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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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To Honor our Heroes: Analysis of the Obituaries of Australians Killed in Action in WWI and WWII

To Honor our Heroes: Analysis of the Obituaries of Australia...

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international conference on pattern recognition

作者： Marc Cheong Mark Alfano School of Computing and Information Systems (Centre for AI and Digital Ethics) University of Melbourne Parkville Australia Philosophy Macquarie University Macquarie University Sydney Australia

ISBN: (纸本)9781728188089;9781728188096

Obituaries represent a prominent way of expressing the human universal of grief. According to philosophers, obituaries are a ritualized way of evaluating both individuals who have passed away and the communities that helped to shape them. the basic idea is that you can tell what it takes to count as a good person of a particular type in a particular community by seeing how persons of that type are described and celebrated in their obituaries. Obituaries of those killed in conflict, in particular, are rich repositories of communal values, as they reflect the values and virtues that are admired and respected in individuals who are considered to be heroes in their communities. In this paper, we use natural language processing techniques to map the patterns of values and virtues attributed to Australian military personnel who were killed in action during World War I and World War II. Doing so reveals several clusters of values and virtues that tend to be attributed together. In addition, we use named entity recognition and geotagging the track the movements of these soldiers to various theatres of the wars, including North Africa, Europe, and the Pacific.

关键词： Obituaries Text analysis Tracking Shape Europe Africa Natural language processing

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Siamese-structure Deep Neural Network Recognizing Changes in Facial Expression According to the Degree of Smiling

Siamese-structure Deep Neural Network Recognizing Changes in...

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international conference on pattern recognition

作者： Kazuaki Kondo Taichi Nakamura Yuichi Nakamura Shinichi Satoh Academic Center for Computing and Media Studies Kyoto University Kyoto Japan National Institute of Informatics Tokyo Japan

A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. therefore, in this study, we assumed that a “smile with happiness” is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. the results demonstrated that the proposed method achieved nearly perfect recognition with > 0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.

关键词： Measurement Visualization Image recognition Face recognition Perturbation methods Neural networks Mouth

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Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection

Transformer-Encoder Detector Module: Using Context to Improv...

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international conference on pattern recognition

作者： Faisal Alamri Sinan Kalkan Nicolas Pugeault The University of Exeter Middle East Technical University School of Computing Science University of Glasgow

ISBN: (纸本)9781728188089;9781728188096

Deep neural network approaches have demonstrated high performance in object recognition (CNN) and detection (Faster-RCNN) tasks, but experiments have shown that such architectures are vulnerable to adversarial attacks (FFF, UAP): low amplitude perturbations, barely perceptible by the human eye, can lead to a drastic reduction in labelling performance. this article proposes a new context module, called Transformer-Encoder Detector Module, that can be applied to an object detector to (i) improve the labelling of object instances; and (ii) improve the detector's robustness to adversarial attacks. the proposed model achieves higher mAP, F1 scores and AUC average score of up to 13% compared to the baseline Faster-RCNN detector, and an mAP score 8 points higher on images subjected to FFF or UAP attacks due to the inclusion of both contextual and visual features extracted from scene and encoded into the model. the result demonstrates that a simple ad-hoc context module can improve the reliability of object detectors significantly.

关键词： Visualization Perturbation methods Detectors Object detection Transforms Field-flow fractionation Feature extraction

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pattern of Postural Sway of Diabetic Peripheral Neuropathy People

Pattern of Postural Sway of Diabetic Peripheral Neuropathy P...

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10th international conference on Applied Human Factors and Ergonomics (AHFE) / international conference on Human Factors and Ergonomics in Healthcare and Medical Devices

作者： Kim, Byungjoon B. J. East Carolina Univ Engn Dept Greenville NC 27858 USA

ISBN: (纸本)9783030204518;9783030204501

Diabetic peripheral neuropathy can cause a loss of sensation in the feet, leading to impaired stability of body balance, and resulting in a risk of falls or slips. In the literature, a considerable number of studies have reported that people with diabetic peripheral neuropathy tend to have trouble keeping posture stable when the equilibrium of body balance is disturbed, even when standing quietly. In general, many studies found differences of postural stability between people with diabetic peripheral neuropathy and without neuropathy. However, little research has been done to find a difference of sway patterns between them. the objective of this study was to characterize the pattern of body sway for finding an effect of peripheral neuropathy on postural control when the stability of posture was disturbed. pattern of body sway was investigated in terms of placement of center of pressure over the four quadrants during several time intervals such as quiet standing before a perturbation, during the perturbation, and after the perturbation. No significant difference of patterns of body sway was found between diabetic people with and without peripheral neuropathy during quiet standing and for most of considered perturbation directions at low velocity of perturbation. However, when the velocity of perturbation increased, a significant difference of placement of center of pressure was found from the third and fourth quadrants.

