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A DIFFERENTIAL OPERATOR TECHNIQUE FOR RESTORING DEGRADED signalS AND imageS

ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE intelligence 1981年第3期3卷 278-284页

作者： OSTREM, JS FALCONER, DG SRI INT CTR ARTIFICIAL INTELL GE KE MENLO PK CA 94025 USA

A technique is described for restoring signals, images, and other physical quantities that have been distorted or degraded by an imperfect measurement system. This technique is based upon the application of a specific differential operator to the measured quantity. For digital implementation, its advantages compared to other restoration techniques are simplicity, computational efficiency, and reduced core memory requirements. Calculations for a one-dimensional example indicate that restorations comparable in quality to Wiener-filter restorations are obtained with better than an order of magnitude decrease in computation time.","doi":"10.1109/TPAMI.1981.4767100","publicationTitle":"ieee Transactions on Pattern Analysis and Machine intelligence","startPage":"278","endPage":"284","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=A+Differential+Operator+Technique+for+Restoring+Degraded+signals+and+images&isbn=&publicationDate=May+1981&author=John+S.+Ostrem&ContentID=10.1109/TPAMI.1981.4767100&orderBeanReset=true&startPage=278&endPage=284&volumeNum=PAMI-3&issueNum=3","displayPublicationTitle":"ieee Transactions on Pattern Analysis and Machine intelligence","pdfPath":"/iel5/34/4767093/***","keywords":[{"type":"ieee Keywords","kwd":["image restoration","signal restoration","Degradation","Deconvolution","Layout","US Department of Transportation","Distortion measurement","Optical distortion","Optical films","Cameras"]},{"type":"Author Keywords ","kwd":["signal processing","Deconvolution","digital image processing","image restoration"]}],"allowComments":false,"pubLink":"/xpl/***?punumber=34","issueLink":"/xpl/***?isnumber=4767093","standardTitle":"A Differential Operator Technique for Restoring Degraded signals and images

关键词： image restoration signal restoration Degradation Deconvolution Layout US Department of Transportation Distortion measurement Optical distortion Optical films Cameras Author Keywords signal processing Deconvolution digital image processing image restoration

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Automatic Tumor Lesion Detection and Segmentation Using Histogram-Based Gravitational Optimization Algorithm

Automatic Tumor Lesion Detection and Segmentation Using Hist...

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Nabizadeh, Nooshin Dorodchi, Mohsen Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA

ISBN: (纸本)9781479945030

In this paper, an automated and customized brain tumor segmentation method is presented and validated against ground truth applying simulated T1-weighted magnetic resonance images in 25 subjects. A new intensity-based segmentation technique called histogram based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. While the mathematical foundation of this algorithm is presented in details, the application of the proposed algorithm in the segmentation of single T1-weighted images (T1-w) modality of healthy and lesion MR images is also presented. The results show that the tumor lesion is segmented from the detected lesion slice with 89.6% accuracy.

关键词： brain segmentation tumor detection magnetic resonance image histogram-based gravitational optimization algorithm

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Using Fuzzy Rough Feature Selection for image Retrieval System

Using Fuzzy Rough Feature Selection for Image Retrieval Syst...

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Lotfabadi, Maryam Shahabi Shiratuddin, Mohd Fairuz Wong, Kok Wai Murdoch Univ Sch Informat Technol Murdoch WA 6150 Australia

ISBN: (纸本)9781467359177

Feature selection is an important step in processing the images especially for applications such as content based image retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve similar images from a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. Thus feature selection is an important step. Fuzzy rough feature selection method has many advantages in determining the relevant features. In this paper, five feature selection methods are compared with the fuzzy rough method. These five feature selection methods are Relief-F, Information Gain, Gain Ratio, OneR and the statistical measure chi(2). The main purpose of the comparison is to rank the image features and see which method provides better results. An image retrieval dataset (COREL dataset) was used in the comparison. In order to evaluate the performance of the six methods, ranking of the important features is defined. This is then used to compare with the automated ranking produced by the aforesaid feature selection methods. Results show that the retrieval system using fuzzy rough feature selection has better retrieval accuracy and provide good Precision Recall performance. The advantages of the use of fuzzy rough feature selection will also be discussed in the paper.

