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International Conference on Pattern Recognition

作者： De Zhang Yunhong Wang Bir Bhanu Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China Center of Research in Intelligent Systems University of California Riverside CA USA

In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM). A gait database including young people and elderly people is built. To extract appropriate gait features, we consider a contour related method in terms of shape variations during human walking. Then the image feature is transformed to a lower-dimensional space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences. Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification from human gait is feasible and reliable.

关键词： Hidden Markov models Senior citizens Databases Legged locomotion Feature extraction Humans Pixel

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(Semi-)Analytic Gaussian Mixture Filter

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IFAC Proceedings Volumes 2011年第1期44卷 10014-10020页

作者： Marco F. Huber Frederik Beutler Uwe D. Hanebeck Variable Image Acquisition and Processing Research Group Fraunhofer Institute of Optronics System Technologies and Image Exploitation (IOSB) Karlsruhe Germany Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

Abstract In nonlinear filtering, special types of Gaussian mixture filters are a straightforward extension of Gaussian filters, where linearizing the system model is performed individually for each Gaussian component. In this paper, two novel types of linearization are combined with Gaussian mixture filters. The first linearization is called analytic stochastic linearization, where the linearization is performed analytically and exactly, i.e., without Taylor-series expansion or approximate sample-based density representation. In cases where a full analytical linearization is not possible, the second approach decomposes the nonlinear system into a set of nonlinear subsystems that are conditionally integrable in closed form. These approaches are more accurate than fully applying classical linearization.

关键词： State Estimation Nonlinear systems Stochastic systems Decomposition Methods

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An ontology-based emergency domain knowledge model for HTN planning

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IFAC Proceedings Volumes 2013年第13期46卷 377-382页

作者： Dian Liu Hong-wei Wang Kuang-yu Liu Jian Wang Institute of Systems Engineering Huazhong University of Science and Technology P.R.China 430074 Key Laboratory of Education Ministry of Image processing and Intelligent Control Huazhong University of Science and Technology P.R. China 430074 (Tel: *** ) Key Laboratory of Education Ministry of Image processing and Intelligent Control Huazhong University of Science and Technology P.R. China 430074 (Tel: ***

Hierarchy task network (HTN) planning, as one of AI planning approaches, has been widely used in the emergency decision making for action planning in recent years, in which domain knowledge plays an important role. The special and complicated characteristics of emergency domain knowledge make it difficult to model, hindering the application of HTN planning to emergency action plan development. Though ontology modeling can get over the difficulty, existing ontology models for the emergency domain knowledge are either incomplete or not applicable for HTN planning. This paper aims at constructing emergency domain knowledge ontology applicable for HTN planner SHOP2 which can effectively support the emergency action plan development. An approach of translating an emergency domain knowledge model into a SHOP2 domain is also discussed in the paper. Finally an implementation of our work is roughly introduced.

关键词： Emergency action plan development HTN planning SHOP2 emergency domain knowledge ontology translation

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DARK CHANNEL PRIOR-BASED SPATIALLY ADAPTIVE CONTRAST ENHANCEMENT FOR BACK LIGHTING COMPENSATION

DARK CHANNEL PRIOR-BASED SPATIALLY ADAPTIVE CONTRAST ENHANCE...

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Jaehyun Im Inhye Yoon Monson H. Hayes Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul 156-756 Korea

ISBN: (纸本)9781479903573

In this paper, we present a novel contrast enhancement method for backlit images that consists of three steps: i) computation of the transmission coefficients using the dark channel prior, ii) generation of multiple images having different exposures based on the transmission coefficients, and iii) image fusion. Compared to global intensity transformation methods and spatially invariant contrast enhancement algorithms, our approach first extracts under-exposed regions using the dark channel prior map, and then performs spatially adaptive contrast enhancement. As a result, the contrast of the image is increased, especially for backlit scenes and those with very wide dynamic range, while still preserving image details and color.

关键词： contrast enhancement contrast enhancement method TRANSMISSION COEFFICIENT Lighting Backlights image CONTRAST multiple image compensated images wide dynamic range imagery (Psychotherapy)

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Microcalcifications detection from mammographie images based on region growing and variational energy convergence 4

Microcalcifications detection from mammographie images based...

