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International conference on artificial Intelligence - applications and Innovations (IC-AIAI)

作者： Gorodilov, Artem Gavrilov, Dmitriy Schelkunov, Dmitriy Moscow Inst Phys & Technol Moscow Russia

ISBN: (纸本)9781728104126

the data transition has become one of the most actual problems nowadays. Classical methods of video and image compression give great results but refers huge computational resources. One of the improvement ways in video and still images processing and transmitting is using neural networks. In this field they can be used for pre-/post-processing, algorithm optimization, segmentation tasks. There is a lack of studies which pay attention to neural network application for reducing spatial and temporal redundancy in video encoding operations. In this work we propose new methods of preprocessing and postprocessing using convolutional neural networks along with classical algorithms of video compression. The main structure consists of downscale and upscale parts which allows to downsize a transmitted image or frame and reconstruct it in decoder side with high accuracy. The reconstruction process uses preprocessing before upscale operation and post-processing after image size restoration. The current algorithm works in conjunction with the classic video codec H.264/AVC. The work's results show improvement of the transmitted image quality at a lower bit rate. The new method improves previous ones in term of compression rate.

关键词： convolutional neural network video compression super-resolution up-/down-scaling

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KNOWLEDGE REPRESENTATION BY DYNAMIC COMPETITIVE LEARNING TECHNIQUES

KNOWLEDGE REPRESENTATION BY DYNAMIC COMPETITIVE LEARNING TEC...

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2ND INTERNATIONAL CONF ON applications OF artificial neural networks

作者： RACZ, J KLOTZ, T Hungarian Academy of Sciences & SZKI Intelligent Software Co (Hungary)

ISBN: (纸本)0819405787

The competitive learning technique is a well-known algorithm used in neural networks which classifies the input vectors, so that the vectors (samples) belonging to the same class have similar characteristics. Each class is represented by one unit. Dynamic competitive learning is an unsupervised learning technique consisting of two additional parts related to conventional competitive learning: a method of generation of new units within a cluster and a method of generating new clusters. As seen in a description of the multilayered neural networks, the number of clusters, their connections, and the generation of new units is determined dynamically during learning. The model is capable of high-level storage of complex data structures and their classification, including exception handling.

关键词： neural networks Data modeling Data storage artificial neural networks image classification Control systems Evolutionary algorithms Machine learning Analog electronics image processing

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Edge-preserved neural network model for image restoration

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JOURNAL OF ELECTRONIC IMAGING 2001年第3期10卷 735-743页

作者： Bao, P Wang, DH Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China

This paper presents a combined approach for image restoration with edge-preserving regularization, subband coding, and artificial neural network. The edge information is detected from the source image as a priori knowledge to recover the details and reduce the ringing artifact of the subband coded image. The multilayer perceptron model is employed to implement the restoration of images. The main merit of the presented approach is that the neural network model is massively parallel with stronger robustness for transmission noise and parameter or structure perturbation, and it can be realized by very large scale integrated technologies for realtime applications. To evaluate the performance of the proposed approach, a comparative study with the set partitioning in hierarchical tree (SPIHT) has been made by using a set of gray-scale digital images. The experiment has shown that the proposed approach could result in considerably better performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image. (C) 2001 SPIE and IS&T.

关键词： neural networks image restoration image compression image quality Computer programming Chromium Network architectures image processing Neurons Quantization

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An overview of deep learning in medical imaging focusing on MRI

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ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK 2019年第2期29卷 102-127页

作者： Lundervold, Alexander Selvikvag Lundervold, Arvid Haukeland Hosp Mohn Med Imaging & Visualizat Ctr MMIV Bergen Norway Western Norway Univ Appl Sci Dept Comp Math & Phys Bergen Norway Univ Bergen Dept Biomed Neuroinformat & Image Anal Lab Bergen Norway Western Norway Univ Appl Sci Dept Hlth & Functioning Bergen Norway

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references;(ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction;(iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

关键词： Machine learning Deep learning Medical imaging MRI

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Classification of interframe difference image blocks for video compression

Classification of interframe difference image blocks for vid...

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conference on applications of artificial neural networks in image processing Vii

作者： Kong, SG Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)0819444081

This paper presents classification of difference image blocks between the two successive image frames for video data compression. Difference blocks are classified to several activity categories according to the image activity distribution. The classification procedure goes in two steps: activity classification and distribution classification. In the activity classification, each interframe difference image block is classified into active or not-active class according to the amount of motion contained in the block. Distribution classification further classifies active image blocks to four activity categories, vertical, horizontal, diagonal, and uniform activities, based on the activity distribution measured by the edge feature vector in the discrete cosine transform domain. A multiplayer feedforward neural network, trained with a small set of sample classification data, successfully classified difference image blocks according to edge feature distribution. The classification scheme improves the performance of video compression at a cost of small increase in the overhead associated with the quantizer switching.

