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检索条件"任意字段=12th International Conference on Computing and Pattern Recognition, ICCPR 2023"

共 284 条记录，以下是51-60 订阅

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pattern recognition of Abnormal Left Ventricle Wall Motion in Cardiac MR

Pattern Recognition of Abnormal Left Ventricle Wall Motion i...

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Lu, Yingli Radau, Perry Connelly, Kim Dick, Alexander Wright, Graham Sunnybrook Hlth Sci Ctr Toronto ON M4N 3M5 Canada St Micheals Hosp Cardiol Toronto ON Canada Sunnybrook Hlth Sci Ctr Cardiol Toronto ON Canada

ISBN: (纸本)9783642042706

there are four main problems that limit application of pattern recognition techniques for recognition of abnormal cardiac left ventricle (LV) wall motion: 1) Normalization of the LV's size, shape, intensity level and position;2) defining a spatial correspondence between phases and Subjects;3) extracting features;4) and discriminating abnormal from normal wall motion. Solving these four problems is required for application of pattern recognition techniques to classify the normal and abnormal LV wall motion. In this work, we introduce a normalization scheme to solve the first and second problems. With this scheme, LVs are normalized to the same position, size, and intensity level. Using the normalized images, we proposed an intra-segment classification criterion based on a con-elation measure to solve the third and fourth problems. Application of the method to recognition of abnormal cardiac MR LV wall motion showed promising results.

关键词： pattern recognition

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n-grams of action primitives for recognizing human behavior

n-grams of action primitives for recognizing human behavior

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12th international conference on Computer Analysis of Images and patterns

作者： thurau, Christian Hlavac, Vaclav Czech Tech Univ Fac Elect Engn Dept Cybernet Ctr Machine Percept Prague 12135 2 Czech Republic

ISBN: (纸本)9783540742715

this paper presents a novel approach for behavior recognition from video data. A biologically inspired action representation is derived by applying a clustering algorithm to sequences of motion images. To obey the temporal context, we express behaviors as sequences of n-grams of basic actions. Novel video sequences are classified by comparing histograms of action n-grams to stored histograms of known behaviors. Experimental validation shows a high accuracy in behavior recognition.

关键词： Social sciences computing

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Ontology-based design pattern recognition

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12th international conference on Knowledge-Based Intelligent Information and Engineering Systems

作者： Kirasic, Damir Basch, Danko Information Support Center University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation University of Zagreb Faculty of Electrical Engineering and Computing

ISBN: (纸本)9783540855620

this paper presents ontlology-based architecture for pattern recognition in the context of static source code analysis. the proposed system has three subsystems: parser, OWL ontologies and analyser. the parser subsystem translates the input coded to AST that is constructed as an XML tree. the OWL ontologies define code patterns and general programming concepts. the analyser subsystem constructs instances of the input code as ontology individuals and asks the reasoner to classify them. the experience gained in the implementation of the proposed system and some practical issues are discussed. the recognition system successfully integrates the knowledge representation field and static code analysis. resulting in greater flexibility of the recognition system.

关键词： knowledge-based system ontology-based system static code analysis description logics OWL application formal pattern definition

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the alpha-procedure as an inductive approach to pattern recognition and its connection with lorentz transformation 12th

The alpha-procedure as an inductive approach to pattern reco...

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12th international Scientific and Technical conference Computer Science and Information Technologies, CSIT 2017

作者： Lange, Tatjana University of Applied Sciences Merseburg06217 Germany

ISBN: (纸本)9783319705804

the paper deals with problems which appear when solving the task of pattern recognition in a feature space that is not identical with the space of the unknown decisive key features. In a common sense it deals with the correctness of solutions which are found in different coordinate systems. Even more, the opportunity of constructing a feature space for the final separation of classes by selecting the features in pairs, how it is done by the Alpha-procedure, will be investigated. thereby, the problem of stability during the solving of pattern recognition tasks will be considered from the point of view of transformation groups. the possibility of avoiding the necessity of regularization by using the geometric equiaffine Lorentz transformation will be shown, exploiting for that aim as example the alpha-procedure. © 2018, Springer international Publishing AG.

关键词： Mathematical transformations

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Research on Improved Faster R-CNN in Stacked Artifact recognition 12

Research on Improved Faster R-CNN in Stacked Artifact Recogn...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Han, Weiguang Han, Xuesong University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China

ISBN: (纸本)9798350332919

In order to improve the sorting efficiency of workpieces, improve the sorting quality, reduce the labor intensity of workers and improve the working environment, and at the same time meet the needs of small batch, multi-variety and mixed production, this paper studies the improvement of Faster R-CNN for the field of robotic sorting. Application in stacked workpiece recognition. this paper improves the Faster R-CNN and proposes the Bin-Soft-NMS algorithm based on the Soft-NMS algorithm. the dynamic threshold is used to improve the low recall rate of the model and the low score of the adjacent stacked workpiece bounding box caused by the inaccurate threshold. the problem of inhibition. A stacked workpiece dataset was made by using four kinds of workpieces, and the model was trained on the dataset to complete the experiment. the experimental results show that the average detection accuracy of the improved model in the stacked workpiece scene is 86.26%, and the improvement in mild, moderate and severe scenarios is improved respectively. increased by 1.0%, 3.2% and 4.825%, with an average increase of 3%. © 2023 IEEE.