关键词： fuzzy rough set feature selection image retrieval system

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Evolved multiresolution analysis transforms for improved image compression and reconstruction under quantization

Evolved multiresolution analysis transforms for improved ima...

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ieee symposium on computational intelligence in image and signal processing

作者： Babb, Brendan J. Moore, Frank W. Marshall, Pat Univ Alaska Anchorage Dept Math Sci 3211 Providence Dr Anchorage AK 99516 USA Univ So Calif Dept Comp Sci Los Angeles CA USA Univ Hawaii Dept Comp Sci Hilo HI USA

ISBN: (纸本)9781424407071

The research described in this paper uses a genetic algorithm (GA) to evolve wavelet and scaling coefficients for transforms that outperform discrete wavelet transforms (DWTs) under conditions subject to quantization. Compression and reconstruction transform pairs evolved against a representative training image reduce mean squared error (MSE) by more than 22% (1.126 dB) when subsequently applied to test images at a single level of decomposition, while evolved three-level multiresolution analysis (MRA) transforms average more than 11% (0.50 dB) MSE reduction when applied to test images in comparison to the Daubechies-4 (D4) wavelet, without increasing the size of the compressed file.

关键词： Vector quantization

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Variational Inference of Finite Generalized Gaussian Mixture Models

Variational Inference of Finite Generalized Gaussian Mixture...

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ieee symposium Series on computational intelligence (SSCI)

作者： Amudala, Srikanth Ali, Samr Najar, Fatma Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781728124858

This paper presents a variational learning framework to analyze finite g eneralized G aussian m ixture models (GGMM). The model incorporates several mixtures that are widely used in signal and image processing applications. The motivation behind this work is the shape flexibility characteristics of the generalized Gaussian distribution (GGD) because of which it can be applied to different types of data. We present a method to evaluate the posterior distribution and Bayes estimators using the variational expectation-maximization algorithm. The effective number of components of the GGMM is determined automatically. The test results show the adequacy of the proposed algorithm by applying it to medical, astrological, and image segmentation applications;while comparing it with various other approaches.

关键词： generalized gaussian mixture model variational inference taylor approximation binary classification image segmentation

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Multivariate PDF Matching via Kernel Density Estimation

Multivariate PDF Matching via Kernel Density Estimation

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Fantinato, Denis G. Boccato, Levy Attux, Romis Neves, Aline Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Fed Univ ABC Engn Modeling & Appl Social Sci Ctr Sao Andre SP Brazil

ISBN: (纸本)9781479945030

In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.

关键词： Gaussian processes evolutionary computation image processing learning (artificial intelligence) probability Gaussian kernel ITL framework Parzen window method blind equalization scenario computational intelligence paradigm correntropy approach differential evolution extreme learning machines information theoretic learning framework kernel density estimation multivariate PDF matching probability density function quadratic distance statistical dependence computational efficiency Estimation Joints Kernel Measurement Probability density function Vectors

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Endoscope image Analysis Method for Evaluating the Extent of Early Gastric Cancer

Endoscope Image Analysis Method for Evaluating the Extent of...

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Hiroyasu, Tomoyuki Hayashinuma, Katsutoshi Ichikawa, Hiroshi Yagi, Nobuaki Yamamoto, Utako Doshisha Univ Fac Life & Med Sci Kyoto Japan Doshisha Univ Grad Sch Life & Med Sci Kyoto Japan Asahi Univ Murakami Mem Hosp Dept Gastroenterol Gifu Japan

ISBN: (纸本)9781479945030

In this study, a system is proposed to help physicians perform processing on images taken with a magnifying endoscopy with narrow band imaging. In our proposed system, the transition from lesion to normal zone is quantitatively analyzed and presented by texture analysis. Eleven feature values are calculated, i.e., six from a co-occurrence matrix and five from a run length matrix with a scanning window. Integrating these feature values formulates an effective and representative feature value, which is used to draw a color map, so the transition from lesion to normal zone can be visibly illustrated. In this paper, the proposed method is applied to images, and the efficacy is considered. This method is also applied to some rotated images to examine whether it could work effectively on such images.

