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International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Phensadsaeng, Pranithan Chamnongthai, Kosin KMUTT Fac Engn Elect & Telecommun Dept Bangkok Thailand

ISBN: (纸本)9781479961207

Tonsillitis disease is one of major causes intervening for heart attack and pneumonia. Nowadays, it is still difficult to make a proper pathological diagnosis of edge detection into the tonsillitis image because borderline grades are very similar to each other and their diagnosis often leads to a considerable variability. To decrease the burden of those diseases, the effective tonsillitis detection system is required. This paper proposes a brief overview of tonsillitis detection system for objective-analysis of the proper identification of tonsil gland by using a combination of gradient and watershed segmentation method as a fundamental tool. The methods include three main steps starting from calculating gradient image value, then using watershed technique based on mean intensity value to segment the image regions and to detect their boundaries, and finally merging two procedures by making image for calculating the number of pixel of each region. The software is implemented using MATLAB. The method used in this study has perform very well and yield better performance as it achieved the accuracy at approximately 90% comparing with results from the medical doctor. Thus, this study is utilized and will be effective tool for diagnosis tonsillitis disease as the result.

关键词： watershed gradient medical image tonsillitis image processing edge-detection

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Widening the remote visualization bottleneck 3

Widening the remote visualization bottleneck

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3rd International symposium on image and signal processing and Analysis (ISPA)

作者： Stegmaier, S Diepstraten, J Weiler, M Ertl, T Univ Stuttgart Visualizat & Interact Syst Grp D-70569 Stuttgart Germany

ISBN: (纸本)953184061X

With the advent of generic remote visualization solutions, accessing distant graphics resources from the desktop is no longer restricted to specially tailored applications. However current solutions still suffer from poor interactivity due to limited network bandwidth and high latency. In this paper we present several improvements to our remote visualization system in order to overcome these drawbacks. Different image compression schemes are evaluated with regard to applicability for scientific visualization, showing that image compression alone cannot guarantee interactive frame rates. Therefore, we furthermore present different techniques for alleviating the bandwidth limit, including quality reduction during user interaction or animations. Latency aspects are addressed by employing multi-threading for asynchronous compression and utilizing features of modern programmable graphics adapters for performing the image compression entirely on the graphics card. The presented ideas are integrated in our remote visualization system without violating universality.

关键词： Analytical models Animation Bandwidth computational modeling Data analysis Data visualization Graphics image coding Interactive systems Personal communication networks

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signal separation method using genetic algorithm

Signal separation method using genetic algorithm

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2nd ieee World Congress on computational intelligence (WCCI 98)

作者： Yoshioka, M Omatu, S Univ Osaka Prefecture Sakai Osaka 591 Japan

ISBN: (纸本)0780348605

In recent years, growing computer networks and multimedia systems require more efficient signal processing methods for independent signal separation or noise reducion. Especially, image noise reduction methods are important to preserve transparency of network communications as a preprocessing filter of the other methods. Some of these noise reduction methods are based on minimizing the dependence among input signals to separate a noise component, because a noise component is usually independent on the other signals. Under such circumstances, we have developed a new method to separate a noise component which directly minimizes the Kullback-Leibler divergence by a genetic algorithm(GA). The Kullback-Leibler divergence is lower when input signals have lower dependence from each other. Therefore, finding the transformation of input signals which minimizes this measure is equivalent to separate independent noise components from the noise mixed input signals. We have adopted a genetic algorithm to minimize the Kullback-Leibler divergence. GA is one of parallel processing optimization methods, which imitates biological genes and is suitable for random optimization problems. Finally, we have performed computer simulations to evaluate the developed method. Results of simulations show that the method is promised but preliminary.

