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Local Spatiotemporal Coding and Sparse Representation based Human Action recognition

Local Spatiotemporal Coding and Sparse Representation based ...

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3rd international conference on Frontiers of Manufacturing Science and Measuring Technology (ICFMM 2013)

作者： Wang, Bin Liu, Yu Wang, Wei Xu, Wei Zhang, Maojun Natl Univ Def Technol Coll Informat Syst & Manage Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783037858462

To handle with the limitation of bag-of-features (BoF) model which ignores spatial and temporal relationships of local features in human action recognition in video, a Local Spatiotemporal Coding (LSC) is proposed. Rather than the exiting methods only uses the feature appearance information for coding, LSC encodes feature appearance and spatiotemporal positions information simultaneously with vector quantization (VQ). It can directly models the spatiotemporal relationships of local features in space time volume (STY). In implement, the local features are projected into sub-space-time-volume (sub-STY), and encoded with LSC. In addition a multi-level LSC is also provided. Then a group of sub-STV descriptors obtained from videos with multi-level LSC and Avg-pooling are used for action video classification. A sparse representation based classification method is adopted to classify action videos upon these sub-STV descriptors. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the previous local spatiotemporal features based human action recognition methods.

关键词： pattern recognition human action recognition feature coding spare representation classification

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Fabric Weave pattern's recognition Based on Texture Orientation Features

Fabric Weave Pattern's Recognition Based on Texture Orientat...

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3rd international conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Shen, Jianqiang Li, Gege Zou, Xuan Li, Yan Univ Shanghai Sci & Technol Shanghai Hamburg Joint Coll Shanghai 200093 Peoples R China

ISBN: (纸本)9783037858882

A novel approach is proposed for representing fabric texture orientations and recognizing weave patterns. Wavelet packet transform is suited for fabric image decomposition in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet packet transform, and the regular orientations can be represented as the energy distributions of these images because the average energies of different fabric texture directions are changeable in a certain way. These energy orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify some complex weaves.

关键词： Wavelet Packet Transform Fabric Weave Texture Orientation Neural Network

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Gaussian-geometric moments and its application in feature matching

Gaussian-geometric moments and its application in feature ma...

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2013 3rd international conference on Advanced Measurement and Test, AMT 2013

作者： Chaoxin, Zhang Ping, Xi Mo, Dai BeiHang University Haidian 100191 Beijing China Institut EGID Université Michel de Montaigne Bordeaux 3 33607 France

ISBN: (纸本)9783037857168

Since the 7 famous Hu's invariants had been introduced in 1960s, the moment invariants play an important role in image analysis and pattern recognition. In this paper, we propose a new moment called Gaussian-Geometric moment, and derived their translation and rotation invariants. One significant conclusion drawn is that the rotation invariants of Gaussian-Geometric moments have the identical forms to those of geometric moments. The Gaussian-Geometric moments and the geometric moments both can represent the image information, difference is that the Gaussian-Geometric moment can represent the center information of an image and the geometric moments represent the edge information of an imageonly. This is particularly evident in the performance of high order ones. Another important property of Gaussian-Geometric moments is that it has a scale parameter which allows choosing the best scale to represent the interest region of an image. A detailed comparison has been made to test the feature matching capability between the proposed moments and the geometric moments. The results show that the proposed moments perform much better than the geometric ones. © (2013) Trans Tech Publications Switzerland.

关键词： pattern recognition

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A New Multiple Classifiers Soft Decisions Fusion Approach for Exons Prediction in DNA Sequences

A New Multiple Classifiers Soft Decisions Fusion Approach fo...

