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28th Signal processing and Communications applications conference (SIU)

作者： Coskuner, Yusuf Genc, Yakup Gebze Tekn Univ Bilgisayar Muhendisligi Bolumu Gebze Turkey

ISBN: (纸本)9781728172064

It is very practical to photograph and store documents using mobile phones. However, it is difficult to obtain a quality document image due to creases on the paper and limitations of the camera pose. These produce geometric distortions and irregular shadows on the document image. The rectification of geometric distortions requires an estimate of the 3D shape of the photographed document. In this study, we introduce a new approach that can estimate the 3D shape of the document using artificial neural networks. neural network models extract geometric information from the context of the image to create a 3D shape. In addition, an adaptive thresholding algorithm was used to correct lighting-related distortions. Data reflecting actual document conditions were used to train the neural networks. Therefore, in addition to previous studies, the method can be applied to photograph samples which creased in many different ways and photographed from varying perspectives. Comparative experiments show that the method works well.

关键词： Machine Learning Computer Vision Convolutional neural networks Transfer Learning Document Reconstruction

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FPGA-based artificial neural network using CORDIC modules

FPGA-based artificial neural network using CORDIC modules

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conference on Advanced Signal processing Algorithms, Architectures, and Implementations XVI

作者： Liddicoat, Albert A. Slivovsky, Lynne A. McLenegan, Tim Heyer, Don Calif Polytech State Univ San Luis Obispo Dept Elect Engn San Luis Obispo CA 93407 USA

ISBN: (纸本)0819463922

artificial neural networks have been used in applications that require complex procedural algorithms and in systems which lack an analytical mathematic model. By designing a large network of computing nodes based on the artificial neuron model, new solutions can be developed for computational problems in fields such as image processing and speech recognition. neural networks are inherently parallel since each neuron, or node, acts as an autonomous computational element. artificial neural networks use a mathematical model for each node that processes information from other nodes in the same region. The information processing entails computing a weighted average computation followed by a nonlinear mathematical transformation. Some typical artificial neural network applications use the exponential function or trigonometric functions for the nonlinear transformation. Various simple artificial neural networks have been implemented using a processor to compute the output for each node sequentially. This approach uses sequential processing and does not take advantage of the parallelism of a complex artificial neural network. In this work a hardware-based approach is investigated for artificial neural network applications. A Field Programmable Gate Arrays (FPGAs) is used to implement an artificial neuron using hardware multipliers, adders and CORDIC functional units. In order to create a large scale artificial neural network, area efficient hardware units such as CORDIC units are needed. High performance and low cost bit serial CORDIC implementations are presented. Finally, the FPGA resources and the performance of a hardware-based artificial neuron are presented.

关键词： computer arithmetic field programmable gate arrays (FPGAs) higher-level functions reconfigurable computing digital signal processing and artificial neural networks

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INTEGRATED DETECTION AND SEGMENTATION FOR HYPERSPECTRAL imageRY USING neural networks 4

INTEGRATED DETECTION AND SEGMENTATION FOR HYPERSPECTRAL IMAG...

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4th International conference on applications of artificial neural networks

作者： BROWN, JR DEROUIN, E MCC MICROELECTR & COMP TECHNOL CORPAUSTINTX 78759

ISBN: (纸本)0819412015

The combination of hyperspectral imaging systems and neural networks are changing the approach to the challenging problem of automatic target recognition (ATR). This paper summarizes a research effort to demonstrate the utility of neural networks in processing hyperspectral imagery for target detection and segmentation. Pixel registered imagery containing 32 spectral bands in the 2.0 to 2.5 micrometers range was used to train and test a backpropagation neural network for detection of camouflaged relocatable targets. Initially, neural networks trained and tested using all 32 spectral bands. Because of the high degree of correlation between features (i.e. spectral bands), the dimensionality of the feature set was reduced to 11 spectral bands using both traditional (Karhunen-Loeve) and recently introduced neural network analysis techniques (Ruck's saliency). The neural network was reconfigured and retrained resulting in a probability of correct classification (Pcc) of 99.8%. The neural networks were implemented in hardware on the Intel ETANN chip, a special purpose analog neural network chip. Pixel level classification allows detection and segmentation of targets in parallel. Integrated detection and segmentation (IDS) offers a powerful, alternative approach in an ATR scenario.