关键词： signal filtering and prediction

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image denoising based on wavelet support vector machine

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International Conference on computational-intelligence and Security

作者： Zhang, Shaoming Chen, Ying Tongji Univ Res Ctr Remote Sensing & Space Informat Technol Shanghai 200092 Peoples R China

ISBN: (纸本)9783540743767

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and is demonstrated by an illustrative example which denoise a, 1-dimension signal with Gauss KBF SVM. The wavelet theory is discussed and applied to construct the wavelet kernel, then the wavelet support vector machine (WSVM) is proposed. The result of experiment shows that the denoising method based on WSVM can reduce noise well, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than Gaussian KBF SVM and other traditional methods.

关键词： image processing

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Extended permutation filters and their application to edge enhancement

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ieee TRANSACTIONS ON image processing 1996年第6期5卷 855-867页

作者： Hardie, RC Barner, KE UNIV DELAWARE DEPT ELECT ENGNAPPL SCI & ENGN LABSNEWARKDE 19716

Extended permutation (EP) filters are defined and analyzed in this paper, In particular, we focus on extended permutation rank selection (EPRS) filters, These filters are constrained to output an order statistic from an extended observation vector, This extended vector includes N observation samples and K statistics that are functions of the observation samples. The rank permutations from selected samples in this extended observation vector are used as the basis for selecting an order statistic output, We show that by including the sample mean in the extended observation vector, the filters exhibit excellent edge enhancement properties, We also show that several previously defined classes of rank-order-based edge enhancers (CS, LUM, and WMMR sharpeners) can be formulated as subclasses of EPRS filters, These sharpening subclasses are in addition to the smoothing subclasses, which include rank conditioned rank selection, permutation, stack, and weighted order statistic filters, Thus, this novel class of filters provides a broad framework within which many rank-order-based smoothers and edge enhancers can be unified. Edge enhancement properties are developed and an L(eta) norm EPRS filter optimization procedure is presented, Finally, extensive computer simulation results are presented, comparing the performance of EPRS and other sharpening filters In edge enhancement applications.

关键词： Nonlinear filters Statistics Paramagnetic resonance Smoothing methods image restoration Computer simulation Application software signal restoration Laboratories computational intelligence Society

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Optimizing Homomorphic Encryption based Secure image Analytics 23

Optimizing Homomorphic Encryption based Secure Image Analyti...

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23rd ieee International Workshop on Multimedia signal processing (ieee MMSP)

作者： Jain, Nayna Nandakumar, Karthik Ratha, Nalini Pankanti, Sharath Kumar, Uttam Int Inst Informat Technol Bangalore Bangalore Karnataka India IBM US Armonk NY 10504 USA Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Univ Buffalo Buffalo NY USA Microsoft Redmond WA USA

ISBN: (纸本)9781665432870

Data privacy is a growing concern as more cloud-based solutions for extracting insights from images are available. Fully Homomorphic Encryption (FHE) is one of the state-of-the-art techniques to enable privacy preserving machine learning. However, encrypted deep neural network-based inference methods face the fundamental challenge of optimizing computational depth and complexity to achieve an acceptable trade-off between accuracy and practical feasibility. Existing works only report high level implementations without providing rigorous analysis of the intricacies involved in the FHE implementation of generic primitive operators of a convolutional neural network (CNN). In this paper, we use the CKKS encryption scheme available in the open-source HElib library to run encrypted inference experiments on the MNIST dataset. The experiments indicate that efficient ciphertext packing schemes, model optimization and multi-threading strategies play a critical role in determining the throughput and latency of the inference process. We also show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters result in significant trade-offs between accuracy, security level and computational time of the machine learning model. The key contribution of the paper is the analysis and recommendation of optimization techniques for efficient encrypted CNN inference.

关键词： Secure image analytics Convolutional neural network fully homomorphic encryption optimization

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Advanced motion estimation techniques for VC-1

Advanced motion estimation techniques for VC-1

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7th ieee International symposium on signal processing and Information Technology

作者： Tammen, Michael J. El-Sharkawy, Mohamed Sliman, Hisham Rizkalla, Maher Purdue Sch Engn & Technol Indianapolis IN 46202 USA

ISBN: (纸本)9781424418343

The SMPTE Standard 421M, commonly known as VC-1, is a state-of-the-art video compression format that provides highly competitive video quality, from very low through very high bit rates, at a reasonable computational complexity. VC-1 has many differences previous standards such as MPEG-2 and H.264/AVC. In this paper, we implement four different motion estimation techniques. These techniques are well known in the video compression world, but have a different effect on each video codec. The effect in VC-1 is shown as an increase in speed and a gain, on average, in the signal-to-noise ratio.

