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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Multi-Scale Residual Pyramid Attention Network for Monocular...

引用

international conference on pattern recognition

作者： Jing Liu Xiaona Zhang Zhaoxin Li Tianlu Mao College of Computer and Cyber Security Hebei Normal University Shijiazhuang Hebei China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728188089;9781728188096

Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end multi-scale residual pyramid attention network (MRPAN) to mitigate these problems. First, we propose a multi-scale attention context aggregation (MACA) module, which consists of spatial attention module (SAM) and global attention module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover complex structures in the scene. Then we proposed an improved residual refinement module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promising performance in object boundaries and local details compared with other state-of-the-art methods.

关键词： Geometry Image segmentation Correlation Three-dimensional displays Semantics Estimation pattern recognition

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4D unsupervised object discovery 22

4D unsupervised object discovery

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Yuqi Wang Yuntao Chen Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HKISI_CAS Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence University of Chinese Academy of Sciences and Centre for Artificial Intelligence and Robotics HKISI_CAS

ISBN: (纸本)9781713871088

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data - 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly it- eratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones.

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Grand Challenges in Image Processing

Grand Challenges in Image Processing

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Natural and Applied Sciences (ICNAS), international conference for

ISBN: (数字)9781665472401

ISBN: (纸本)9781665472418

Summary form only. The field of image processing has been the subject of intensive research and development activities for several decades. This broad area encompasses topics such as image/video processing, image/video analysis, image/video communications, image/video sensing, modeling and representation, computational imaging, electronic imaging, information forensics and security, 3D imaging, medical imaging, and machine learning applied to these respective topics. Rapid technological advances, especially in terms of computing power and network transmission bandwidth, have resulted in many remarkable and successful applications. Nowadays, images are ubiquitous in our daily life. Entertainment is one class of applications that has greatly benefited, including digital TV (e.g., broadcast, cable, and satellite TV), Internet video streaming, digital cinema, and video games. Beyond entertainment, imaging technologies are central in many other applications, including digital photography, video conferencing, video monitoring and surveillance, satellite imaging, but also in more distant domains such as healthcare and medicine, distance learning, digital archiving, cultural heritage or the automotive industry. I will give a highlight for few research grand challenges for future imaging and video systems, in order to achieve breakthroughs to meet the growing expectations of end users. 2022 international

关键词： Imaging Streaming media Image processing Satellite broadcasting pattern recognition Entertainment industry Computer science

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Spatial-Temporal Graph Neural Networks Fusing Multiple Data

Spatial-Temporal Graph Neural Networks Fusing Multiple Data

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Haonan Xu Wenfang Xue Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Recently, quantitative investment has become a new way of investment finance, which combines the model with the computer to predict the investment stock trend more efficiently and accurately. Stock trend forecasting is one of the difficult and important tasks in modern quantitative investment systems. With the development of artificial intelligence technology, many deep learning models have emerged. The application of deep learning in the stock market not only reduces the difficulty of stock analysis and trend prediction, but also introduces new investment methods and ideas for investors. However, most of the models are based on volume and price data, and the consideration of stock relationships are relatively simple, so we propose a Spatial-Temporal Graph Neural Network model fusing Barra factors (BSTGNN), which integrates factor knowledge, mines potential inter-stock relationships, and fully consider the heterogeneity of the relationship. The experiments conducted on the real-world stock market dataset prove that BSTGNN perform better than the baseline methods.

关键词： Deep learning Solid modeling Computational modeling Time series analysis Market research Graph neural networks Data models

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Face recognition Using Laplacian Completed Local Ternary pattern (LapCLTP)

Face Recognition Using Laplacian Completed Local Ternary Pat...

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international conference on Computer Science, Electrical and Electronic Engineering, ICCEE 2019

作者： Yee, Sam Yin Rassem, Taha H. Mohammed, Mohammed Falah Awang, Suryanti Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Kuantan26300 Malaysia

ISBN: (纸本)9789811512889

Nowadays, the face is one of the typical biometrics that has high-security technology in the biometrics field. In face recognition systems, feature extraction is considered as one of the important steps. In feature extraction, the important and interesting parts of the image are represented as a compact feature vector. Many features had been proposed in the image processing fields such as texture, colour, and shape. Recently, texture descriptors are playing an important and significant role as a local descriptor. Different types of texture descriptors had been proposed and used for face recognition task, such as Local Binary pattern (LBP), Local Ternary pattern (LTP), and Completed Local Ternary pattern (CLTP). All these texture features have achieved good performances in terms of recognition accuracy. In this paper, we propose to improve the performance of the CLTP and use it for face recognition. A Laplacian Completed Local Ternary pattern (LapCLTP) is proposed in this paper. The image is enhanced using a Laplacian filter for pre-processing image process before extracting the CLTP. JAFFR and YALE standard face datasets are used to investigate the performance of the LapCLTP. The experiment results showed that the LapCLTP outperformed the original CLTP in both datasets and achieved higher recognition accuracy. The LapCLTP achieved 99.24%, while CLTP achieved 98.78% with JAFFE dataset. IN YALE, the LapCLTP achieved 85.13%, while CLTP, only 84.46%. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Adaptive recognition for Tracking of Moving Objects 28th

Adaptive Recognition for Tracking of Moving Objects

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28th international conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Dobrovodsky, Karol Andris, Pavel Slovak Acad Sci Inst Informat Dubravska Cesta 9 Bratislava 84237 Slovakia

ISBN: (纸本)9783030196486;9783030196479

An adaptive multilocator for online recognition and tracking of moving objects is considered. The vision system is based on the fast real time sub image localization module aimed to recognize patterns in order to track the motion of the selected object. In order to reduce mistakes concerned with the identity, the system is able to recognize several similar objects simultaneously. Nevertheless, the operator can point out an object on the screen to correct the tracking process. An adaptation of the actual patterns according to the lastly localized objects in the area of interest is performed in each step of the real time process. The aim of the paper is to present an effective adaptation and correction procedure based on constant primary patterns defined by the operator and varying secondary patterns defined by the automatic adaptation.

