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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiasheng Xu Tianyi Zhang Yangqian Wu Jie Yang Guang–Zhong Yang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai CHINA Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CHINA MOE Key Laboratory of System Control and Information Processing Shanghai P.R. China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.

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Suspicious face detection based on key frame recognition under surveillance video

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9712 LNCS卷 645-652页

作者： Zheng, Xiaohui Ning, Yi Chen, Xianjun Zhan, Yongsong Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783319409993

Surveillance video is characterized by large amount of data and redundancy, which makes the suspicious face detection to be a problem. To solve the problem above we proposed suspicious face detection based on key frame. Surveillance video has the type of fixed background, so this paper used the frame difference method with low computational complexity and small computation to extract the key frame. We proposed a new method combined DPM with skin color detection to detect the suspicious face in the key frames. To solve the speed bottleneck of the traditional DPM, we proposed to use the fast HOG LUT feature extraction and the near optimal cost sensitive decision making improving the traditional method. Meanwhile we used YCrCb + otsu skin color segmentation. Since the otsu is easily affected by the illumination, we proposed an improved skin color detection. Experiment results show that the proposed algorithms are robust and accurate for real-time. © Springer International Publishing Switzerland 2016.

关键词： Face recognition

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A Decoupling Paradigm with Prompt Learning for Remote Sensing image Change Captioning

TechRxiv

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TechRxiv 2023年

作者： Liu, Chenyang Zhao, Rui Chen, Jianqi Qi, Zipeng Zou, Zhengxia Shi, Zhenwei Rhe Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Remote sensing image change captioning (RSICC) is a novel task that aims to describe the differences between bitemporal images by natural language. Previous methods ignore a significant specificity of the task: the difficulty of RSICC is different for unchanged and changed image pairs. They process the unchanged and changed image pairs in a coupled way, which usually causes confusion for change captioning. In this paper, we decouple the task into two issues to ease it: whether and what changes have occurred. An image-level classifier performs binary classification to address the first issue. A feature-level encoder contributes to extracting discriminative features to help the caption generation module address the second issue. Besides, for caption generation, we utilize prompt learning to introduce pre-trained large language models (LLMs) into the RSICC task. A multi-prompt learning strategy is proposed to generate a set of unified prompts and a class-specific prompt conditioned on the image-level classifier’s results. The strategy can prompt a pre-trained LLM to know whether changes exist and generate captions. Finally, the multiple prompts and the visual features of the feature-level encoder are fed into a frozen LLM for language generation. Compared with previous methods, our method can leverage the powerful abilities of the pre-trained LLM in language to generate plausible captions, which is free of training. Extensive experiments show that our method is effective and achieves state-of-the-art performance. Besides, an additional experiment demonstrates that our decoupling paradigm is more promising than the previous coupled paradigm for the RSICC task. We will make our codebase publicly available to facilitate future research at https://***/Chen-Yang-Liu/PromptCC © 2023, CC BY-NC-SA.

关键词： Learning systems

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Hierarchical P systems with randomized right-hand sides of rules 18th

Hierarchical P systems with randomized right-hand sides of r...

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18th International Conference on Membrane Computing, CMC 1018

作者： Alhazov, Artiom Freund, Rudolf Ivanov, Sergiu Institute of Mathematics and Computer Science Academy of Sciences of Moldova Academiei 5 Chişinău2028 Moldova Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China Faculty of Informatics TU Wien Favoritenstraße 9–11 Vienna1040 Austria LACL Université Paris Est – Créteil Val de Marne 61 av. Général de Gaulle Créteil94010 France TIMC-IMAG/DyCTiM Faculty of Medicine of Grenoble 5 avenue du Grand Sablon La Tronche38700 France

ISBN: (纸本)9783319733586

P systems are a model of hierarchically compartmentalized multiset rewriting. We introduce a novel kind of P systems in which rules are dynamically constructed in each step by non-deterministic pairing of left-hand and right-hand sides. We define three variants of right-hand side randomization and compare each of them with the power of conventional P systems. It turns out that all three variants enable non-cooperative P systems to generate exponential (and thus non-semi-linear) number languages. We also give a binary normal form for one of the variants of P systems with randomized rule right-hand sides. © 2018, Springer International Publishing AG.

