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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

共 786 条记录，以下是71-80 订阅

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Person Identification from Full-Body Movement Using String Grammar Fuzzy-Possibilistic C-Medians 8

Person Identification from Full-Body Movement Using String G...

引用

8th IEEE international conference on Control System, computing and Engineering (ICCSCE)

作者： Chantapakul, Watchanan Auephanwiriyakul, Sansanee theera-Umpon, Nipon Khunlertgit, Navadon Chiang Mai Univ Fac Engn Dept Comp Engn Chiang Mai Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai Thailand Chiang Mai Univ Inst Biomed Engn Chiang Mai Thailand

ISBN: (纸本)9781538663240

Biometrics is a method to identify a person. However, there are several biometric techniques including face recognition, palm recognition, iris recognition, fingerprint recognition, and so on. there is a new approach in biometrics, i.e., identification using full-body movement. In this paper, we introduce a full-body movement in human identification using three Kinects. In particular, we utilize the string grammar fuzzy-possibilistic C-medians (sgFPCNIed) to group string sequences from skeleton frames into pose string, then group the pose string sequences of each person into multi-group to create multi prototypes for each person. the K-nearest neighbor is used to identify the person in the test process on 27 subjects. the system yields 73.33% correct classification on the best validation set of four-fold cross validation.

关键词： person identification syntactic pattern recognition string grammar fuzzy-possibilistic C-medians Kinect

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RBF nets in faults localization

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8th international conference on Artificial Intelligence and Soft computing (ICAISC 2006)

作者： Rafajlowicz, Ewaryst Wroclaw Univ Technol Inst Comp Engn Control & Robot PL-50370 Wroclaw Poland

ISBN: (纸本)3540357483

the task of faults localization is discussed in a model-free setting. As a tool for its solution we consider a multiclass pattern recognition problem with a metric in the label space. then, this problem is approximately solved, providing hints on selecting appropriate RBF nets. It was shown that the approximate solution is the exact one in several important cases. Finally, we propose the algorithm for learning the proposed RBF net. the results of its testing are briefly reported.

关键词： pattern recognition

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Deep Learning-based Method for Classifying and Localizing Potato Blemishes 8th

Deep Learning-based Method for Classifying and Localizing Po...

引用

8th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Marino, Sofia Beauseroy, Pierre Smolarz, Andre Univ Technol Troyes Inst Charles Delaunay M2S FRE 2019 Troyes France

ISBN: (纸本)9789897583513

In this paper we address the problem of potato blemish classification and localization. A large database with multiple varieties was created containing 6 classes, i.e., healthy, damaged, greening, black dot, common scab and black scurf. A Convolutional Neural Network was trained to classify face potato images and was also used as a filter to select faces where more analysis was required. then, a combination of autoencoder and SVMs was applied on the selected images to detect damaged and greening defects in a patch-wise manner. the localization results were used to classify the potato according to the severity of the blemish. A final global evaluation of the potato was done where four face images per potato were considered to characterize the entire tuber. Experimental results show a face-wise average precision of 95% and average recall of 93%. For damaged and greening patch-wise localization, we achieve a False Positive Rate of 4.2% and 5.5% and a False Negative Rate of 14.2% and 28.1% respectively. Concerning the final potato-wise classification, we achieved in a test dataset an average precision of 92% and average recall of 91%.

关键词： Deep Learning Potato Blemishes Classification Localization Autoencoder SVM

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Aspects Regarding the Electromagnetic pattern for the Operating Mechanism of a Medium Voltage Circuit Breaker 8

Aspects Regarding the Electromagnetic Pattern for the Operat...

引用

8th international conference on Modern Power Systems (MPS)

作者： Micu, Marian-Bogdan Adam, Maricel Andrusca, Mihai Deac, Cosmin Gheorghe Asachi Tech Univ Iasi Fac Elect Engn Dept Power Engn Iasi Romania

the paper presents a possibility to determine the electromagnetic pattern for the reinforcement electromotor, for the opening and closing electromagnets of the operating mechanism for a medium voltage circuit breaker ... 详细信息

ISBN: (纸本)9781728107509

关键词： current electromagnetic field operating mechanism circuit breaker

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Haar Cascade Face Detection and Local Binary pattern Histogram Face recognition based Drone 8

Haar Cascade Face Detection and Local Binary Pattern Histogr...

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8th international conference on Advanced computing and Communication Systems, ICACCS 2022

作者： Shanthi, K.G. Sesha Vidhya, S. Vishakha, K. Subiksha, S. Srija, K.K. Srinee Mamtha, R. R.M.K College of Engineering and Technology Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665408165

Face recognition is a popular technique that uses image processing to identify people's faces. Face recognition is becoming momentous due to the growing populace, which necessitates high security and monitoring systems, as well as the need for self confirmation in the current era, fight in rustic regions, disaster relief, and so on. this study proposes a way to assist the task force to track illicit, missing people and for surveillance employing a face recognition based drone. Haar Cascade is a face detection algorithm that is used to make out faces in images and real-time videos. Local Binary pattern Histogram (LBPH) is used to recognize a person's face. the drone yielded an accuracy of 98 percent in the identification of individuals there by finds significant applications. © 2022 IEEE.

