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IEEE international conference on Granular computing

作者： Zhu, Huasheng Lei, Jiner Tian, Xiumei NIT Comp Sci & Technol Dept Nanchang 330099 Peoples R China

ISBN: (纸本)9781424425129

A Chinese chessman pattern recognition system is presented. First, system structure is introduced, and chessman image pretreatment is discussed Then primarily discusses the chessman recognition algorithms. Because of direction haphazardry, chessman recognition is quite different from character recognition. This paper presents two kinds of chessman recognition algorithms - RC and concentric circle algorithm for feature extraction. Then choose a relatively suitable recognition algorithm by comparing their time complexity and accuracy.

关键词： computer vision pattern recognition chess algorithm

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Retrieving Relative soft Biometrics for Semantic Identification 23

Retrieving Relative Soft Biometrics for Semantic Identificat...

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23rd international conference on pattern recognition (ICPR)

作者： Martinho-Corbishley, Daniel Nixon, Mark S. Carter, John N. Univ Southampton Sch Elect & Comp Sci Southampton Hants England

ISBN: (纸本)9781509048472

Automatically describing pedestrians in surveillance footage is crucial to facilitate human accessible solutions for suspect identification. We aim to identify pedestrians based solely on human description, by automatically retrieving semantic attributes from surveillance images, alleviating exhaustive label annotation. This work unites a deep learning solution with relative soft biometric labels, to accurately retrieve more discriminative image attributes. We propose a Semantic Retrieval Convolutional Neural Network to investigate automatic retrieval of three soft biometric modalities, across a number of 'closed-world' and 'open-world' re-identification scenarios. Findings suggest that relative-continuous labels are more accurately predicted than absolute-binary and relative-binary labels, improving semantic identification in every scenario. Furthermore, we demonstrate a top rank-1 improvement of 23.2% and 26.3% over a traditional, baseline retrieval approach, in one-shot and multi-shot re-identification scenarios respectively.

关键词： Semantics

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Face recognition based on adaptive soft histogram local binary patterns

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2012 international conference on Service-Oriented computing, ICSOC 2012

作者： Ye, Huixing Hu, Roland Yu, Huimin Damper, Robert Ian Department of Information Science and Electronic Engineering Zhejiang University China Department of Electronics and Computer Science University of Southampton United Kingdom

ISBN: (纸本)9783319029603

In this paper we propose the adaptive soft histogram local binary pattern (ASLBP) for face recognition. ASLBP is an extension of the soft histogram local binary pattern (SLBP). Different from the local binary pattern (LBP) and its variants, ASLBP is based on adaptively learning the soft margin of decision boundaries with the aim to improve recognition accuracy. Experiments on the CMU-PIE database show that ASLBP outperforms LBP and SLBP. Although ASLBP is designed to increase the performance of SLBP, the proposed learning process can be generalized to other LBP variants. © Springer international Publishing 2013.

关键词： Local binary pattern

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One pattern recognition Algorithm Based on Centroids of Fuzzy Hyper-Pyramid Numbers 15th

One Pattern Recognition Algorithm Based on Centroids of Fuzz...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Xu, Yifeng Wang, Guixiang Shen, Chenjie Institute of Operations Research and Cybernetics Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9783030324551

In this paper, a pattern recognition algorithm is given based on centroids of fuzzy hyper-pyramid numbers which are special type fuzzy n-cell numbers. The specific calculation formula (which can be easy calculated by computer program in applications) is obtained for fuzzy hyper-pyramid numbers. Using the results obtained by us, we propose a algorithm of pattern recognition. At last, a practical example is given to demonstrate the application of the algorithm given by us in recognizing uncertain or imprecise multidimensional digital information. © Springer Nature Switzerland AG, 2020.

关键词： pattern recognition

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Massive Training in Artificial Immune recognition Algorithm for Enhancement of Lung CT Scans

Massive Training in Artificial Immune Recognition Algorithm ...