关键词： neural networks image segmentation Sensors Target detection Automatic target recognition Hyperspectral imaging image processing Analog electronics artificial neural networks Hyperspectral target detection

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Memristor-based LSTM network with in situ training and its applications

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neural networks 2020年 131卷 300-311页

作者： Liu, Xiaoyang Zeng, Zhigang Wunsch, Donald C., ii Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Appl Computat Intelligence Lab Rolla MO 65401 USA

artificial neural networks (ANNs), such as the convolutional neural network (CNN) and long short-term memory (LSTM), have high complexity and contain large numbers of parameters. Memristor-based neural networks, which have the ability of in-memory and parallel computing, are therefore proposed to accelerate the operations of ANNs. In this paper, a memristor-based hardware realization of long short-term memory (LSTM) network with in situ training is presented. The designed memristor-based LSTM (MbLSTM) network is composed of memristor-based LSTM cell and memristor-based dense layer. Sigmoid and tanh (hyperbolic tangent) activation functions are approximately implemented through intentionally designing circuit parameters. A weight update scheme with row-parallel characteristic is put forward to update the conductance of memristors in crossbars. The highlights of MbLSTM include an effective hardware-based inference process and in situ training. The validity of MbLSTM is substantiated through classification tasks. The robustness of MbLSTM to conductance variations is also analyzed. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Memristor Memristor-based neural network Recurrent neural network Long short-term memory Sequence data processing

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Deep Learning-based image Text processing Research 9

Deep Learning-based Image Text Processing Research

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9th IEEE International conference on Big Data Security on Cloud (BigDataSecurity) / 9th IEEE International conference on High Performance and Smart Computing (IEEE HPSC) / 9th IEEE International conference on Intelligent Data and Security (IEEE IDS)

作者： Xiong, Huixuan Jin, Kai Liu, Jingnian Cai, Jiahong Xiao, Lijun Hunan Univ Sci & Technol Sch Comp Sci & Engn Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410002 Peoples R China Guangzhou Coll Technol & Business Big data Dev & Res Ctr Guangzhou 510850 Peoples R China

ISBN: (纸本)9798350312935

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning image processing text classification artificial Intelligence

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Multi-sensor integration and image recognition using fuzzy adaptive resonance theory 2

Multi-sensor integration and image recognition using fuzzy a...

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conference on applications of artificial neural networks in image processing ii

作者： Singer, SM Naval Air Warfare Ctr. (United States)

ISBN: (纸本)0819424412

The main objective of this work was to investigate the use of ''Sensor Based Real Time Decision and Control Technology'' applied to actively central the arrestment of aircraft (Manned or Unmanned), The proposed method is to develop an adaptively controlled system that would locate the aircraft's extended tailhook, predict its position and speed at the time of arrestment, adjust an arresting end effector to actively mate with the arresting hook and remove the aircraft's kinetic energy, thus minimizing the arresting distance and impact stresses. The focus of the work presented in this paper was to explore the use of Fuzzy Adaptive Resonance Theorem (FuzzyArt) neural Network to form a MSI scheme which reduces image data to recognize incoming aircraft and extended tailhook. Using inputs from several image sources a single fused image was generated to give details about range and tailhook characteristics for an F18 naval aircraft. The idea is to partition an image into cells and evaluate each using FuzzyArt. Once the incoming aircraft is located in a cell that subimage is again divided into smaller cells. This image is evaluated to locate various parts of the aircraft (i.e., wings, tail, tailhook, etc..). The cell that contains the tailhook provides resolved position information. Multiple images from separate sensors provides opportunity to generate range details overtime.