关键词： Hierarchical systems

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Sample Weighted Gradient Regularization for Source-Free Domain Adaptation

SSRN

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SSRN 2024年

作者： Long, Yunchong Xu, Bingrong Zhang, Qingyong Yin, Jianhua Zeng, Zhigang School of Automation Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China The Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

Conventional Domain Adaptation(DA) algorithms typically rely on access to the source data,which may not always be practical due to privacy concerns or limited storage *** address these challenges,Source-Free Domain Adaptation(SFDA) has emerged as an alternative that relies solely on a pre-trained source model rather than the actual source *** SFDA,pseudo-label supervised learning is commonly employed and initially plays a significant role in enhancing model performance during the early stages of target domain ***,as adaptation progresses,the performance can stagnate or even deteriorate due to the noise inherent in pseudo-labels,a phenomenon known as Confirmation *** noise present in target domain pseudo-labels introduces additional uncertainty and overfitting risks to the *** this study,a novel framework named Sample Weighted Gradient Regularization (SWGR) was introduced to tackle this *** first propose the Label Uncertainty Estimation Metric (LUEM),a reweighting strategy that assesses the reliability of *** metric evaluates label uncertainty from both the source domain pre-trained model and spatial distribution of target domain samples,allowing the model to achieve improved cross-domain transfer performance with high-confidence samples in the target domain. Additionally,we impose a constraint on the gradient descent process of the total loss function in the form of an inequality to prevent entropy from *** Gradient Regularization eliminates overfitting while also guiding the model in lowering prediction uncertainty *** approach reduces the uncertainty in the target domain and enhances the model's generalization *** results demonstrate that our method surpasses competing SFDA approaches and even outperforms many traditional DA methods which require extensive amounts of source domain data. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Infectious Probability Analysis on COVID-19 Spreading with Wireless Edge Networks

arXiv

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arXiv 2022年

作者： Li, Xuran Guo, Shuaishuai Dai, Hong-Ning Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing School of Physics and Electronics Shandong Normal University Jinan250061 China School of Control Science and Engineering Shandong University Jinan250061 China Shandong Provincial Key Laboratory of Wireless Communication Technologies Jinan China The Department of Computer Science Hong Kong Baptist University Hong Kong

The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

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International Conference on control and Automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms Networked control systems Reinforcement learning Real-time systems Sensors

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A Study on the Differences of Attitude Learning and Learning Behavior Sequences for University Computer MOOC+ SPOC Course

A Study on the Differences of Attitude Learning and Learning...

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International Conference on Computer Science & Education (ICCSE)

作者： Zhao Changhua Mi Chunqiao School of Computer Science and Engineering Huaihua University Huaihua P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua P. R. China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

The outbreak of COVID-19 make University Computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University Computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University Computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning.

关键词： Interviews Electronic learning Data analysis Encoding Correlation

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Research on face recognition method based on deep learning in natural environment

Research on face recognition method based on deep learning i...

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International Conference on Awareness Science and Technology (iCAST)

作者： Jia Li Yan Longfei Zhang YuFeng Wu Penghui Guo Fuquan Zhang Shuo Tang Gangyi Ding Lin Xu Digital Performance and Simulation Key Laboratory Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjing University Fuzhou China School of Software Beijing Institute of Technology Beijing China Innovative Information Industry Research Institute Fujian Normal University Fuzhou Fujian CN

In the present study, there are a number of recognition methods with high recognition accuracy, which are based on deep learning. However, these methods usually have a good effect in a restricted environment, but in the natural environment, the accuracy of face recognition has decreased significantly, especially in the case of occlusion, face recognition will appear inaccurate or unrecognized situation. Based on this, this paper presents a face recognition method based on the deep learning in the natural environment, hoping to achieve robust performance in the natural environment, especially in the case of occlusion. The main contribution of this paper is improving the method of multi-patches by using 4 areas' patches in the face. And in order to have a higher performance, we use a Joint Bayesian (JB) measure in face-verification. Finally, we trained the model by the set of CASIA-WebFace and test it in the Labeled Faces in the Wild (LFW).

关键词： Face Face recognition Feature extraction Training Databases Bayes methods Conferences

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image Matching Algorithm Under Adverse Conditions Based on SIFT

Image Matching Algorithm Under Adverse Conditions Based on S...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Hao Chen Xinrong Cao Zuoyong Li Lihui Lin College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance.

关键词： Location awareness Matched filters Accuracy image matching Lighting Transforms Feature extraction Robustness Libraries Information technology

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