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international conference of soft computing and pattern recognition (SoCPaR)

作者： See Pheng Hang Shamsuddin, Siti Mariyam Ralescu, Anca Univ Teknol Malaysia Soft Comp Res Grp Johor Baharu Malaysia Univ Cincinnati Sch Comp Sci & Informat Cincinnati OH USA

ISBN: (纸本)9781479934003

We proposed a pixel-based machine learning algorithm in the training of artificial immune recognition system (AIRS) to detect lung lesions in two-dimensional computed tomography (CT) scans. AIRS is an immune based algorithm which inspired by several biological mechanisms in mammalian immune system such as mutation, clonal expansion and immune memory generation. The proposed framework implements the concept of pixel machine learning (PML) where no segmentation and features calculation are required in the pre-processing of pixels. Hounsfield (HU) values in the selected region of interest (ROI) in CT scan are used directly to form a large number of learning sub-regions for massive training process. By using raw data in training, the loss of pixel information during detection of abnormality on medical images can be avoided. There are two versions of the AIRS (AIRS1 and AIRS2) algorithms are involved in the experiments of comparing their performance in the classification of medical images. The main advantage of these AIRS algorithms is to remove surplus training data while remain only relevant features in the processing of large amount of data training. The validation of results based on visualization validation and quantitative comparison using Kullback Leibler Divergence (KLD) are introduced. In this research, the massive training AIRS (MTAIRS) algorithms have generated promising results in visualization for lesions enhancement and detection in CT scans.

关键词： massive pixel machine training: artificial immune recognition system lung CT scans

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On soft biometrics

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pattern recognition LETTERS 2015年第Part2期68卷 218-230页

作者： Nixon, Mark S. Correia, Paulo L. Nasrollahi, Kamal Moeslund, Thomas B. Hadid, Abdenour Tistarelli, Massimo Univ Southampton Southampton SO9 5NH Hants England Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal Aalborg Univ Aalborg Denmark Univ Oulu SF-90100 Oulu Finland Univ Sassari I-07100 Sassari Italy

Innovation has formed much of the rich history in biometrics. The field of soft biometrics was originally aimed to augment the recognition process by fusion of metrics that were sufficient to discriminate populations rather than individuals. This was later refined to use measures that could be used to discriminate individuals, especially using descriptions that can be perceived using human vision and in surveillance imagery. A further branch of this new field concerns approaches to estimate soft biometrics, either using conventional biometrics approaches or just from images alone. These three strands combine to form what is now known as soft biometrics. We survey the achievements that have been made in recognition by and in estimation of these parameters, describing how these approaches can be used and where they might lead to. The approaches lead to a new type of recognition, and one similar to Bertillonage which is one of the earliest approaches to human identification. (C) 2015 Elsevier B.V. All rights reserved.

关键词： soft biometrics recognition Surveillance Body Gender Age

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Neonatal Events recognition using LBP descriptor and Wavelet Thresholding Technique

Neonatal Events Recognition using LBP descriptor and Wavelet...

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international conference on Multimedia computing and Systems (ICMCS)

作者： Al Alwani, Adnan Chahir, Youssef Univ Caen Lower Normandy UMR 6072 CNRS GREYC Caen France

ISBN: (纸本)9781479938247

this paper presents an approach for preterm events recognition using local Binary pattern LBP and wavelet thresholding. Unlike conventional approaches which adopt contact measurement of vital signal and develop models for monitoring the preterm in neonatal intensive care unit (NICU), our approach describes non-contact monitoring vital signal based on thermal video as a new premature pain infant profile (PPIP). An interest clinical vital signal is face temperature of neonate. In this study, we collaborate with premature Infant events 'pain and normal states' detection and recognition during daily care monitoring. The proposed approach is composed of a thermal video analysis component and an activity recognition component. A video analysis component contains features descriptor using histogram of Local Binary pattern LBP in the first experiment, and applying wavelet thresholding technique on approximation wave coefficients of histogram feature in the second experiment. Activity recognition contains classification of premature events using classifiers. We present recognition results by considering a variety of classifiers.

