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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2638 条记录，以下是231-240 订阅

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An algorithm of face recognition based on generative adversarial networks 41

An algorithm of face recognition based on generative adversa...

引用

conference on applications of Digital image processing XLI

作者： Leonov, Sergey Vasilyev, Alexander Makovetskii, Artyom Diaz-Escobar, J. Chelyabinsk State Univ Dept Math 129 Br Kashirinikh Chelyabinsk 454001 Russia Peter Great St Petersburg Polytech Univ Inst Appl Math & Mech Dept Higher Math 29 Politech Skaya Str St Petersburg 195251 Russia CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510620766

ISBN: (纸本)9781510620766

The problem of face recognition is the important task in the security field, closed-circuit television (CCTV), artificial intelligence, and etc. One of the most effective approaches for pattern recognition is the use of artificial neural networks. In this presentation, an algorithm using generative adversarial networks is developed for face recognition. The proposed method consists in the interaction of two neural networks. The first neural network (generative network) generates face patterns, and the second network (discriminative network) rejects false face patterns. neural network of feed forward type (single-layer or multilayer perceptron) is used as generative network. The convolutional neural network is used as discriminative network for the purpose of pattern selection. A big database of normalized to brightness changes and standardized in scale artificial images is created for the training of neural networks. New facial images are synthesized from existing ones. Results obtained with the proposed algorithm using generative adversarial networks are presented and compared with common algorithms in terms of recognition and classification efficiency and speed of processing.

关键词： artificial neural network generative adversarial network feed forward network convolutional neural network face recognition

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Fourier-Hankel transform used in image raw data preprocessing

Fourier-Hankel transform used in image raw data preprocessin...

引用

conference on applications of artificial neural networks in image processing VI

作者： Hu, CLJ So Illinois Univ Dept Elect & Comp Engn Carbondale IL 62901 USA

ISBN: (纸本)0819439835

For a raw picture data set in either binary or gray-scaled digital form, we can first apply a pixel-quantization method to condense the picture to a much smaller file. Then we can use a math-graphic program such as Microsoft Visual Basic to compute its center of mass (CM). From this CM, we can then construct a polar coordinate with M sectors and N rings. If we apply a normalized Magnitude Fourier Transform (MFT) to these M sectors and a normalized Hankel transform (HT) to these N rings, we will obtain two numerical series truncated at P and Q terms (e.g., P=Q=16). We can then construct a P+Q (or 32) dimension ANALOG vectors. This vector may be used as the pre-processed image vectors for feeding to any neural network (including the noniterative neural networks we presented in the last 8 years) for training and learning.

关键词： Fourier-Hankel transforms neural network preprocessing optimum robustness

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Three applications of pulse coupled neural networks and an optoelectronic hardware implementation

Three applications of pulse coupled neural networks and an o...

引用

conference on applications of artificial neural networks in image processing IV

作者： Banish, M Ranganath, H Karpinsky, J Clark, R Germany, G Richards, P MemsOpt Inc Huntsville AL 35806 USA

ISBN: (纸本)0819431184

Pulse Coupled neural networks have been extended and modified to suit image segmentation applications. Previous research demonstrated the ability of a PCNN to ignore noisy variations in intensity and small spatial discontinuities in images that prove beneficial to image segmentation and image smoothing. This paper describes four research and development projects that relate to PCNN segmentation - three different digital image processing applications: and a CMOS integrated circuit implementation. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3-D SPECT (Single Photon Emission Computed Tomography) images of the heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global aurora images obtained from Ultraviolet imager (WT). The paper also describes the hardware implementation of PCNN algorithm as an electrooptical chip.

关键词： pulse coupled neural networks image segmentation aurora pulmonary embolism ischemia image segmentation

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HIGH-ORDER CURVE RECOGNITION WITH THE HOUGH TRANSFORM USING A CONNECTIONIST APPROACH 2

HIGH-ORDER CURVE RECOGNITION WITH THE HOUGH TRANSFORM USING ...

引用

CONF ON SCIENCE OF artificial neural networks 2

作者： ARRUE, BC INIGO, RM Univ. of Virginia (Spain) Univ. of Virginia (United States)

ISBN: (纸本)0819412023

Extraction of lines and curves from images is one of the most important and fundamental tasks in machine inspection and computer vision in general. Among all the techniques in detecting lines and curves, the Hough Transform (HT) method is unique in its ability to cope effectively with noise, gaps in outlines and even partial occlusion. In spite of this ability, the HT method is still not widely used in real time applications due to its computationally intensive requirements. One solution to this problem is to find an architecture for parallel processing. Recently, some approaches using parallel architectures have been reported. In real-time applications, the overall time required using these approaches is of the order of hundreds of milliseconds for a typical image of 256 X 256 resolution. Clearly, this speed is not good enough for most image processing and machine vision tasks where line detection is just partial work. Since these architectures use commercial components and are not highly parallel, there is ample opportunity to improve the speed by using neural-like analog circuitry. An original method of higher order curve (HOC) detection using the Hough Transform is presented. This method is computationally very efficient and may yield to hardware implementation, thus making it possible to use the Hough Transform in fast real time applications.

关键词： Hough transforms artificial neural networks Content addressable memory Quantization Algorithm development image resolution image segmentation Machine vision neural networks Pattern recognition

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Pose-invariant face-head identification using a bank of neural networks and the 3-D neck reference point

Pose-invariant face-head identification using a bank of neur...