关键词： image coding motion compensation

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A preliminary report on a full-body imaging system for effectively collecting and processing biometric traits of prisoners

A preliminary report on a full-body imaging system for effec...

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2014 ieee symposium Series on computational intelligence, ieee SSCI 2014 - 2014 ieee symposium on computational intelligence in Biometrics and Identity Management, CIBIM 2014

作者： Huynh, Nhat Quang Xu, Xingpeng Kong, Adams Wai Kin Subbiah, Sathyan School of Computer Engineering Nanyang Technological University Singapore Singapore Department of Mechanical Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9781479945344

Because of recent advances in imaging technology, the use of image-based evidences, such as faces and tattoos, is increasing dramatically. Face and tattoo images of prisoners are collected regularly for suspect image database establishment. New biometric traits such as skin marks, androgenic hairs, and blood vessels hidden in color images are getting more attention because they are shown to be useful for criminal and victim identification, especially when their faces and tattoos are neither observable nor available. The current manual approach of collecting images of prisoners is extremely time consuming and does not record these new biometric traits. To address this problem, an unprecedented full-body imaging system is developed. Furthermore, an automatic and systematic routine based on the system for effectively collecting prisoners' images is proposed. This paper concentrates on the system hardware design as well as its image collecting and processing capability. The system has been used to collect and process more than 30,000 infrared and color images from 188 subjects. Its performance is very encouraging. © 2014 ieee.

关键词： Imaging systems

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An effective image halftoning and inverse halftoning technique based on HVS 5

An effective image halftoning and inverse halftoning techniq...

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5th International Conference on computational intelligence and Multimedia Applications

作者： Zhang, XH Liu, F Jiao, LC Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)0769519571

In this paper we propose an effective image halftone and inverse technique based on HVS function. First, a new quantity PPSNR is defined to measure the difference between new image and the original image, at the same time new error diffusion coefficients are constructed to give a halftone image with better texture, contrast, last a new low pass filter is constructed to inverse the halftone with a higher PSNR and PPSNR. The proposed algorithm is significantly simpler than most existing algorithms. We also present experiment result to demonstrate the effectiveness of the proposed algorithm.

关键词： image processing image intensifiers image enhancement image halftoning inverse halftoning HVS PPSNR error diffusion coefficient halftone image low pass filter PSNR

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Application of Neural Networks to Compression of CT images 11

Application of Neural Networks to Compression of CT Images

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11th International symposium on Telecommunications (BIHTEL)

作者： Turajlic, Emir Univ Sarajevo Fac Elect Engn Sarajevo Bosnia & Herceg

ISBN: (纸本)9781509029020

Efficient compression of medical images is needed to decrease the storage space and enable efficient image transfer over network for access of electronic patient records. Since the medical images contain diagnostically relevant information, it is necessary for the process of image compression to preserve high levels of image fidelity, especially when the images are compressed at low bit rates. This paper investigates the capacity of an artificial neural network framework for medical image compression. Specifically, the performance of the proposed image compression method is evaluated on a database of computed tomography images of lungs, where PSNR and MSE are used as the principal image quality metrics. The compressed image data is derived from the hidden layer outputs, where the artificial neural networks are trained to reconstruct the network input features. The results of image block segmentation are used as the network training features. The paper proposes the use of Kohonen's self-organizing maps for segmentation of feature space and the use of multiple finely tuned multi-layer perceptrons to achieve an improved compression performance. This paper presents a study on how the choice of block size, network architecture, and training method affect the compression performance. An attempt is made to optimize the artificial neural network framework for the compression of computed tomography lung images.

关键词： signal processing image processing Artificial intelligence

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