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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 394 条记录，以下是101-110 订阅

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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 11

Multi-Scale Features Integration for Handwritten Mathematica...

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11th international conference on computing and pattern recognition, iccpr 2022

作者： Liu, Xianghao Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method. © 2022 ACM.

关键词： Character recognition

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Reaching near neighbors with far and random proxies

Reaching near neighbors with far and random proxies

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2011 8th international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2011

作者： Chávez, Edgar Ludueña, Veronica Reyes, Nora Roggero, Patricia Universidad Michoacana Morelia Mexico Departamento de Informática Universidad Nacional de San Luis Argentina

ISBN: (纸本)9781457710117

Proximity searching is an algorithmic abstraction covering a large number of applications in areas such as machine learning, statistics, multimedia information retrieval, computer vision and pattern recognition, to name a few. the algorithmic problem consist in preprocessing a set of objects to quickly find the objects near a given query. One of the nicest algorithmic constructions in the proximity searching literature is the Spatial Approximation Tree (SAT), built with the primary design goal of approximating to the query spatially instead of using a divide and conquer approach. A key aspect in building the SAT is the order of insertion of nodes in the tree. In the plain version the nodes are inserted in increasing order of distance to the root, and this order is recursively used in the construction. In this paper we introduce the SAT + which generalizes the SAT by using an arbitrary insertion order. We tested two alternative insertion strategies improving the efficiency of the SAT at searching time. © 2011 IEEE.

关键词： pattern recognition

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StyleVC: Non-Parallel Voice Conversion with Adversarial Style Generalization 26

StyleVC: Non-Parallel Voice Conversion with Adversarial Styl...

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

作者： Hwang, In-Sun Lee, Sang-Hoon Lee, Seong-Whan Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Voice conversion converts the voice while maintaining the language information. It uses two samples to synthesize speech: the source sample is used for content, the target sample is used for style representation. therefore, VC has been progressed to design information flow to disentangle content and style in a speech. However, separated representations are damaged while passing sparse subspace. Besides, VC models suffer from the training-inference mismatch problem: they only use one sample in training. Accordingly, the model extracts inappropriate content and style representation and generates low-quality speech during inference. To address the mismatch scenario problem, we propose a StyleVC, which utilizes adversarial style generalization. First, we propose style generalization, which captures global style representation and restricts the model from copying information. Second, we use a pitch predictor to estimate pitch information according to content and style representation. third, we further use adversarial training to make the model generate more realistic speech. Finally, we demonstrate our proposed model can generate high-quality speech. the experimental results also show that the proposed StyleVC significantly outperforms to extract the desired features and improve audio quality during inference.

关键词： Training Feature extraction pattern recognition Decoding

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Comparative analysis of histograms of oriented gradients and local binary pattern coefficients for facial emotion recognition 8

Comparative analysis of histograms of oriented gradients and...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Subudhiray, Swapna Palo, Hemanta Kumar Das, Niva Mohanty, Sachi Nandan Bhubaneswar India College of Engineering Pune Dept. of Computer Engineering Pune India

ISBN: (纸本)9789380544434

this paper examines the human expressive states dependent on facial pictures utilizing a few viable component extraction methods. It reproduces the K-Nearest Neighbor (k-NN) classifier to approve the adequacy of successful capabilities separated from the Local Binary pattern (LBP) and Histograms of Oriented Gradients (HOG) for the said task. An examination of the strategies has been made dependent on the normal acknowledgment precision of the classifiers utilizing the calculation unpredictability as a compromise. the component extraction methods have been approved for their discriminative force under various preparations for testing information division proportions, Kappa Coefficient, and order time. the LBP has outperformed the HOG include extraction strategy with a normal precision of 79.6% yet remains computationally costly. On the contrary, the HOG method has furnished a lower characterization time with a normal precision of 59.3 % as uncovered from our outcomes. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Emotion recognition

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Contact State Classification in Industrial Robotic Assembly Tasks Based on Extreme Learning Machine 8

Contact State Classification in Industrial Robotic Assembly ...

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8th IEEE Annual international conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER)

作者： Zhang, Sisi Jiang, Qi Li, Yibin Li, Fengming Song, Rui Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

ISBN: (纸本)9781538670576

In industrial robotic assembly process, the work surrounding environment is generally described by Contact State (CS). In order to solve the issue of contact state recognition, contact state classification based upon Extreme Learning Machine (ELM) is proposed in industrial robotic assembly process and extended the neural network to kernel learning in this paper. ELM has excellent performance in accuracy and speed of classification, owing to node generation being independent of training data, and ELM with kernel (ELM kernel), based on the basic classifier ELM, is applied to classify contact state of the complex assembly process. Results have shown that contact state can be recognized by the proposed classification method and the classification performance of ELM-kernel is superior to ELM. In this way, more accurate information of contact states can be provided for the robot favoring the assembly tasks.

关键词： control engineering computing industrial robots learning (artificial intelligence) pattern classification robotic assembly robotic assembly control engineering computing industrial robots Elm Ecosystem models pattern recognition Learning machines Classification Methods CONTACT Contact Information assembly process

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Aspect-based Sentiment Analysis with Graph Convolutional Networks over Dependency Awareness 26

Aspect-based Sentiment Analysis with Graph Convolutional Net...

