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共 2167 条记录，以下是2001-2010 订阅

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A New Approach to Time-Frequency Analysis

A New Approach to Time-Frequency Analysis

引用

Data Compression conference (DCC)

作者： Xiteng Liu Electrical and Computer Engineering Department McMaster University Hamilton ONT Canada

ISBN: (纸本)9781424464258;9780769539942

Summary form only given. Time-frequency analysis is the fundamental methodology in signal processing. It provides a description of signal in the time-frequency plane. Conventionally, in applications of time-frequency analysis, the time domain of a signal is partitioned into intervals at first. Interval by interval, one computes the local frequency spectrum in an interval and then makes signal processing with respect to this local spectrum. The whole procedure goes on while time interval changes. This is the popular, classic approach to time-frequency analysis. To date, the research and applications of time-frequency analysis have been taking this approach. By this approach, signal contents are treated interval by interval locally in time. It neglects the statistical dependency between local frequency spectra in neighboring time intervals. Data compression is an important and successful application of time-frequency analysis. The international standards JPEG(1990), MPEG1(1993), MPEG2(1994), MPEG4(1998), H.264(2003) and JPEG XR(2009) are all good examples. In these systems, image data are encoded in the classic approach to time-frequency analysis. Separately block by block, image data are transformed, quantized and entropy encoded. Another mentionable image coding scheme in classic approach was introduced, where image data are transformed by 8 x 8 DCT. After transform, the 64 coefficients in an 8 x 8 block are treated as a depth-3 tree, without any inter-block regroupment or other operations. Within each block, the coefficients are separately quantized and encoded by the famous EZW method which is popular in wavelet-based image coding. In this paper, we change the way and advocate a new approach to time-frequency analysis. We make signal processing within frequency bands rather than time intervals. We treat signal contents band by band locally in frequency. The new approach gives rise to a good platform to exploit the dependency between local frequency spectra. The ba

关键词： Time frequency analysis signal processing Transform coding Data compression image coding Data engineering Application software Time domain analysis Entropy Discrete cosine transforms

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image processor capable of block-noise-free JPEG2000 compression with 30 frames/s for digital camera applications

Image processor capable of block-noise-free JPEG2000 compres...

引用

IEEE International conference on Solid-State Circuits (ISSCC)

作者： H. Yamauchi S. Okada K. Taketa T. Ohyama Y. Matsuda T. Mori T. Watanabe Y. Matsuo Y. Yamada T. Ichikawa Y. Matsushita SANYO Electric Gifu Japan

A one-chip image processor for next-generation digital cameras and broadband PDA multimedia mobile phones is described. It is capable of processing JPEG2000 data with 30 frames/s and a 27 MHz operating frequency. The ... 详细信息

ISBN: (纸本)0780377079

关键词： Transform coding image coding Digital cameras Discrete wavelet transforms Tiles Pipelines Parallel processing wavelet transforms signal processing Costs

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Distributed compressive video sensing

Distributed compressive video sensing

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li-Wei Kang Chun-Shien Lu Institute of Information Science Academia Sinica Taipei Taiwan

Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently. In this paper, by integrating the respective characteristics of DVC and CS, a distributed compressive video sensing (DCVS) framework is proposed to simultaneously capture and compress video data, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. At the decoder, compressed video can be efficiently reconstructed using the modified GPSR (gradient projection for sparse reconstruction) algorithm. With the assistance of the proposed initialization and stopping criteria for GRSR, derived from statistical dependencies among successive video frames, our modified GPSR algorithm can terminate faster and reconstruct better video quality. The performance of our DCVS method is demonstrated via simulations to outperform three known CS reconstruction algorithms.

关键词： Video compression image coding Decoding image reconstruction Video coding Videoconference Encoding Reconstruction algorithms Discrete wavelet transforms Wireless sensor networks

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An Efficient Spatial Domain Methods & wavelet Transform for Contour Compression

An Efficient Spatial Domain Methods & Wavelet Transform for ...