关键词： cancer endoscopes feature extraction image colour analysis image texture matrix algebra medical image processing color map cooccurrence matrix early gastric cancer endoscopic image analysis image processing representative feature value run length matrix texture analysis Cancer Educational institutions Endoscopes Feature extraction image color analysis Lesions

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An Adaptive Non-Local Total Variation Blind Deconvolution employing Split Bregman Iteration

An Adaptive Non-Local Total Variation Blind Deconvolution em...

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5th International symposium on computational intelligence and Design (ISCID)

作者： Gao, Huijie Zuo, Zhiyong Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China

ISBN: (纸本)9780769548111;9781467326469

Total variation has been used as a popular and effective image prior model in regularization-based image blind restoration, because of its ability to preserve edges. However, as the total variation model favors a piecewise constant solution, the processing results in the flat regions of the image being poor, and it cannot automatically balance the processing strength between different spatial property regions in the image. In this paper, we propose an adaptive non-local total variation image blind restoration algorithm for deblurring a single image via non-local total variation operator, which make full use of the spatially information distributed in the different image regions, and an extended split Bregman iteration is proposed to address the joint minimization problem. Extensive experiments demonstrate the proposed approach produces results superior to most methods in both visual image quality and quantitative measures.

关键词： blind deconvolution deblurring image restoration split Bregman

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Evaluating Sharpness Metrics for HE Stained Blood Cell images 15

Evaluating Sharpness Metrics for HE Stained Blood Cell Image...

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15th ieee International symposium on computational intelligence and Informatics

作者： Windisch, Gergely Kozlovszky, Miklos Obuda Univ John Von Neumann Fac Informat Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary

ISBN: (纸本)9781479953370

image sharpness is a feature that has great influence on image quality. In this paper we present methods to evaluate the sharpness of images, that can be used to select the best image in a sequence of images taken of the same scene using different focus settings. We then execute measurements on 1195 image sets using two sharpness metrics one based on image gradient sums and one based on the edges found in the images. The metrics are evaluated on original noise free images, different amount of noise is added to the images and the metrics are reevaluated in those circumstances as well. Two additional evaluation properties are proposed in this article: roboustness and position of the sharpest image in a miss.

关键词： blood gradient methods image resolution medical image processing HE stained blood cell image image gradient sums image quality image sharpness metrics Gray-scale image color analysis image edge detection Noise Noise level Noise measurement

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Multiresolution superpixels for visual saliency detection

Multiresolution superpixels for visual saliency detection

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Singh, Anurag Chu, Chee-Hung Henry Pratt, Michael A. Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA Univ Louisiana Lafayette William Hansen Hall Dept Elect & Comp Engn Lafayette LA 70504 USA

ISBN: (纸本)9781479945030

Salient regions are those that stand out from others in an image. We present an algorithm to detect salient regions in an image that is represented as superpixels at a number of resolutions. Superpixels are segments generated by oversegmenting an image and they form a perceptually meaningful representation that preserves the underlying image structure. The novelty of our method is the ranking of a superpixel by its dissimilarities with respect to other superpixels and highlighting the statistically salient region proportional to their rank. This is based on the premise that salient region group together and they stand out. We tested our method using standard data sets containing images of varied complexity and compared the results to ground truth data. Our results show that our saliency detection algorithm is robust to changes in color, object size, object location in image and background type.

关键词： image Perception Saliency Foreground-background separation Superpixel Multi-resolution analysis

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