引用

conference on applications of artificial neural networks in image processing Vii

作者： Hild, M Yoshida, K Hashimoto, M Osaka Electrocommun Univ Grad Sch Engn Neyagawa Osaka 5728530 Japan

ISBN: (纸本)081944815X

A method for recognizing faces in relatively unconstrained environments, such as offices, is described. It can recognize faces occurring over an extended range of orientations and distances relative to the camera. As the pattern recognition mechanism, a bank of small neural networks of the multilayer perceptron type is used, where each perceptron has the task of recognizing only a single person's face. The perceptrons are trained with a set of nine face images representing the nine main facial orientations of the person to be identified, and a set face images from various other persons. The center of the neck is determined as the reference point for face position unification. Geometric normalization and reference point determination utilizes 3-D data point measurements obtained with a stereo camera. The system achieves a recognition rate of about 95%.

关键词： face recognition neural network bank face position unification neck reference point

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Classification of the images of gene expression patterns using neural network based on multi-valued neurons with the minimal number of inputs

Classification of the images of gene expression patterns usi...

引用

conference on applications of artificial neural networks in image processing Vii

作者： Aizenberg, I Butakoff, C Myasnikova, E Samsonova, M Reinitz, J NNT Ltd IL-52523 Ramat Gan Israel

ISBN: (纸本)0819444081

Multi-valued neurons (MVN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN. The MVN-based neural networks are applied to temporal classification of images of gene expression patterns, obtained by confocal scanning microscopy. The classification results confirmed the efficiency of this method for image recognition. It was shown that frequency domain of the representation of gene expression images is highly effective for their description.

关键词： neural network multi-valued neuron image recognition

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neural image recognition system with application to tuberculosis detection

Neural image recognition system with application to tubercul...

引用

International conference on Information Technology - Coding and Computing

作者： Ramana, KV Khader, BS JNTUCE CSE Dept Kakinda AP India

ISBN: (纸本)0769521088

Now a days, image recognition systems have several applications in enormous fields. The use of recognition systems (based on artificial neural networks) as means of predicting medical diagnosis and recommending successful treatments has been a highly active research field in past five years. The purpose of this paper is to construct and train an artificial neural network to serve as a knowledge base that can accurately detect Pulmonary Tuberculosis. The first necessary step is to preprocess the different patients MMR's, which consisted of lesions of Tuberculosis and extract the features. Then the extracted features are converted into usable format (gray scale values) and given to neural net for training. It is based on back propagation algorithm.. Then the knowledge base is used to detect a sample collected from a new patient is given as target to recognize it.

关键词： image processing artificial neural networks back propagation algorithm pulmonary tuberculosis

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Random neural network texture model

Random neural network texture model

引用

conference on applications of artificial neural networks in image processing V

作者： Gelenbe, E Hussain, K Abdelbaki, H Univ Cent Florida Sch Comp Sci Orlando FL 32816 USA

ISBN: (纸本)0819435805

This paper presents a novel technique for texture modeling and synthesis using the random neural network (RNM). This technique is based on learning the weights of a recurrent network directly from the texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original one. The proposed texture learning technique is very efficient and its computation time is much smaller than that of approaches using Markov Random Fields. Texture generation is also very fast. We have tested our method with different synthetic and natural textures. The experimental results show that the RNN can efficiently model a large category of homogeneous microtextures. Statistical features extracted from the co-occurrence matrix of the original and the RNN based texture are used to evaluate the quality of fit of the RNN based approach.

关键词： neural networks random neural network texture synthesis texture generation Markov random fields color change mechanism in animals

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Investigation for the Need of Traditional Data-Preprocessing when Applying artificial neural networks to FMCW-Radar Data 29

Investigation for the Need of Traditional Data-Preprocessing...

引用

29th International conference on Systems, Signals and image processing (IWSSIP)

作者： Valti, Jakob Mendez, Javier Mauro, Gianfranco Cabrera, Antonio Issakov, Vadim Infineon Technol AG Neubiberg Germany Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

Robust functionality of autonomous driving vehicles relies on their ability to detect obstables and various scenarios on the road. This can be only achieved by applying robust, fast and efficient AI-based signal processing to radar data. In this work we present an empirical investigation on the question, whether one can apply artificial neural networks (ANNs) directly to frequency modulated continuous wave (FMCW) radar raw data. We show that preproceessing is not necessary if one has enough raw data. In our experiment we have data of 153 648 frames collected with a 60 GHz FMCW radar. We compare systematically the options of preprocessing the data using variational autoencoder, applying traditional preprocessing or omit data-preprocessing and apply ANN directly to raw data. We show that the last option results in 28% faster signal processing and highest accuracy. This is a promising result, since it enables edge computing and direct signal processing at the sensor level.

关键词： artificial neural networks Data preprocessing Radar applications

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Test Automation for Symbol Recognition on the Map 31

Test Automation for Symbol Recognition on the Map

引用

31st IEEE conference on Signal processing and Communications applications (SIU)

作者： Turhan, Fatmanur Carkacioglu, Levent Toreyin, Behcet Ugur Aselsan AS Ankara Turkiye Istanbul Tech Univ Bilisim Enstitusu Istanbul Turkiye

ISBN: (纸本)9798350343557

In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

关键词： Symbol Recognition Feature Extraction Support Vector Machines Template Matching Convolutional neural Network Object Detection Software Testing

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