关键词： fuzzy adaptive resonance theory (FuzzyART) arresting gear recursive least squares estimator (RLE) model/reference adaptive control (MRAC) multi-sensor integration (MSI) neural networks pattern recognition path planning aircraft (AC)

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Source localization in resource-constrained sensor networks based on deep learning

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neural COMPUTING & applications 2021年第9期33卷 4217-4228页

作者： Javadi, S. Hamed Guerrero, Angela Mouazen, Abdul M. Univ Ghent Fac Biosci Engn Dept Environm B-9000 Ghent Belgium

Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.

关键词： artificial neural network (ANN) Decentralized detection Deep learning Error type I Error type ii Internet of things (IoT) Source localization Target tracking Wireless sensor networks (WSN)

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neural-NET RANGE image SEGMENTATION FOR OBJECT RECOGNITION 4

NEURAL-NET RANGE IMAGE SEGMENTATION FOR OBJECT RECOGNITION

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4th International conference on applications of artificial neural networks

作者： VILLALOBOS, L MERAT, FL CASE WESTERN RESERVE UNIV CTR AUTOMAT & INTELLIGENT SYST RESDEPT ELECT ENGNCLEVELANDOH 44106

ISBN: (纸本)0819412015

A technique for performing surface-based segmentation of range images using neural nets is introduced. In this approach, multilayered neural nets are used to classify local image patches according to the type of surface they belong to, based on features extracted from range and surface normal information. Central component to the efficiency and robustness is a near orientational invariant local data organization which takes place before features are extracted. This data organization reduces internal complexity by shifting the orientation invariance burden from the dimensionality of the feature spaces and/or from the internal architecture of the networks, to a much simpler sequencing of local data. The result is a well segmented image in which surfaces are properly labeled and delimited, without over segmentation. The approach shows to be robust in front of noise.

关键词： image segmentation neural networks Feature extraction image filtering image processing Natural surfaces Edge detection Network architectures Object recognition Data processing

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neural network-based image resolution enhancement from a multiple of low-resolution images

Neural network-based image resolution enhancement from a mul...

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conference on applications of artificial neural networks in image processing Vii

作者： Salari, E Zhang, S Univ Toledo Dept Elect Engn & Comp Sci Toledo OH 43607 USA Eastern Kentucky Univ Dept Comp Sci Richmond KY 40475 USA

ISBN: (纸本)081944815X

A neural network based image enhancement method is introduced to improve the image resolution from a sequence of low resolution image frames. Most of the existing methods reconstruct a high-resolution image from a multiple of low-resolution image frames by minimizing some established cost function using a mathematical technique. This method, however, uses an integrated recurrent neural network (IRNN) that is particularly designed to be capable of learning an optimal mapping from a multiple of low-resolution image frames to a high-resolution image through training. The IRNN consists of four feed-forward sub-networks working collectively with the ability of having a feedback of information from its output to input. As such, it is capable of both learning and searching the optimal solution in the solution space leading to high resolution images. Simulation results demonstrate that the proposed IRNN has good potential in solving image resolution enhancement problem, as it can adapt itself to the various conditions of the reconstruction problem by learning.

关键词： image resolution enhancement neural networks image sequence analysis

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image coding by cellular neural networks.

Image coding by cellular neural networks.

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conference on applications of artificial neural networks in image processing VI

作者： Montufar-Chaveznava, R Guinea, D García-Alegre, MC Preciado, VM CSIC Inst Automat Ind Dept Syst Madrid 28500 Spain

ISBN: (纸本)0819439835

We present the pyramidal wavelet coder implemented with a Cellular neural Network architecture, as an example of a Cellular neural Network application, considering that some times it is extremely desired the massive and real-time processing and this kind of architecture fits very well for such purposes. The pyramidal wavelet coder works performing the image wavelet transform plus threshold and quantization operations. The wavelet transform consists essentially in a bank of filters, where an image is passed through them repeatedly, and after each filtering a sampling operation is performed. Once image has been filtered and sampled according the rules of the pyramidal image coder, the threshold operation is carried out, where we pretend to keep only the most significant wavelet coefficients. Finally, a quantization operation is performed in order to translate the coefficient values to a discrete environment.

关键词： cellular neural networks image coding wavelets image processing neural network applications

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