关键词： Bin-Soft-NMS Faster R-CNN Soft-NMS Stacked artifacts Workpiece identification

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A kind of shape-color moment invariants

A kind of shape-color moment invariants

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12th international conference on Computer-Aided Design and Computer Graphics, CAD/Graphics 2011

作者： Gong, Ming Hu, Ping Cao, Weiguo Li, Hua National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Science Beijing 100190 China

ISBN: (纸本)9780769544977

Shape or color based moment invariants are conventional pattern sensitive features in the object recognition and image description. However, the existing moment invariants are not robust because they handle simplified cases, such as single impact of shape transformation, single color transformation, and a simple combination of them. In this paper, we propose a kind of shape-color moment invariants (SCMIs), taking complicated shape and color transformation into account. It is applicable to more complicated cases, such as changing viewpoints, different illuminations, different camera white balance modes, and different color spaces. the integral-based SCMI construction framework is extensible and general. We evaluate the technique through comprehensive experiments. © 2011 IEEE.

关键词： Color

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Texture of Activities: Exploiting Local Binary patterns for Accelerometer Data Analysis 12

Texture of Activities: Exploiting Local Binary Patterns for ...

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12th international conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Asuroglu, Tunc Acici, Koray Erdas, Cagatay Berke Ogul, Hasan Baskent Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781509056989

recognition of activities through wearable sensors such as accelerometers is a recent challenge in pervasive and ubiquitous computing. the problem is often considered as a classification task where a set of descriptive features are extracted from input signal to feed a machine learning classifier. A major issue ignored so far in these studies is the incorporation of locally embedded features that could indeed be informative in describing the main activity performed by the individual being experimented. To close this gap, we offer here adapting Local Binary pattern (LBP) approach, which is frequently used in identifying textures in images, in one-dimensional space of accelerometer data. To this end, we exploit the histogram of LPB found in each axes of input accelerometer signal as a feature set to feed a k-Nearest Neighbor classifier. the experiments on a benchmark dataset have shown that the proposed method can outperform some previous methods.

关键词： activity recognition accerelometer data wearable sensor classification local binary pattern

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Machine Learning Approaches for Emotion recognition: A Comparative Study 12

Machine Learning Approaches for Emotion Recognition: A Compa...

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12th international conference on Current Trends in Advanced computing, ICCTAC 2024

作者： Singh, Himanshu Singh, Arushi Chauhan, Priyaj Kamboj, Deepika IT KIET Group of Institution Ghaziabad India CSIT KIET Group of Institution Ghaziabad India

ISBN: (纸本)9798350395808

A key component that has arisen in[10] human-computer interaction, artificial intelligence, and affective computing is the ability to recognize emotions. the objective of the research project is to examine a method that is integrated for the recognition of facial expressions of emotion. Humans transmit their emotions through indirect and non-verbal means most of the time. their sentiments are communicated through these methods. Providing a complete overview that includes an assessment of all the important emotion detection methods that can be found in a single region is the objective of the exposition that is now being presented. the paper represents the first attempt to offer an overview of all the emotion detection models that have been developed for the previous 10 years. the document contains an in-depth analysis of the technique as well as the datasets. For face identification, the technique makes use of the local Binary pattern Histogram (LBPH), Convolutional Neural Networks (CNN) for feature extraction, and the Deep Face model for comprehensive emotion analysis. © 2024 IEEE.

关键词： Local binary pattern

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LSTM-based structural pattern recognition for floating bridges 12th

LSTM-based structural pattern recognition for floating bridg...

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12th international conference on Bridge Maintenance, Safety and Management, IABMAS 2024

作者： Ryu, S. Min, S. Kang, Y.J. Kim, S. Korea University Seoul Korea Republic of

ISBN: (纸本)9781032770406

this study introduces a deep learning approach to identify patterns in floating bridges, specifically employing the Long Short-Term Memory (LSTM) algorithm to analyze time-domain data. Wind, wave, and displacement that have been measured in the Bergsøysund bridge between the years 2014 and 2018 were used for the pattern recognition. For the feasibility study, the LSTM-based model was trained to recognize the complicated relation between the variables including the environmental conditions and induced response. After that, the response predicted by the trained model was directly compared to the measured one to evaluate the pattern model. According to this study, the proposed method can effectively recognize the structural pattern of a floating bridge from measured data. In addition, the pattern analysis-based structural health monitoring method can be used for early detection of the structural condition change of the floating bridge. © 2024 the Editor(s).

关键词： Long short-term memory

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Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Novel Supervised Feature Extraction Algorithm Based on Itera...

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12th IEEE international conference on Information Reuse and Integration (IRI)/1st international Workshop on Issues and Challenges in Social computing (WICSOC)

作者： Takeuchi, Yohei Ito, Momoyo Kashihara, Koji Fukumi, Minoru Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9781457709661

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.

关键词： pattern recognition principal component analysis supervised learning

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