and implemented using ALTERA QUARTUS-II.

关键词： Watermarking Discrete wavelet transforms Two dimensional displays Mathematical model Very large scale integration Hardware design languages

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Human age classification using facial skin analysis and multi-class support vector machine

Human age classification using facial skin analysis and mult...

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International Conference on signal and Information processing (IConSIP)

作者： Jayant Jagtap Manesh Kokare Dept. of Electronics and Telecommunication Engg. S. G. G. S. Institute of Engineering and Technology Vishnupuri Nanded India

ISBN: (纸本)9781509015238

Human age classification via face images becoming an interesting research area because of potential applications in the field of computer vision such as Age Specific Human Computer Interaction (ASHCI), biometrics, security and surveillance, etc. In this paper, a novel method for human age classification using facial skin analysis for aging feature extraction and Multi-class Support Vector Machine (M-SVM) for age classification is proposed to classify the face images into four age groups. Facial skin analysis consists of skin texture analysis and wrinkle analysis. Gabor wavelet is used to analyze the facial skin textural changes with age progression. Wrinkle analysis detects the wrinkle density changes at particular regions on face image with age progression. The performance evaluation of proposed age classification system is carried out by using face images from PAL face database. In this paper, the performance of M-SVM classifier is compared with the performance of Artificial Neural Network (ANN) classifier for the task of human age classification using Gabor wavelet and wrinkle analysis. The result analysis concludes that the best age classification accuracy of 93.61% is achieved by using proposed age classification system and M-SVM is an efficient classifier than the ANN classifier for the task of human age classification in combination with Gabor wavelet and wrinkle analysis.

关键词： Face Feature extraction Aging Databases Skin wavelet analysis Support vector machines

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