关键词： Face recognition

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A Fast Implementation of Adaptive Histogram Equalization

A Fast Implementation of Adaptive Histogram Equalization

引用

2006 8th international conference on Signal Processing

作者： WANG Zhiming TAO Jianhua School of Information Engineering University of Science and Technology Beijing National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)0780397371

Adaptive Histogram Equalization(AHE) is a popular and effective algorithm for image contrast *** it's quite computationally expensive and time *** this paper,a fast implementation of AHE based on pure software techniques is *** accelerative techniques are combined to form the new fast AHE:First,local histogram is acquired by an iterative approach with a sliding window;Second,in computing cumulative histogram function,not more than half of the histogram is cumulated;third,by keep the block size W2 equal to the product of grey level number and integral power of 2,all the multiplication and division operations are replaced with fast bitwise *** theoretical analysis and experimental results demonstrate the proposed algorithm is effective.

关键词： Image Process Contrast Enhancement Histogram Equalization Adaptive Histogram Equalization

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Form factors for mobile computing and device symbiosis

Form factors for mobile computing and device symbiosis

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8th international conference on Document Analysis and recognition (ICDAR 2005)

作者： Narayanaswami, C IBM Corp TJ Watson Res Ctr Hawthorne NY 10598 USA

ISBN: (纸本)0769524206

Several novel portable and wearable form factors have been prototyped at IBM over the last eight years. Each of the different form factors has some unique advantages and addresses different user needs this invited paper will present our experience building these prototypes and describe how improvements in technology have advanced our prototypes over time. Recently we have been investigating the middleware necessary to use a collection of devices symbiotically to address some of the limitations of each form factor. Drawbacks of some form factors include battery, small screen size, limited bandwidth and limited user interface capability. However form factors with such limitations also typically come with several advantages, i.e., they are always with you and provide a private display and a trusted platform. the paper will sketch a few applications that could use devices symbiotically and outline some of the challenges that need to be overcome to enable this symbiosis.

关键词： Mobile computing

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A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN computing 26

A Low-Complexity Approach to Rate-Distortion Optimized Varia...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Datta, Parual Ahuja, Nilesh Somayazulu, V. Srinivasa Tickoo, Omesh Intel Labs Bangalore Karnataka India Intel Labs Santa Clara CA USA Intel Labs Hillsboro OR USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-server (or cloud). Data compression is applied to the intermediate tensor from the DNN that needs to be transmitted, addressing the challenge of optimizing the rate-accuracy-complexity trade-off. Existing split-computing approaches adopt ML-based data compression, but require that the parameters of either the entire DNN model, or a significant portion of it, be retrained for different compression levels. this incurs a high computational and storage burden: training a full DNN model from scratch is computationally demanding, maintaining multiple copies of the DNN parameters increases storage requirements, and switching the full set of weights during inference increases memory bandwidth. In this paper, we present an approach that addresses all these challenges. It involves the systematic design and training of bottleneck units - simple, low-cost neural networks - that can be inserted at the point of split. Our approach is remarkably lightweight, both during training and inference, highly effective and achieves excellent rate-distortion performance at a small fraction of the compute and storage overhead compared to existing methods.

关键词： Training Tensors Systematics Computational modeling Neural networks Memory management Rate-distortion

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Advances in Intelligent Data Analysis VIII 2009

引用

丛书名： Lecture Notes in Computer Science

2009年

作者： Niall M. Adams Céline Robardet Arno Siebes Jean-Francois Boulicaut

ISBN: (数字)9783642039157

ISBN: (纸本)9783642039140

this book constitutes the refereed proceedings of the 8th international conference on Intelligent Data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. the 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

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Improving the accuracy of neural networks in analog computing-in-memory systems by analog weight 26

Improving the accuracy of neural networks in analog computin...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Dai, Lingjun Zhang, Qingtian Wu, Huaqiang Tsinghua Univ Sch Integrated Circuits Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Crossbar-enabled analog computing-in-memory (CACIM) systems can significantly improve the computation speed and energy efficiency of deep neural networks (DNNs). However, an important issue is that the performance of DNNs degrades severely when deploying the DNNs onto the CACIM systems. Because the devices in the CACIM systems have low precision to present the weights, which is caused by the intrinsic variation and high programming overhead. the computational paradigms of the CACIM systems and the digital systems are essentially different. One of the main differences is that the weights are expressed in analog terms, and it has no encoding and decoding process during the computation. We can take advantage of the characteristic of data presentation to get better performance in limited data precision. A generalized quantization method that does not constrain the range of quanta and can obtain less quantization error will be effective in the CACIM systems. For the first time, we introduced a generalized quantization method into CACIM systems and showed superior performance on a series of computer vision tasks, such as image classification, object detection, and semantic segmentation. Using the generalized quantization method, the DNN with 8-level analog weights can outperform the 32-bit networks. With fewer levels, the generalized quantization method can obtain less accuracy loss than other uniform quantization methods.

关键词： Degradation Weight measurement Performance evaluation Quantization (signal) Neural networks Programming Energy efficiency

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