关键词： Local Binary pattern (LBP) wavelet thresholding distance metric preterm events recognition

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A new Deep learning model for Sequential pattern Mining using Ensemble learning and Models selection Taking Mobile Activity recognition as a case 16

A new Deep learning model for Sequential Pattern Mining usin...

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16th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 14th international conference on Future Networks and Communications (FNC) / 9th international conference on Sustainable Energy Information Technology

作者： Djellali, Choukri Adda, Mehdi Univ Quebec Rimouski Dept Comp Sci & Engn Rimouski PQ Canada

Sequential pattern mining has gained popularity in Data Mining and pattern recognition. Most sequential pattern mining algorithms are influenced by noisy variables, parameters tuning, bias-variance dilemma and learning instability. This paper presents a new deep learning model for sequential pattern mining, by using ensemble learning and models selection. Experimental studies on mobile activity recognition showed that our deep learning model, which is named Deep Sequential pattern Mining (abbreviated as DeepSPM), obtained an enhanced generalization in comparison with Long Short Term Memory (LSTM), Bidirectional Associative Memory (BAM) and Hopfield. We provide a comparative performance analysis of pattern recognition. The advantages and the drawbacks of the benchmarking models are critically discussed. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Data Mining pattern recognition Deep learning Categorization Artificial Neural Networks Models selection Ensemble learning

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RADIAL HOMOMORPHIC DECONVOLUTION OF B-MODE MEDICAL ULTRASOUND IMAGES 12

RADIAL HOMOMORPHIC DECONVOLUTION OF B-MODE MEDICAL ULTRASOUN...

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conference C on Signal Processing and conference D on Parallel computing, at the 12th IAPR international conference on pattern recognition

作者： TAXT, T UNIV BERGEN MED IMAGE & PATTERN ANAL SECTN-5009 BERGENNORWAY

ISBN: (纸本)0818662751

Describes how homomorphic deconvolution can be used to improve the radial resolution of in vitro and in vivo medical ultrasound images. Each of the recorded radiofrequency ultrasound beams used to form the image was considered as a finite depth sequence of length N, and was weighted with the same exponential depth sequence to create at least some minimum phase sequences. The mean value at each depth sample of the complex cepstrum sequences was computed, and the low depth portion of this mean sequence was taken as the complex cepstrum representation of the ultrasound pulse. It was transformed back to the Fourier frequency domain, and was used to compute the deconvolved echo depth sequence. The method gave substantial improvement in the radial resolution of B-scan images of a tissue mimicking phantom and of human tissues in vivo without significant amplification of the image noise.

关键词： Deconvolution Ultrasonic imaging Biomedical imaging Image resolution In vivo Cepstrum In vitro Radio frequency Pulse amplifiers Back

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VLSI ARRAYS FOR SPEECH PROCESSING WITH LINEAR PREDICTIVE CODING 12

VLSI ARRAYS FOR SPEECH PROCESSING WITH LINEAR PREDICTIVE COD...

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conference C on Signal Processing and conference D on Parallel computing, at the 12th IAPR international conference on pattern recognition

作者： TANG, YY LI, T SUEN, CY CONCORDIA UNIV CTR PATTERN RECOGNIT & MACHINE INTELLIGENCEMONTREALPQ H3G 1M8CANADA

ISBN: (纸本)0818662751

The covariance analysis of linear predictive coding has wide applications, especially in speech recognition and speech signal processing. Real-time applications demand very high processing speed for linear predictive coding analysis. VLSI technology which possesses properties of low-cost, high-speed and massive computing capabilities is a suitable candidate. In this paper, systolic array processors for the covariance analysis of linear predictive coding are developed. The covariance analysis of linear predictive coding contains a large set of irregular and nested recurrence equations. Systolizing the algorithm is a difficulty task for such a complex problem. Existing methods of systematic design for systolic arrays are not much helpful to this problem. To overcome it, a break-combination method is presented in this paper. In this manner, the task is first decomposed and then mapped onto several interconnected systolic arrays. The resulting systolic arrays of the sub problems are then combined to form a complete solution. © 1994 IEEE.

关键词： Systolic arrays

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