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2022 International Symposium on Design Studies and Intelligence Engineering, DSIE 2022

作者： Chen, Long Liu, Lijuan Yuan, Lei College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781643683720

Passenger flow prediction is of great significance for public transportation. Most of the existing studies mainly predict the flow for a single station only extracting temporal features without considering spatial features. Passenger flow predicting for multiple stations or even the whole network is beneficial to grasp the overall situation, which has more research value in practical application. Thus, a passenger flow prediction model for multiple stations has been proposed based on spatio-Temporal attention mechanism. This model is applied to Xiamen bus rapid transit (BRT) using the time granularity of 5-min. The experimental results demonstrate that our model improves the prediction accuracy compared to the baselines. © 2023 The authors and IOS Press.

关键词： Forecasting

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Image Segmentation of Endoscopic Cataract Based on Semantic Segmentation 6

Image Segmentation of Endoscopic Cataract Based on Semantic ...

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2022 6th International Conference on Machine vision and Information Technology, CMVIT 2022

作者： Chen, Jingshi Ye, Haili Yin, Huayi Wang, Da-Han Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

Medical imaging technology has become one of the indispensable computer-assisted intervention methods in clinical disease diagnosis and treatment, including identifying and locating lesion areas, detecting and segmenting tissue and organ lesions in different modalities. The wide application of medical image analysis in clinical examination and medical auxiliary diagnosis has effectively improved the diagnostic efficiency of doctors. In this paper, we propose a novel and effective model to achieve semantic segmentation of cataract datasets. This model uses the self-supervised method BYOL for parameter pre-training, which improves the model's ability to extract image consistency features. In addition, we have added a lightweight Coordinate attention mechanism to the backbone network to enable the model to independently learn the correlation between the channel and the space and enhance the ability of network feature expression. Experiments are conducted on the cataract endoscopy fine-grained segmentation data set, showing the effectiveness of the proposed method for segment the organs and surgical instruments in the cataract surgical microscope image, which demonstrates the accuracy and robustness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Endoscopy

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Cognitive Science and Artificial Intelligence 1

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丛书名： SpringerBriefs in Applied Sciences and Technology

1000年

作者： Sasikumar Gurumoorthy Bangole Narendra Kumar Rao Xiao-Zhi Gao

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A Novel Clustering Model TEC for Station Classification 37th

A Novel Clustering Model TEC for Station Classification

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Yuan, Lei Liu, Lijuan Zhu, Shunzhi Chen, Long College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9783031290558

Station classification plays an important role in urban intelligent transportation systems. Recently, most of studies usually choose different features to analyze the function of stations. Moreover, they usually choose K-means as the clustering method, which may lead to unstable clustering results. Therefore, a novel clustering model (Transformer Encoder Clustering, TEC) has been proposed for station classification by only using passenger flow data. Firstly, transformer encoder has been applied to deeply extract the features from original passenger flow. Then, an improved Canopy + has been proposed to determine the initial centers for K-means. Finally, a novel evaluation metric (NR) has been introduced to verify the stability and volatility of station classification results. Experiments are conducted on Xiamen bus rapid transit ridership dataset. The results show that the 44 stations have been classified into three categories, and the NRs in all stations are 0.75, which verifies the stability of results for station classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intelligent systems

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ResAsapp: An Effective Convolution to Distinguish Adjacent Pixels For Scene Text Detection 11

ResAsapp: An Effective Convolution to Distinguish Adjacent P...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Weng, Kangming Du, Xia Chen, Kunze Wang, Dahan Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450397056

The segmentation-based approach is an essential direction of scene text detection, and it can detect arbitrary or curved text, which has attracted the increasing attention of many researchers. However, extensive research has shown that the segmentation-based method will be disturbed by adjoining pixels and cannot effectively identify the text boundaries. To tackle this problem, we proposed a ResAsapp Conv based on the PSE algorithm. This convolution structure can provide different scale visual fields about the object and make it effectively recognize the boundary of texts. The method's effectiveness is validated on three benchmark datasets, CTW1500, Total-Text, and ICDAR2015 datasets. In particular, on the CTW1500 dataset, a dataset full of long curve text in all kinds of scenes, which is hard to distinguish, our network achieves an F-measure of 81.2%. © 2022 ACM.

关键词： Pixels

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Learning to Predict Context-Adaptive Convolution for Semantic Segmentation 16th

Learning to Predict Context-Adaptive Convolution for Semanti...

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16th European Conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Hong Kong

ISBN: (纸本)9783030585945

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Robust stereo on multiple resolutions

Robust stereo on multiple resolutions

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13th International Conference on pattern recognition, ICPR 1996

作者： Menard, Christian Leonardis, Aleš Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 3A-1040 Vienna Austria University of Ljubljana Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia

ISBN: (纸本)081867282X

Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent postprocessing stages. In this paper we present a modification of the standard area-based correlation approach so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. The confidence measure of the correlation and the number of outliers provide two complementary sources of information which, when implemented in a multiresolution framework, result in a robust and efficient method. We present the results of this approach on a number of synthetic and real images. © 1996 IEEE.

