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IEEE International Conference on Multi Topic

作者： Zeeshan E. Bhatti Usama I. Bajwa Imtiaz A. Taj Vision and Pattern Recognition Systems Research Group Mohammad Ali Jinnah University Islamabad Pakistan Vision and Pattern Recognition Systems Research Group Center for Advanced Studies in Engineering Islamabad Pakistan

Laplacianfaces is a recent addition to appearance based face recognition algorithms with promising future potential. Unlike Eigenfaces algorithm, Laplacianfaces algorithm finds an embedding that preserves the locality information of the subjects in feature space. In this study we have comprehensively evaluated the performance of Laplacianfaces against PCA on FERET face-image database using csuFaceIdEval as the testing platform. The effect of internal parameters, including size of locality to be preserved, the choice of distance measure to determine locality and the number of leading eigenvalues to be used for matching has been thoroughly studied for the first time. The impact of illumination, face expression and age variations on the relative performance of Laplacianfaces and Eigenfaces has been shown to be very significant and best parameter settings for enhanced performance have been proposed.

关键词： Laplace equations Face recognition Eigenvalues and eigenfunctions Embedded computing Face detection pattern recognition Principal component analysis Spatial databases Testing Size measurement

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21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06

作者： Stahl, Armin Gabel, Thomas German Research Center Image Understanding and Pattern Recognition Group Technical University of Kaiserslautern Department of Mathematics and Computer Science Institute of Cognitive Science University of Osnabrück

ISBN: (纸本)1577352815

The definition of accurate similarity measures is a key issue of every Case-Based Reasoning application. Although some approaches to optimize similarity measures automatically have already been applied, these approaches are not suited for all CBR application domains. On the one hand, they are restricted to classification tasks. On the other hand, they only allow optimization of feature weights. We propose a novel learning approach which addresses both problems, i.e. it is suited for most CBR application domains beyond simple classification and it enables learning of more sophisticated similarity measures. Copyright © 2006, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Information retrieval

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Advances in Biometric Person Authentication 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Stan Z. Li Zhenan Sun Tieniu Tan Sharath Pankanti Gérard Chollet David Zhang

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A system for Indian postal automation

A system for Indian postal automation

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International Conference on Document Analysis and recognition

作者： K. Roy S. Vajda U. Pal B.B. Chaudhuri A. Belaid Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India LORIA Research Center Nancy France

In this paper, we present a system towards Indian postal automation based on the recognition of pin-code and city name of the postal document. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and destination address block (DAB) is identified from the document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country, the address part may be written by combination of two scripts. To identify the script by which a word is written, we propose a water reservoir based technique. It is very difficult to identify the script by which the pin-code portion is written. So, we have used two-stage artificial neural network (NN) based general classifiers for the recognition of pin-code digits written in English/Bangla. For recognition of city names, we propose an NSHP-HMM (non-symmetric half plane-hidden Markov model) based technique.

关键词： Automation Cities and towns pattern recognition Seals Water resources Reservoirs Neural networks Natural languages Data mining Image recognition

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems Engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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Skew estimation and correction for form documents using wavelet decomposition

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2nd International Conference on Image Analysis and recognition, ICIAR 2005

作者： Xi, Dihua Kamel, Mohamed Lee, Seong-Whan Pattern Analysis and Machine Intelligence Research Group Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Center for Artificial Vision Research Korea University Seoul Korea Republic of

ISBN: (纸本)3540290699

Form document image processing has become an increasingly essential technology in office automation tasks. One of the problems is that the document image may appear skewed for many reasons. Therefore, the skew estimation plays an important role in any automatic document analysis system. In the past few years, many algorithms have been developed to detect the skew angle of text document images. However, these algorithms suffer from two major deficiencies. Firstly, most of them suppose that the original image is monochrome and therefore they are not suitable to apply to documents with a complicated background. Secondly, most of the current methods were developed for general document images that are not as complicated as form documents. In this paper, we present a new approach to skew detection for grey-level form document images. In our system, image decomposition by 2D wavelet transformations is used to estimate the skew angle. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image processing

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CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild

CodRED: A Cross-Document Relation Extraction Dataset for Acq...