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3rd IEEE international conference on Signal and Image Processing Applications (ICSIPA)

作者： El-Badawy, Ismail M. Aziz, Ashraf M. Gasser, Safa Khedr, Mohamed E. Arab Acad Sci Technol & Maritime Transport Dept Elect & Commun Engn Alexandria Egypt

ISBN: (纸本)9781479902699;9781479902675

Prediction of exons locations in deoxyribonucleic acid (DNA) sequences is a significant issue for biologists. This paper proposes a new method to solve this problem. Unlike the published studies, in which the prediction of exons locations depends on hard decisions from a single classifier, the proposed prediction approach depends on fusion of soft decisions from two classifiers. In the proposed approach we utilize the sliding window discrete Fourier transform (DFT), which is normally used to detect exons 3-base periodicity feature, in a different manner. The novelty here depends on obtaining soft decisions, rather than hard decisions, from two classifiers using different numerical mapping schemes, and fuses them in a decision fusion center to obtain a final global decision about the prediction of exons locations. Simulation results based on real data performed on the HMR195 dataset showed that the proposed soft decisions fusion method achieves better prediction performance compared to the traditional hard decision single classifier method. Moreover the proposed method can easily be extended to more than two classifiers.

关键词： DNA bioinformatics decision making discrete Fourier transforms pattern classification sensor fusion

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Context-Based Information Quality for Sequential Decision Making

Context-Based Information Quality for Sequential Decision Ma...

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3rd IEEE international Multi-Disciplinary conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Rogova, Galina Hadzagic, Melita St-Hilaire, Marie-Odette Florea, Mihai C. Valin, Pierre Encompass Consulting Honeoye Falls NY 14472 USA Univ Montreal Montreal PQ H3C 3J7 Canada OODA Technol Inc Montreal PQ Canada Thales Canada Quebec City PQ Canada Def R&D Canada Valcartier PQ Canada

ISBN: (纸本)9781467324373;9781467324366

The context-dependent nature of information quality has been well recognized in the literature. Depending on the context,the overall information quality may relate to a single quality attribute, a combination of several, or all the attributes. Selection of quality attributes, methods for their combination as well as quality control strategies require consideration of decision makers objectives and functions together with context parameters such as characteristics of the environment and current situations. This paper considers such context-dependent information quality attributes, namely, credibility, reliability, and timeliness, and present same thodofin corporating their combination into sequential decision making for pattern recognition. The context is represented by the time-dependent distance between an observed target and a sensor,and a situation-based time-dependent threshold on credibility. A case study involving an evidential learning system for sequential decision making designed for aquantitative evaluation of the described method, is also presented.

关键词： context information quality sequential decision making information fusion

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Progressive failure analysis of thermoplastic PE/PE composites: Labeling of clustered AE signals

Progressive failure analysis of thermoplastic PE/PE composit...

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3rd international conference on Textile Engineering and Materials (ICTEM 2013)

作者： Yang, Bi Ling Liu, Sen He, Xiao Xia Zheng, Shao Qiong Guangdong Polytech Dept Text Foshan 528041 Guangdong Provi Peoples R China

ISBN: (纸本)9783037858844

Thermoplastic self-reinforced polyethylene (PE/PE) composites were tested under quasi-static tensile load and the failure processes were monitored by Acoustic Emission (AE) technique. The AE signals were collected and clustered by Unsupervised pattern recognition (UPR) scheme. The initiation and progression of the damage mechanisms in the composites can then be reviewed by the cumulative AE hits of each cluster versus strain curves. But the labeling of each cluster is crucial to the failure analysis. The paper focuses on this correlating between the obtained clusters and their specific damage modes. This was carried out by waveform visualization and Fast Fourier Transform analysis. Pure resin and fiber bundles were tested to assist in the labeling of signal classes in the composites (90 degrees, 0 degrees and [+/- 45 degrees] specimens). Typical waveforms of matrix cracking, fiber-matrix debonding, fiber fracture and fiber pullout were indentified respectively. The evolution process of various damage mechanisms in the composites revealed that the correlating method was effective. An objective and repeatable analytical procedure is established for the investigation of progressive failure mechanisms in the thermoplastic composites.

