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applications of artificial neural networks in image processing ii 1997

The proceedings contain 17 papers. The topics discussed include: entropy-constrained learning vector quantization algorithms and their application in image compression;predictive vector quantization using neural networks;design of an adaptive genetic learning neural network system for image compression;binary image compression using identity mapping backpropagation neural network;block-predictive image coder of neural network in multiresolution domain;and comparison of ML parameter estimation and neural network classifier for texture classification.

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Recognition and evaluation of cutaneous condition through assorted artificial intelligence reliant algorithms

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Mishra, Manmohan Yadav, Ajay Kumar Mazumdar, Bireshwar Dass Gupta, Prashant K. Panwar, Arvind Bharadwaj, Shivam Department of Computer Application United Institute of Management Prayagraj India School of Computer Science & Engineering Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Prayagraj 203201 India

Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life threatening diseases. The most prioritized step toward restoring health is early illness signs identification. Identifying Cutaneous Condition from clinical images is one of the foremost challenges in medical image investigation. In the presented study we will enlighten the various artificial Intelligence techniques falling under the categories of supervised machine learning including Probabilistic classifier (Naïve Bayes), Statistical algorithm (Logistic Regression), Ensemble learning (Random Decision Trees), Data analysis technique (Convolutional neural Network) and Kernel approach (Support Vector Machine) to identify and classify the cutaneous condition appropriately so that corrective measures of skin treatment can be endow with. The proposed approach entails collecting images as input, preprocessing, segmenting, feature extraction and lastly applying the classification algorithms to derive the Cutaneous Condition categories. Additional trials are conducted using the different approaches as indicated and it was discovered from the suggested tests that the Convolutional neural Network strategy yields the best results overall. The proposed model is trained, tested, and evaluated using the International Skin Imaging Collaboration (ISIC) 2019 challenge dataset and Human Against Machine with 10,000 training images (HAM10000) for the detection of manifold Cutaneous Condition. © Bharati Vidyapeeth's Institute of Computer applications and Management 2025.

关键词： artificial intelligence Confusion matrix Convolutional neural networks Cutaneous condition image processing Machine learning

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Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

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ADVANCED SCIENCE 2025年第1期12卷 e2409568页

作者： Boahen, Elvis K. Kweon, Hyukmin Oh, Hayoung Kim, Ji Hong Lim, Hayoung Kim, Do Hwan Hanyang Univ Dept Chem Engn Seoul 04763 South Korea Hanyang Univ Inst Nano Sci & Technol Seoul 04763 South Korea Hanyang Univ Clean Energy Res Inst Seoul 04763 South Korea Stanford Univ Dept Chem Engn Stanford CA 94305 USA

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

关键词： artificial synapse bio-inspired machine learning neuromorphic sensory system

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ANALOG CMOS IMPLEMENTATION OF CELLULAR neural networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-ANALOG AND DIGITAL SIGNAL processing 1993年第3期40卷 200-206页

作者： BAKTIR, IA TAN, MA Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey

The analog CMOS circuit realization of cellular neural networks with transconductance elements is presented. This realization can be easily adapted to various types of applications in image processing just by choosing the appropriate transconductance parameters according to the predetermined coefficients. The effectiveness of the designed circuits for connected component detection is shown by HSPICE simulations. For ''fixed function'' cellular neural network circuits the number of transistors are reduced further by using multi-input transconductance elements.

关键词： Cellular neural networks Transconductance CMOS analog integrated circuits artificial neural networks Very large scale integration image processing Parallel processing Biological neural networks Voltage Transducers

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Model-based neural network for target detection in SAR images

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IEEE TRANSACTIONS ON image processing 1997年第1期6卷 203-216页

作者： Perlovsky, LI Schoendorf, WH Burdick, BJ Tye, DM DATACUBE INC DANVERSMA 01973

A controversial issue in the research of mathematics of intelligence has been that of the roles of a priori knowledge versus adaptive learning, After discussing mathematical difficulties of combining a priority with adaptivity encountered in the past, we introduce a concept of a model-based neural network, whose adaptive learning is based on a priori models. applications to target detection in SAR images are discussed, We briefly overview the SAR principles, derive relatively simple physics-based models of SAR signals, and describe model-based neural networks that utilize these models. A number of real-world application examples are presented.