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4th International Conference on Advanced Technologies for Signal and image processing, ATSIP 2018

作者： Touil, Asma Kalti, Karim Solaiman, Basel Mahjoub, Mohamed Ali Hammam Sousse4011 Tunisia Tunisia Laboratory: LATIS- Laboratory of Advanced Technology and Intelligent Systems ENISo Sousse University Sousse4023 Tunisia IMT Atlantique Bretagne-Pays de la Loire Ecoles Mines-Telecom Department of Image and Information Processing Technôpole Brest-Iroise CS 83818 Brest Cedex 329238 France

ISBN: (纸本)9781538652398

In this paper we present a new region growing approach for microcalcification detection in mammographie images. This approach is based on combining a variational approach concept and the Region Growing algorithm in order to correctly delineate the microcalcifications (MCs) that appear in a Region Of Interest (ROI). Its main goal is to make an initial seeds evolve towards a meaningful partition of the ROI, using the minimization of such a region-based energy criterion. The obtained results show encouraging results in terms of reduction of false negative detections and in terms of the delineation of the targeted microcalcifications from the ROIs. © 2018 IEEE.

关键词： image segmentation

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A dual-stream recurrence-attention network with global–local awareness for emotion recognition in textual dialog

arXiv

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arXiv 2023年

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang Wuhan430074 China Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

In real-world dialog systems, the ability to understand the user’s emotions and interact anthropomorphically is of great significance. Emotion Recognition in Conversation (ERC) is one of the key ways to accomplish this goal and has attracted growing attention. How to model the context in a conversation is a central aspect and a major challenge of ERC tasks. Most existing approaches struggle to adequately incorporate both global and local contextual information, and their network structures are overly sophisticated. For this reason, we propose a simple and effective Dual-stream Recurrence-Attention Network (DualRAN), which is based on Recurrent Neural Network (RNN) and Multi-head ATtention network (MAT). DualRAN eschews the complex components of current methods and focuses on combining recurrence-based methods with attention-based ones. DualRAN is a dual-stream structure mainly consisting of local- and global-aware modules, modeling a conversation simultaneously from distinct perspectives. In addition, we develop two single-stream network variants for DualRAN, i.e., SingleRANv1 and SingleRANv2. According to the experimental findings, DualRAN boosts the weighted F1 scores by 1.43% and 0.64% on the IEMOCAP and MELD datasets, respectively, in comparison to the strongest baseline. On two other datasets (i.e., EmoryNLP and DailyDialog), our method also attains competitive results. Copyright © 2023, The Authors. All rights reserved.

关键词： Recurrent neural networks

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InferEM: Inferring the Speaker’s Intention for Empathetic Dialogue Generation

arXiv

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arXiv 2022年

作者： Lv, Guoqing Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China The Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker’s intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human’s psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multitask learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression. Copyright © 2022, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Segmentation-based adaptive spatio-temporal filtering for noise canceling and MPEG pre-processing

Segmentation-based adaptive spatio-temporal filtering for no...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Seong-Won Lee V. Maik Jihoon Jang Jeongho Shin Joonki Paik Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung Ang University Seoul South Korea Research & Development Center Next chip Solution Company Limited Seoul South Korea

Noise reduction especially in low light level images is an important feature in consumer cameras. Existing methods to reduce such noise often degrade image quality due to an improper choice of filters. We present a high quality, low-cost noise reduction filter for enhancing low light level images. The proposed algorithm is simple, effective and computationally fast; it is suitable for low cost camcorders, digital cameras, CCTVs, and surveillance video systems.

关键词： Adaptive filters Filtering Noise cancellation Noise reduction image segmentation Cameras Degradation image quality Costs Video equipment

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Fog images Generation about Unmanned Surface Vessels with Improved Generative Adversarial Network

Fog Images Generation about Unmanned Surface Vessels with Im...

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第42届中国控制会议

作者： Yifeng Tang Zhihui Huang Chuancong Tang Chuanshang Luo Yifan Xu Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

This paper proposes a fog weather data augmentation method for the unmanned surface vessels(USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images.

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USV Path Planning Based on ACO-SA Algorithm

USV Path Planning Based on ACO-SA Algorithm

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第42届中国控制会议

作者： Chuanshang Luo Yifeng Tang Zhihui Huang Yifan Xu Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization ***, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running;change the heuristic function and state transition probability taking three factors into consideration;change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same ***, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm.

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