关键词： Vision system Online recognition pattern adaptation Real time localization

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HD-EMG Electrode Count and Feature Selection Influence on pattern-based Movement Classification Accuracy 42

HD-EMG Electrode Count and Feature Selection Influence on Pa...

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42nd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Lara, J. Paskaranandavadivel, N. Cheng, L. K. Univ Auckland Auckland Bioengn Inst Auckland New Zealand

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119908

Control schemes that rely on electromyography (EMG) pattern classification have shown to improve their accuracy when coupled with an increasing number of electrodes. In this study, HD-EMG signals from the hand and forearm of volunteers performing a series of movements were recorded. Different amounts of input EMG channels were selected and time-domain features were extracted to train several SVM classifiers. Detailed comparisons were made to evaluate the impact of electrode count and feature selection over the overall classification accuracy of 17 different movements. The increased resolution achieved from higher electrode counts yielded significant improvements in classification accuracy;however, these improvements were marginal when the number of channels utilized surpassed 100 electrodes.

关键词： Feature extraction Electrodes Electromyography Muscles Wrist Spatial resolution pattern recognition

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Unifying Input and Output Smoothing in Neural Machine Translation 28

Unifying Input and Output Smoothing in Neural Machine Transl...

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28th international conference on Computational Linguistics, COLING 2020

作者： Gao, Yingbo Liao, Baohao Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781952148279

soft contextualized data augmentation is a recent method that replaces one-hot representation of words with soft posterior distributions of an external language model, smoothing the input of neural machine translation systems. Label smoothing is another effective method that penalizes over-confident model outputs by discounting some probability mass from the true target word, smoothing the output of neural machine translation systems. Having the benefit of updating all word vectors in each optimization step and better regularizing the models, the two smoothing methods are shown to bring significant improvements in translation performance. In this work, we study how to best combine the methods and stack the improvements. Specifically, we vary the prior distributions to smooth with, the hyperparameters that control the smoothing strength, and the token selection procedures. We conduct extensive experiments on small datasets, evaluate the recipes on larger datasets, and examine the implications when back-translation is further used. Our results confirm cumulative improvements when input and output smoothing are used in combination, giving up to +1.9 BLEU scores on standard machine translation tasks and reveal reasons why these smoothing methods should be preferred. © 2020 COLING 2020 - 28th international conference on Computational Linguistics, Proceedings of the conference. All rights reserved.

关键词： Neural machine translation

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soft Biometrics and Its Implementation in Keystroke Dynamics 2

Soft Biometrics and Its Implementation in Keystroke Dynamics

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2nd Joint international conference on Emerging computing Technology and Sports, JICETS 2019

作者： Yaacob, Mohd Noorulfakhri Idrus, Syed Zulkarnain Syed Ali, Wan Nor Ashiqin Wan Mustafa, Wan Azani Jamlos, Mohd Aminudin Wahab, Mohd Helmy Abd School of Human Development and Technocommunication Universiti Malaysia Perlis Perlis01000 Malaysia Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis Perlis01000 Malaysia Faculty of Engineering Technology Universiti Malaysia Perlis Perlis01000 Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Malaysia

Biometrics is a unique art that exists within a person and allows it to be used to differentiate between one another. These biometrics can be divided into two categories namely behaviour and physical such as face, fingerprint, hand, voice and gait. There are previous studies which examine the personal's characteristic or personality as gender, age, cultural, weight, height, colour of hair etc. This personal's characteristic or personality also known as soft biometric. Several previous studies have shown that the use of soft biometric element (one or combination of elements) in the process of identifying individuals can improve the performance of individual recognition. This paper will elaborate and concludes past studies related to the use of soft biometric elements in KD. Several soft biometric elements applied in various methods of recognition on previous studies have been listed and the results of the studies are compared. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Biometrics

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Global-Local Attention Network for Semantic Segmentation in Aerial Images

Global-Local Attention Network for Semantic Segmentation in ...

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international conference on pattern recognition

作者： Minglong Li Lianlei Shan Xiaobin Li Yang Bai Dengji Zhou Weiqiang Wang Ke Lv Bin Luo Si-Bao Chen University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Science Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Errors in semantic segmentation could be classified into two types: the large area misclassification and inaccurate local boundaries. Previously attention-based methods typically capture rich global contextual information, which benefits the large area classification but cannot address the local errors of boundaries. In this paper, we propose a Global-Local Attention Network (GLANet) which can simultaneously consider the global context and local details. Specifically, our GLANet consists of two branches: (1) the global attention branch and (2) local attention branch. Furthermore, three different modules are embedded in GLANet for respectively modelling the semantic interdependencies in spatial, channel and boundary dimension. Lastly, we merge the outputs of different branches to enhance the feature representation further, resulting in more precise segmentation. Overall, the proposed method achieves the competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over the existing baselines.

关键词： Image segmentation Image color analysis Image edge detection Semantics Vegetation mapping Vegetation pattern recognition

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