引用

International conference on Artificial Intelligence and image processing(ICAIIP’2012);International conference on Innovations in Electrical and Electronics Engineering(ICIEE’2012);International conference on Mechanical,Automobile and Biodiesel Engineering(ICMABE’2012);International conference on History,Literature and Management(ICHLM’2012);International conference on Agriculture,Chemical and Environmental Sciences(ICACES’2012);International conference on Biological and Medical applications(ICBMA’2012)

作者： Ali Ukasha Fathi Alwafie the Sebha University Faculty of EngineeringElectronics Department

This paper presents a method of contour extraction and compression from grey level *** proposed algorithm is applied in spectral domain using singlelevel wavelet transform (WT).Single step parallel contour extraction (SSPCE) method is used for the binary image after inverse wavelet transform is applied to the details *** the contours are compressed using either ramer,tangent,triangle,or trapezoid methods in spatial *** of the contour extraction and compression for different classes of images is *** the paper the main idea of the analyzed procedure for both contour extraction and image compression are *** compare the results,the mean square error,signal-to-noise ratio criterions,and compression ratio (or bit per pixel) were *** simplicity to obtain compressed image and extracted contours with accepted level of the reconstruction is the main advantage of the proposed algorithm.

关键词： wavelet transform,image compression,ramer,tangent,triangle and trapezoid methods.

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On the Effects of Synthetic-Aperture Length on SAS Seabed Segmentation

On the Effects of Synthetic-Aperture Length on SAS Seabed Se...

引用

International conference on Intelligent Systems Design and applications (ISDA)

作者： David P. Williams Johannes Groen NATO Undersea Research Centre La Spezia Italy

In this work, we quantify the relationship between synthetic-aperture length (or equivalently, along-track resolution) and seabed segmentation performance experimentally for real synthetic aperture sonar (SAS) imagery. The seabed segmentation algorithm employed uses wavelet-based features, spectral clustering, and a variational Bayesian Gaussian mixture model. It is observed that for this approach, the correct seabed segmentation rate drops approximately ten percentage points for each halving of the along-track resolution between 3 cm and 96 cm. Moreover, changing the along-track resolution has the most significant effect on rocky seabeds.

关键词： Synthetic aperture sonar image resolution image segmentation signal resolution Degradation High-resolution imaging image processing image reconstruction Intelligent systems Clustering algorithms

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Low SNR Sonar image Restoration Based on Mixed Probability Statistical Model in wavelet Domain

Low SNR Sonar Image Restoration Based on Mixed Probability S...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ping Xia Qiang Ren Dong-xia Shi Bang-jun Lei Yao-bin Zou Guang-zhu Xv Hubei Key Laboratory of Intelligent Vision based Monitoring for Hydroelectric Engineering College of Computer and Information Technology Three Gorges University YiChang China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Since it is difficult to get texture details for sonar image denoising with strong noise and weak feature information. A sonar image enhancement algorithm in wavelet domain based on Gaussian mixture model and Gaussian mixture model is proposed to preserve the weak feature information of the sonar image. Under strong noise interference, the traditional enhancement method has certain difficulties in measuring the similarity of the details of the sonar image. For this reason, wavelet multi-scale analysis is performed on the sonar image to extract the weak feature information of each resolution. Secondly, a directed probability map model between adjacent scales in the wavelet domain is constructed to realize the similar weak feature information association. According to the parent node state probability and the transition probability matrix, the state values of the corresponding child nodes are determined, and the relationship between the state information of parent and child node is constructed. Thirdly, the Gaussian mixture model is used to fit the wavelet coefficient state distribution, and the neighborhood coefficient correlation is used to describe the relationship between the weak feature information in the scale. Finally, the wavelet coefficient estimation of the restored image signal is calculated by the state probability obtained in the expectation maximization (EM) algorithm, and the sonar image is reconstructed. The results of the contrast experiment are verified by visual effects and objective evaluation. The proposed algorithm can preserve the weak edge and contour information while suppressing the noise of the sonar image. It has better peak signal-to-noise ratio, signal-to-noise ratio and Structural similarity.