关键词： video compression interframe difference activity classification activity distribution neural networks discrete cosine transform

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neural-NETWORK ATR INDEXING SYSTEM 4

NEURAL-NETWORK ATR INDEXING SYSTEM

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4th International conference on applications of artificial neural networks

作者： GEE, A DORIA, DM LEONARD, JD HUGHES AIRCRAFT CO ELECTRO OPT SYST INFORMAT SYST LABEL SEGUNDOCA 90245

ISBN: (纸本)0819412015

We have developed a novel neural network based automatic target recognition (ATR) indexing system. This system utilizes regularization edge detection, adaptive vector quantization (AVQ) clustering, model driven feedback, and backpropagation trained networks. It can be designed to be invariant to either translation, or translation and rotation. The system incorporates both top-down and bottom-up processing to suppress background clutter.

关键词： neural networks Prototyping Data modeling Automatic target recognition artificial neural networks image processing Edge detection Electro optical modeling Electro optical systems Quantization

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Tube leak detection and isolation in industrial boiler systems

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3722卷 134-143页

作者： Alouani, A.T. Tennessee Technological Univ Cookeville United States

This paper deals with tube leak detection in industrial boilers. A decentralized information processing approach is used to detect and isolate the location of boilers tube leaks. Tube leak sensitive variables (TLSV) are used as the information source for detection and isolation. Such variables are already collected by the system for the purpose of control and monitoring. Given the TLSV, artificial neural networks are used to detect the presence of a leak and its location in the boiler. The proposed approach was successfully applied to tube leak detection and isolation in five subsystems of a utility boiler.

关键词： neural networks

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Automatic edge and target extraction Base on Pulse-Couple Neuron networks Wavelet theory (PCNNW).

Automatic edge and target extraction Base on Pulse-Couple Ne...

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conference on applications of artificial neural networks in image processing Vii

作者： Kya, B Yang, Y Univ Sci & Technol Informat Engn Sch Beijing 100083 Peoples R China

ISBN: (纸本)0819444081

Recent developments in Pulse-Coupled neural networks (PCNN) techniques provide efficiency in edge and target extraction [ 1]. The detection of targets is facilitated by PCNN multi-scale image factorization. But noise is still the enemy of PCNN. An efficient new Pulse-Coupled neural networks technique has been proposed in combination with the wavelet theory. The new Pulse-Couple Neuron Network Wavelet (PCNNW) is based on multi-resolution decomposition for extracting the main features of the images by eliminating the noise. In addition, the wavelet coefficients provide the Pulse-Couple Neuron Network (PCNN) supplemental discrimination and lead to characteristic sets of numbers useful in identifying image factors of interest. The efficiency of the method has been tested and compared with other PCNN denoising methods.

关键词： Pulse-Couple neural networks wavelet theory noise suppression edges detection

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A Fingerprint Recognition Framework Using artificial neural Network 10

A Fingerprint Recognition Framework Using Artificial Neural ...

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10th International conference on Intelligent Systems - Theories and applications (SITA)

作者： Oulhiq, Ridouane Ibntahir, Saad Sebgui, Marouane Guennoun, Zouhair Mohammed V Univ LEC Lab Rabat Morocco Mohammed V Univ EMI Rabat Morocco

ISBN: (纸本)9781509002207

Fingerprinting is one of the most used biometrics for people identification, it relays on image processing and classification algorithms. In this work we propose and test a framework that enables fingerprint detection using a set of image pre-processing algorithm. Concerning the features extraction, we propose the use of the number of bifurcations in image localities, and we propose the use of artificial neural Network (ANN) for the classification. The performance of our framework is evaluated for three different activation functions and show that we can reach an accuracy of 81%.

关键词： fingerprint recognition neural networks minutiae extraction image pre-processing logistic sigmoid

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A novel approach to prevent 9/11/2001 events using artificial neural networks and decision support systems

A novel approach to prevent 9/11/2001 events using artificia...

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conference on Technologies, Systems, and Architectures for Transnational Defense ii

作者： Srinivasan, A Subramani, M Pandya, AS Florida Atlantic Univ Dept Comp Sci & Engn Boca Raton FL 33431 USA

ISBN: (纸本)0819449318

The ability to detect objects from image sequences and estimate their trajectory is useful in many applications like satellite tracking, missile guidance and interception. This paper proposes a reliable and an effective application for preventing loss of lives on event of airline crashes similar to the one on 9/11/2001. This contribution uses the MixeD algorithm for object detection, velocity estimation and the trajectory of the moving object in the spatiotemporal domain. The case study of the 9/11 event shows that the proposed method could have helped the authorities alert the people inside the towers far in advance about the hostile situation and could have saved a few more lives.

关键词： Signal processing Intelligence systems Fourier transforms Fuzzy systems artificial neural networks Decision support systems Fuzzy logic Sensors Rule based systems 3D image processing

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