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

作者： Wang, Xue Liu, Peiyu Zhu, Zhenfang Lu, Ran Shandong Normal Univ Jinan Shandong Peoples R China Shandong Jiaotong Univ Jinan Shandong Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarities of specific aspects in a comment sentence. Nowadays, models based on graph neural networks enhance semantic perception by using dependency relations on dependency graphs to analyze context and aspect words. However, these models ignore the importance of the dependency type information contained in word relations and do not utilize the dependency types to pay attention to semantic information and the noise problem caused by dependency tree parsing error. To solve the above problems, we propose a novel deep dependency-aware graph convolutional networks (DA-GCN) model in this paper. Among them, the DA-GCN establishes interactive relations with multi-head attention and makes use of the grammar information of dependency perception jointly to effectively learn related information from the generated graphs. We introduce multiple conditional random fields fusing structured attention to better capture specific aspect opinion words. Experimental results on five datasets prove the effectiveness and advancement of our proposed model.

关键词： Sentiment analysis Analytical models Semantics Benchmark testing Graph neural networks pattern recognition Grammar

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Improving Persona Understanding for Persona-based Dialogue Generation with Diverse Knowledge Selection 26

Improving Persona Understanding for Persona-based Dialogue G...

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

作者： Wang, Yuanying Xu, Fuyong Wang, Ru Zhu, Zhenfang Liu, Peiyu Lu, Ran Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A significant goal in an open-domain dialogue system is to make chatbots generate more persona coherent responses given a context. To achieve this goal, some researchers attempt to introduce persona information into neural dialogue models. However, these neural dialogue models describe excessively persona traits during the conversation, which still suffer from the problem of generating boring and meaningful responses. In this paper, we divide the open-domain personalized dialogue generation task into two processes, persona recognition and persona fusion. In the persona recognition process, we use the pretraining model to encode the personas and conversation history independently, which is beneficial to the persona information fusion. then, we design a dynamic persona fusion mechanism to effectively mine the relevance of dialogue context and persona information, and dynamically predict whether to incorporate persona features in the process of the dialogues. Our model outperforms with 1.23% in Acc., 0.36% in BLEU, 0.92% in F1, and 0.036% in Distinct than baseline models. the experimental results on the ConvAI2 dataset illustrate that the proposed model is superior to baseline approaches for generating more coherent and persona consistent responses.

关键词： Oral communication Predictive models Chatbots pattern recognition History Boring Task analysis

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DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points 26

DenseGAP: Graph-Structured Dense Correspondence Learning wit...

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

作者： Kuang, Zhengfei Li, Jiaman He, Mingming Wang, Tong Zhao, Yajie Univ Southern Calif Los Angeles CA USA USC Inst Creat Technol Los Angeles CA USA Stanford Univ Stanford CA USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are often insufficient for disambiguating similar regions. And computing the pairwise feature correlation across images is both computation-expensive and memory-intensive. To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points. Specifically, we first propose a graph structure that utilizes anchor points to provide sparse but reliable prior on inter- and intra-image context and propagates them to all image points via directed edges. We also design a graph-structured network to broadcast multi-level contexts via light-weighted message-passing layers and generate high-resolution feature maps at low memory cost. Finally, based on the predicted feature maps, we introduce a coarse-to-fine framework for accurate correspondence prediction using cycle consistency. Our feature descriptors capture both local and global information, thus enabling a continuous feature field for querying arbitrary points at high resolution. through comprehensive ablative experiments and evaluations on large-scale indoor and outdoor datasets, we demonstrate that our method advances the state-of-the-art of correspondence learning on most benchmarks.

关键词： Computer vision Costs Correlation Image edge detection Neural networks Estimation pattern recognition

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Regular Language Prediction from Complete Sample in Grammatical Inference theory - A Literature Survey 8

Regular Language Prediction from Complete Sample in Grammati...

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Senthil Kumar, K. Balakrishnan, Nishaanth Pranav, N. Srmist Dept. of Computing Technology India

ISBN: (纸本)9798350376425

Inferring regular language and the corresponding finite automaton from the given set of samples (both positive and negative) is an important task in grammatical inference theory. the corresponding underlying automaton, viz., Deterministic Finite Automaton, has many applications ranging from lexical analysis of compilers, pattern recognition, image grammar, genetic engineering, etc. © 2024 IEEE.

关键词： Finite automata

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2015 8th international conference on Contemporary computing, IC3 2015

2015 8th International Conference on Contemporary Computing,...

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8th international conference on Contemporary computing, IC3 2015

ISBN: (纸本)9781467379489

the proceedings contain 101 papers. the topics discussed include: an efficient and modified median root prior based framework for PET/SPECT reconstruction algorithm;frequent block access pattern-based replication algorithm for cloud storage systems;gray scale image watermarking using fuzzy entropy and Lagrangian twin SVR in DCT domain;task behaviour inputs to a heterogeneous multiprocessor scheduler;online anomaly detection via class-imbalance learning;extracting academic social networks among conference participants;automatic classification of frogs calls based on fusion of features and SVM;an efficient DCT based image watermarking scheme for protecting distribution rights;an image processing based method to identify and grade conjunctivitis infected eye according to its types and intensity;dimension reduction using spectral methods in fanny for fuzzy clustering of graphs;and hand written digit recognition system for South Indian languages using artificial neural networks.

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