关键词： Sonar wavelet coefficients wavelet domain Hidden Markov models image enhancement Feature extraction wavelet analysis

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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...

引用

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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Joint image classification and compression using hierarchical table-lookup vector quantization

Joint image classification and compression using hierarchica...

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Data Compression conference (DCC)

作者： N. Chaddha K. Perlmutter R.M. Gray Information Systems Laboratoty University of Stanford CA USA

Classification and compression play important roles today in communicating digital information and their combination is useful in many applications. The aim is to produce image classification without any further signal processing on the compressed image. This paper presents techniques for the design of block based joint classifier and quantizer classifiers/encoders implemented by table lookups. In the table lookup classifiers/encoders, input vectors to the encoders are used directly as addresses in code tables to choose the codewords with the appropriate classification information. In order to preserve manageable table sizes for large dimension VQs, hierarchical structures that quantize the vector successively in stages are used. Since both the classifier/encoder and decoder are implemented by table lookups, there are no arithmetic computations required in the final system implementation. They are unique in that both the classifier/encoder and the decoder are implemented with only table lookups and are amenable to efficient software and hardware solutions.

关键词： image classification image coding Vector quantization Table lookup Decoding image storage signal processing algorithms Humans Distortion measurement wavelet transforms

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wavelet Knowledge Distillation for Efficient Stain Normalization in Digital Histopathology

Wavelet Knowledge Distillation for Efficient Stain Normaliza...

引用

IEEE signal processing and Communications applications (SIU)

作者： Sefa Keklik Murat Ekinci Elif Baykal Kablan Bilgisayar Mühendisliği Bölümü Karadeniz Teknik Üniversitesi Trabzon Türkiye Yazılım Mühendisliği Bölümü Karadeniz Teknik Üniversitesi Trabzon Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In digital histopathology, color standardization, known as stain normalization, is widely used in computer-aided diagnosis (CAD) systems. This study details the adaptation and implementation of the wavelet Knowledge Distillation (WKD) method to CAD systems. The proposed method focuses on knowledge transfer between the teacher and student models within a specially designed Pix2Pix Generative Adversarial Network (GAN) for stain normalization in histopathology images. The student model, guided by the knowledge transferred by the teacher model using wavelet-based feature extraction, significantly improves the accuracy of stain normalization, which is crucial for preserving histological details and image quality. The WKD method has demonstrated high performance on the publicly available paired MITOS-ATYPIA dataset, outperforming state-of-the-art methods. Using the same settings, the Teacher model achieved a PSNR of 25.559, SSIM of 0.934, and RMSE of 7.270. Additionally, the student model used in the method yielded better results in the Fréchet Inception Uzaklığı (FID) metric compared to the teacher and baseline models.

关键词： Knowledge engineering Measurement image quality Solid modeling Adaptation models Histopathology Standardization

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Automatic Selection of Control Points for Remote Sensing image Registration Based on Multi-Scale SIFT

Automatic Selection of Control Points for Remote Sensing Ima...

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2011 International conference on signal,image processing and applications（ICSIA 2011）

作者： Ibrahim El rube Maha Sharkas Ahmed Salman Ashor Salem Taif University Arab Academy for Science Technologyand Maritime Transport

In this paper,we present an automated image registration algorithm,which combines the multiscale wavelet transform(WT)with Scale Invariant Feature Transform(SIFT).The control points are selected using three levels ***,the image is decomposed into multi-scale levels using WT,then low frequency(approximation)images are input to SIFT algorithm for automatically obtaining control *** results show the efficiency and the accuracy of the proposed technique for selecting enough and correct control points.

关键词： image registration,SIFT,wavelet transform,correspondence points.

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