关键词： Self-reinforced Polyethylene Composites Progressive Failure Mechanisms Damage Mode Thermoplastic Acoustic Emission pattern recognition

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Social event detection with robust high-order co-clustering 13

Social event detection with robust high-order co-clustering

引用

3rd ACM international conference on Multimedia Retrieval, ICMR 2013

作者： Bao, Bing-Kun Min, Weiqing Lu, Ke Xu, Changsheng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China China-Singapore Institute of Digital Media Singapore Singapore University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450320337

This paper is devoted to detecting social, real-world events from the sharing images/videos on social media sites like Flickr and YouTube. The fast growing contents make the social media sites become gold mines for social event detection, but we still need to overcome the challenge of processing the associated heterogeneous metadata, such as time-stamp, location, visual content and textual content. Different from the traditional early or late fusion with different types of metadata, we represent them into a star-structured K-partite graph, that is, social media itself is regarded as the central vertices set and different types of metadata are treated as the auxiliary vertices sets which are pairwise independent with each other but correlated with the central one. Based on this graph, Social Event Detection with Robust High-Order Co-Clustering (SED-RHOCC) algorithm is proposed and it includes two steps: 1) coarse event detection, 2) clusters and samples refinement. In the first step, by revealing the inter-relationship on the constructed star-structured K-partite graph and the intra-relationship within some metadata sets such as time-stamp, we co-cluster social media and the associated metadata separately and iteratively to avoid information loss in early/late fusion. After that, a post process is utilized to refine the clusters and social media samples in the second step. MediaEval Social Event Detection Dataset [1] and its subset are selected to demonstrate the effectiveness of our proposed approach in handling the datasets with and without non-event samples. © 2013 ACM.

关键词： Metadata

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Rule Extraction from a Modified FMM Neural Network for Sign Language recognition

Rule Extraction from a Modified FMM Neural Network for Sign ...

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The 3rd international conference on Emerging Trends in Computer and Image Processing(ICETCIP’2013);The 3rd international conference on Advancements in Electronics and Power Engineering(ICAEPE’2013);The 3rd international conference on Trends in Mechanical and Industrial Engineering(ICTMIE’2013);The 3rd international conference on Advances in Biotechnology and Pharmaceutical Sciences(ICABPS’2013);The 3rd international conference on Chemical,Biological and Environment Sciences(ICCEBS’2013);The 3rd international conference on Management,Economics and Social Sciences(ICMESS’2013)

作者： Ho-Joon Kim Seung-Kang Lee the Handong Global University

This paper presents a rule extraction method using a modified FMM neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. A modified hyperbox membership function is introduced and the learning algorithm with the model is presented in this paper. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

关键词： Rule extraction, FMM neural network, sign language recognition, pattern classification

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A framework for recognition of motion events using qualitative knowledge

A framework for recognition of motion events using qualitati...

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IEEE international Advance Computing conference, IACC

作者： Rupam Baruah Shyamanta M Hazarika Computer Science & Engineering Department Tezpur University Tezpur Assam India

ISBN: (纸本)9781467345279

In this paper, we have proposed a framework for representation and recognition of motion events between two or more objects. The framework combines qualitative spatial abstractions with the technique of syntactic pattern recognition in order to recognize motion patterns in an input stream. The key feature of the framework is recognition of motion events among multiple objects and handling of temporal constraints among events.

关键词： Grammar pattern recognition Silicon Syntactics Cognition Production conferences

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The investigation of terahertz metamaterials by normalized power transmission

The investigation of terahertz metamaterials by normalized p...

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2012 3rd international conference on Applied Mechanics and Mechanical Engineering, ICAMME 2012

作者： Luo, Jun Chen, Ying Kang, Shengwu Zhang, Xinyu Xie, Changsheng Zhang, Tianxu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei Province 430074 China College of Sciences Huazhong Agricultural University Wuhan Hubei Province 430070 China Hubei Province Fiber Inspection Bureau Wuhan Hubei Province 430060 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan Hubei Province 430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan Hubei Province 430074 China

ISBN: (纸本)9783037855577

We present the design and fabrication of metamaterials, and investigated the normalized power transmission properties of four metamaterials at different frequency in terahertz regime. We compared the power transmission performances of four types of metamaterials microstructures, and analyzed their transmission differences. © (2013) Trans Tech Publications, Switzerland.

关键词： Power transmission

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