关键词： neural networks Object detection Intelligent networks Biological neural networks Mathematical model artificial neural networks artificial intelligence Learning Adaptive systems Target recognition

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Design of an adaptive genetic learning neural network system for image compression 2

Design of an adaptive genetic learning neural network system...

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Conference on applications of artificial neural networks in image processing ii

作者： Jiang, J Butler, D Loughborough Univ. (United Kingdom) Bolton Institute (United Kingdom)

ISBN: (纸本)0819424412

In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neural networks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.

关键词： genetic algorithm neural networks vector quantization image compression and processing

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Predictive vector quantization using neural networks 2

Predictive vector quantization using neural networks

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Conference on applications of artificial neural networks in image processing ii

作者： Hashemi, MR Yeap, TH Panchanathan, S UNIV OTTAWA DEPT ELECT ENGNVISUAL COMP & COMMUN LABOTTAWAON K1N 6N5CANADA

ISBN: (纸本)0819424412

In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression, This technique has been implemented using neural networks. A Kohonen self organized feature may, is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5-10% improvement in coding performance over the existing neural networks based PVQ techniques.

关键词： predictive vector quantization neural networks image compression multimedia

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Medical image analysis with artificial neural networks

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2010年第8期34卷 617-631页

作者： Jiang, J. Trundle, P. Ren, J. Univ Bradford Digital Media & Syst Res Inst Bradford BD7 1DP W Yorkshire England

Given that neural networks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image segmentation and edge detection towards visual content analysts and medical image registration for Its pre-processing and post-processing with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem (ii) how medical images could be analysed processed and characterised by neural networks and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging In the concluding section a highlight of comparison among many neural network applications is Included to provide a global view on computational intelligence with neural networks in medical Imaging (C) 2010 Elsevier Ltd All rights reserved

关键词： neural networks Medical imaging analysis Intelligent computing

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Fuzzy cellular neural network: A new paradigm for image processing

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND applications 1997年第6期25卷 469-481页

作者： Yang, T Yang, LB UNIV CALIF BERKELEY DEPT ELECT ENGN & COMP SCIBERKELEYCA 94720 UNIV CALIF BERKELEY ELECT RES LABBERKELEYCA 94720 UNIV E ZHOU HUBEI 436000PEOPLES R CHINA

A non-linear network structure called the fuzzy cellular neural network (FCNN) is presented. It is a reasonable extension of the cellular neural network (CNN) from classical to fuzzy sets. In this paper, structures of type ii FCNNs are presented. A type ii FCNN has fuzzy signals and crisp synaptic weights. Some theorems on the dynamical range and equilibrium points of type ii FCNNs are presented. applications of type ii FCNNs to min-max medical axis transformation, noise removal and edge detection under a low-SNR condition are presented. Computer simulation results are given. (C) 1997 by John Wiley & Sons, Ltd.

关键词： Cellular neural networks

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Block predictive image coder of neural network in multiresolution domain 2

Block predictive image coder of neural network in multiresol...

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Conference on applications of artificial neural networks in image processing ii

作者： Lou, SQ Huangpu, K Zhou, LZ Wang, JW Xu, GM National Univ. of Defense Technology (China)

ISBN: (纸本)0819424412

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but has not been fully recognized and exploited for general images, This paper presents a new image coder in which the similarity among blocks of different subbands is exploited by block prediction based on neural network. After a pyramid subband decomposition, the detail subbands are partitioned into a set of uniform non-overlapping blocks. In order to speed up the coding procedure and improve the coding efficiency, a new classifying criteria is presented, the blocks are classified into two sets : the simple block set and the edge block set. In our proposed method, the edge blocks are predicted from blocks in lower scale subband with sane orientation through neural network. The simple blocks and predictive edge error blocks are coded with arithmetic coder simulation results show that the method presented in this paper is a promising coding technique which is worth for us to do further research.

关键词： image coding wavelet transform neural network

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