关键词： Stereo image processing

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Text-Video Knowledge Guided Prompting for Weakly Supervised Temporal Action Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期35卷 4184-4197页

作者： Shao, Yuxiang Zhang, Feifei Xu, Changsheng Tianjin University of Technology Tianjin300384 China Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Peng Cheng Laboratory Shenzhen518066 China

Weakly supervised temporal action localization (WTAL) aims to localize action instances with only video-level labels for supervision. Recent methods convert category labels to natural language through prompting and utilize pre-trained vision-language models to generate text representation from natural language for supervision. This is because natural language can provide more prosperous and generalized semantic supervision to compensate for the lack of supervision in weakly supervised scenarios. However, it should be noted that current prompting methods face limitations in generating dynamic prompts that adapt to each video, which leads to difficulties in accurately aligning text and video representations. In this work, we propose a novel Text-Video Knowledge Guided Prompting (TVKP) framework for WTAL, which generates video-aware prompts based on text-video knowledge to enhance semantic alignment between text and video representations and introduce more video-related external category labels to enrich semantic supervision. We introduce the video-aware prompting (VAP) module to learn text-video knowledge from the joint distribution of text and video representations to generate video-aware text representation. Meanwhile, to make VAP more effectively learn text-video knowledge, a text-video contrastive loss is proposed to ensure semantic consistency between text and video representations. In addition, we propose the external knowledge prompting (EKP) module to introduce more video-related text labels from an external knowledge base to enrich prompts for accurate semantic alignment. Extensive experiments are conducted on three public datasets, THUMOS14, ActivityNet1.2, and ActivityNet1.3, demonstrating that our approach outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Video analysis

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Rapid disparity prediction for dynamic scenes

Rapid disparity prediction for dynamic scenes

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9th International Symposium on Advances in Visual Computing, ISVC 2013

作者： Jiang, Jun Cheng, Jun Chen, Baowen Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Chinese University of Hong Kong Hong Kong Hong Kong Shsenzhen Institute of Information Technology China Guangdong Provincial Key Laboratory of Robotics and Intelligent System China Shenzhen Key Laboratory of Computer Vision and Pattern Recognition China

ISBN: (纸本)9783642419133

Real-time 3D sensing plays a critical role in robotic navigation, video surveillance and human-computer interaction, etc. When computing 3D structures of dynamic scenes from stereo sequences, spatiotemporal stereo and scene flow methods can produce temporally coherent disparity. However, most existing methods do not utilize the previous disparity map sufficiently to compute the next disparity map, and the searching space of correspondences limits the speed of disparity computation for each image pair. This paper proposes an effective scheme to predict disparity maps from stereo sequences. In particular, we apply a robust 3D registration algorithm based on the angular-invariant feature to estimate the ego-motion of the stereo rig between consecutive frames, and present the transformation between consecutive disparity maps. The scheme can produce a sequence of temporally coherent disparity maps rapidly. We apply the new scheme to real outdoor scenes, and thorough empirical studies indicate the effectiveness of the new scheme for practical applications. © 2013 Springer-Verlag.

关键词： Human computer interaction

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Fast and Error-Bounded Space-Variant Bilateral Filtering

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Journal of computer Science & Technology 2019年第3期34卷 550-568页

作者： Meng-Ke Yuan Long-Quan Dai Dong-Ming Yan Li-Qiang Zhang Jun Xiao Xiao-Peng Zhang National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Technology Nanjing University of Science and TechnologyNanjing 210094China State Key Laboratory of Remote Sensing Science School of GeographyBeijing Normal UniversityBeijing 100875China

The traditional space-invariant isotropic kernel utilized by a bilateral filter(BF)frequently leads to blurry edges and gradient reversal artifacts due to tlie existence of a large amount of outliers in the local averaging ***,the efficient and accurate cstiinatioii of space-variant k(4rnels which adapt to image structures,and the fast realization of the corresponding space-variant bilateral filtering are challenging *** address these problems,we present a space-variant BF(SVBF).and its linear time and error-bounded acceleration ***,we accurately estimate spacevariant,anisotropic kernels that vary with image structures in linear time through structure tensor and mininnini spanning ***,we perform SVBF in linear time using two error-bounded approximation methods,namely,low-rank tensor approximation via higher-order singular value decomposition and exponential sum *** proposed SVBF can efficiently achieve good edge-preserving *** validate the advantages of the proposed filter in applications including:image denoising,image enhancement,and image focus *** results(leinonstrate that our fast and error-bounded SVBF is superior to state-of-the-art methods.

关键词： error-bounded acceleration edge-aware smoothing space-variant bilateral filtering

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