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2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021

作者： Yao, Yuan Du, Jiaju Lin, Yankai Li, Peng Liu, Zhiyuan Zhou, Jie Sun, Maosong Department of Computer Science and Technology Institute for Artificial Intelligence Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China Pattern Recognition Center WeChat AI Tencent Inc

ISBN: (纸本)9781955917094

Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents. However, a large quantity of relational facts in knowledge bases can only be inferred across documents in practice. In this work, we present the problem of cross-document RE, making an initial step towards knowledge acquisition in the wild. To facilitate the research, we construct the first human-annotated cross-document RE dataset CodRED. Compared to existing RE datasets, CodRED presents two key challenges: Given two entities, (1) it requires finding the relevant documents that can provide clues for identifying their relations;(2) it requires reasoning over multiple documents to extract the relational facts. We conduct comprehensive experiments to show that CodRED is challenging to existing RE methods including strong BERT-based models. We make CodRED and the code for our baselines publicly available at https://***/thunlp/CodRED. © 2021 Association for Computational Linguistics

关键词： Extraction

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Local frequency descriptor for low-resolution face recognition

Local frequency descriptor for low-resolution face recogniti...

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IEEE International Conference on Automatic Face and Gesture recognition

作者： Lei, Zhen Ahonen, Timo Pietikainen, Matti Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Chinese Academy of Sciences 95 Zhongguancun Donglu Beijing 100190 China Machine Vision Group University of Oulu FI-90014 Oulun yliopisto Finland

ISBN: (纸本)9781424491407

Face recognition from low-resolution images is a common yet challenging case in real applications. Since the high-frequency information is lost in low-resolution images, it is necessary to explore robust information in the low frequency domain. In this paper, we propose an effective local frequency descriptor (LFD) for low resolution face recognition, by building upon the ideas behind local phase quantization (LPQ) and exploring both blur-invariant magnitude and phase information in the low frequency domain. The proposed descriptor is more descriptive than LPQ with more comprehensive information. In addition, a statistical uniform pattern definition method is introduced to improve the efficiency of the proposed descriptor. Experimental results on FERET and a real video database show that LFD is effective and robust for low-resolution face recognition. © 2011 IEEE.

关键词： Face recognition

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NLP-IISERB@Simpletext2022: To Explore the Performance of BM25 and Transformer Based Frameworks for Automatic Simplification of Scientific Texts

NLP-IISERB@Simpletext2022: To Explore the Performance of BM2...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Saha, Sourav Roy, Dwaipayan Goud, B. Yuvaraj Reddy, Chethan S. Basu, Tanmay Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computational and Data Sciences Indian Institute of Science Education and Research Kolkata India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian Institute of Science Education and research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3. © 2022 Copyright for this paper by its authors.

关键词： Information retrieval

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Arabic-Jawi Scripts Font recognition Using First-Order Edge Direction Matrix

Arabic-Jawi Scripts Font Recognition Using First-Order Edge ...

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2nd International Multi-Conference on Artificial Intelligence Technology, M-CAIT 2013

作者： Bataineh, Bilal Abdullah, Siti Norul Huda Sheikh Omar, Khairuddin Batayneh, Anas Pattern Recognition Research Group Center For Artificial Intelligence Technology Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia Department of Computer Engineering Yarmouk University 21163 Irbid Jordan

ISBN: (纸本)9783642405662

Document image analysis and recognition (DIAR) techniques are a primary application of pattern recognition. OFR is one of the most important DIAR techniques. The information about font type indicates important information to support human knowledge and other document analysis and recognition techniques. In this paper, a new optical font recognition method for Arabic scripts is proposed based on the First order edge direction matrix, which is an effected simple feature extraction method for binary images. The proposed methods based on several recent methods in pre-processing and feature extraction stages. The performance of the proposed method is compared with the previous OFR methods that based on texture analysis methods in the feature extraction stage. The results show that the proposed method presents the best performance than of other methods in terms of computation time and accuracy. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Feature extraction

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