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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3813卷 251-262页

作者： Aiazzi, Bruno Alparone, Luciano Baronti, Stefano Garzelli, Andrea

A well-suited approach to calculate the fractal dimension of digital images stems from the power spectrum of a fractional Brownian motion: the ratio between powers at different scales is related to the persistence parameter H and, thus, to the fractal dimension D = 3 - H. The signal-dependent nature of the speckle noise, however, prevents from a correct estimation of fractal dimension from Synthetic Aperture Radar (SAR) images. Here, we propose and assess a novel method to obtain D based on the multiscale decomposition provided by the normalized Laplacian pyramid (NLP), which is a bandpass representation obtained by dividing the layers of an LP by its expanded baseband and is designed to force the noise to become signal-independent. Extensive experiments on synthetic fractal textures, both noise-free and noisy, corroborate the underlying assumptions and show the performances, in terms of both accuracy and confidence of estimation, of pyramid methods compared with the well-established method based on the wavelet transform. Preliminary results on true SAR images from ERS-1 look promising as well.

关键词： Synthetic aperture radar

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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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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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wavelet Knowledge Distillation for Efficient Stain Normalization in Digital Histopathology

Wavelet Knowledge Distillation for Efficient Stain Normaliza...

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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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Statistical measurement of ultrasound placenta images using segmentation approach

Statistical measurement of ultrasound placenta images using ...

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International conference on signal and image processing (ICSIP)

作者： G. Malathi V. Shanthi Department of Computer Science Mother Teresa Women''s University Kodaikannal India Department of Computer Applications Saint Joseph''s College of Engineering Chennai India

Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.

关键词： image segmentation Ultrasonic imaging image edge detection Pixel Diabetes Kernel Ultrasonic variables measurement

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On the structure of 2-channel paraunitary finite field multirate filter banks

On the structure of 2-channel paraunitary finite field multi...

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European conference on Circuit Theory and Design, ECCTD

作者： C.M. Lucey C.C. Murphy Department of Elec Engineering University College Cork Ireland

Finite field multirate filter banks can be used to implement the finite field wavelet transform. The finite field wavelet transform has recently found applications in lossless image compression, error control coding and cryptography. Over the real field the polyphase form of the paraunitary multirate filter bank can be factored into lattice structures to reduce implementation complexity. Here we examine the factorisation of the paraunitary polyphase matrix over finite fields of characteristic two, i.e. (GF(2/sup r/)), and identify further improvements when compared to a direct polyphase implementation. Implementation complexity in terms of MPUs and APUs is examined.

关键词： Galois fields Channel bank filters Filter bank Lattices wavelet transforms Finite impulse response filter image coding Error correction Cryptography signal processing

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No-reference blur metric using double-density and dual-tree two-dimensional wavelet transformation

No-reference blur metric using double-density and dual-tree ...

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IEEE National conference on Aerospace and Electronics (NAECON)

作者： Soundararajan Ezekiel Kyle Harrity Erik Blasch Adnan Bubalo Indiana University of Pennsylvania Indiana PA Air Force Research Laboratory Rome NY

Over the past decade the digital camera has become widely available in many devices such as cell phones, computers, etc. Therefore, the perceptual quality of digital images is an important and necessary requirement to evaluate digital images. To improve the quality of images captured with camera, we must identify and measure the artifacts that cause blur within the images. Blur is mainly caused by pixel intensity due to multiple sources. The most common types of blurs are known as object motion, defocus, and camera motion. In the last two decades, the discrete wavelet transformation (DWT) has become a cutting-edge technology in the signal and image processing field for such applications as denoising. The disadvantage of the DWT is that it is not able to directly observe blur coefficients. In this paper, we propose a novel framework for a blur metric for an image. Our approach is based on the double-density dual tree two dimensional wavelet transformations (D3TDWT) which simultaneously processes the properties of both the double-density DWT and dual tree DWT. We also utilize gradient to evaluate blurring artifacts and measure the image quality.

关键词： Measurement Discrete wavelet transforms image edge detection image quality Digital images

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Motion-compensated adaptive wavelet filtering for image sequence processing

Motion-compensated adaptive wavelet filtering for image sequ...

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

作者： J.-P. Leduc J.-M. Odobez C. Labit Campus de Beaulieu Centre INRIA de Rennes Rennes France

The paper presents new approaches in the field of motion-compensated spatio-temporal filters applied to digital image sequences. In time-varying imagery, the temporal correlation information of pixel intensities is folded by motions which may originate from both camera and object displacements. Motion-compensated filters are defined as temporal filters applied along assumed motion trajectories. As a matter of fact, the paper deals with three-dimensional spatio-temporal filters and aims at generalizing the motion-compensated temporal filtering process as the product of two distinct operators. The first operator depends only on the estimated motion parameters derived from both motion-based image segmentations and parametric affine modelings of regions in motion. The second operator analyzes only the correlations of image-by-image intensities measured along the assumed motion trajectories. Multiresolution filters or wavelets may be consequently applied along the motion trajectories to produce optimum and adaptive resulting procedures for purposes like spatio-temporal prediction, interpolation and smoothing. In the paper, applications are provided to cover the field of image sequence coding and interpolation.

关键词： Adaptive filters Filtering image sequences Interpolation Digital filters Digital images Pixel Cameras Motion estimation Parameter estimation

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A Hybrid Multi-focus image Fusion Technique using SWT and PCA

A Hybrid Multi-focus Image Fusion Technique using SWT and PC...

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International conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Tushar Tyagi Parth Gupta Prabhishek Singh Department of CSE Amity University Uttar Pradesh Noida India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal image Quality Index), and PSNR (Peak signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

关键词： conferences Cloud computing Data science Principal component analysis wavelet analysis wavelet transforms

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