关键词： Diabetic neuropathy Postural sway Perturbation

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Attributes Aware Face Generation with Generative Adversarial Networks

Attributes Aware Face Generation with Generative Adversarial...

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international conference on pattern recognition

作者： Zheng Yuan Jie Zhang Shiguang Shan Xilin Chen University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728188089;9781728188096

Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. then three stacked generators generate 64 × 64, 128 × 128 and 256 × 256 resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.

关键词： Image resolution Correlation Image synthesis Face recognition Generative adversarial networks Generators Faces

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Extracting Discriminative Features for Cross-View Gait recognition Based on the Attention Mechanism

Extracting Discriminative Features for Cross-View Gait Recog...

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IEEE international conference on Computational Science and Engineering, CSE

作者： Ruicheng Sun Shuo Han Weihang Peng Hanxiang Zhuang Xin Zeng Xingang Liu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665416610

Human identification based on gait biometrics has become a popular research topic of computer vision and pattern recognition due to its great potential in public security and surveillance system. However, the recognition accuracy can be seriously degraded because of the appearance differences caused by view angle variation. To tackle this problem, we propose a method based on convolutional neural network (CNN) and attention mechanism to solve the cross-view problem in gait recognition. In the proposed algorithm, we firstly extract the features based on CNN structure and then the Horizontal Splitting operation is done to obtain the feature partitions in different granularities. After that, the attention mechanism is utilized to calculate the attention scores of the input partitions on both spatial and channel domain and finally the group of feature vectors can be obtained to determine the corresponding identity. In order to verify the effectiveness of the proposed method, the experiments are done based on two popular gait datasets–CASIA-B and OU-ISIR LP. the results show that the proposed model can effectively extract the discriminative gait features robust to view angle variation and improve the crossview gait recognition accuracy compared with the state-of-the-arts.

关键词： Computer vision Scientific computing Surveillance conferences Feature extraction pattern recognition Partitioning algorithms

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Intelligent Process Automation: the Future of Digital Transformation

Intelligent Process Automation: The Future of Digital Transf...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Pankaj Singh Kholiya Akshat Kapoor Meghavi Rana Megha Bhushan School of Computing DIT University Dehradun India

ISBN: (纸本)9781665439718

the technological evolution in the last decades has led us to the forefront of the digitization of services at every stratum of the processes and businesses. As the technology is advancing, an era of automation is witnessed in each field. Intelligent Process Automation (IPA) is amalgamation of Robotic Process Automation (RPA) with Artificial Intelligence with a view to create end-to-end processes which can think, learn, and adapt on their own. this paper aims to present a comprehensive study of the RPA tools associated to the intelligent automation using Artificial Intelligence. It also provides a brief overview of the current development in IPA market which can contribute to the implementation of the IPA in various organizations.

关键词： Intelligent automation Digital transformation Organizations Learning (artificial intelligence) Market research

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PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

PICK: Processing Key Information Extraction from Documents u...

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international conference on pattern recognition

作者： Wenwen Yu Ning Lu Xianbiao Qi Ping Gong Rong Xiao School of Medical Imaging Xuzhou Medical University Xuzhou China Visual Computing Group Ping An Property & Casualty Insurance Company Shenzhen China

Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character recognition (OCR) including text detection and recognition tasks recently. However, Key Information Extraction (KIE) from documents as the downstream task of OCR, having a large number of use scenarios in real-world, remains a challenge because documents not only have textual features extracting from OCR systems but also have semantic visual features that are not fully exploited and play a critical role in KIE. Too little work has been devoted to efficiently make full use of both textual and visual features of the documents. In this paper, we introduce PICK, a framework that is effective and robust in handling complex documents layout for KIE by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the textual and visual features and global layout without ambiguity. Extensive experiments on realworld datasets have been conducted to show that our method outperforms baselines methods by significant margins. Our code is available at https://***/wenwenyu/PICK-pytorch.

关键词： Deep learning Visualization Text recognition Layout Semantics Feature extraction Information retrieval

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Binary neural network based real time emotion detection on an edge computing device to detect passenger anomaly

Binary neural network based real time emotion detection on a...

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international conference on VLSI Design

作者： B S Ajay Madhav Rao Pre-Si Valid/Verif Engineer Intel Technology India Pvt. Ltd. Bangalore India Surgical and Assistive Robotics Lab International Institute of Information Technology Bangalore India

ISBN: (纸本)9781665440875;9781665431279

Passenger safety in public transportation especially while riding in the form of shared cabs, and taxis are often ignored, and not much preventive protocols are devised. In the connected mobility world, emotion recognition from facial expressions is a possibility, however a faster processing and edge computing device to derive anomaly state inferences will be apt for further notifying about the safety of the passenger. FPGA implementation is a viable approach to not only implement in the embedded system automotive electronics, but also accelerate the inference results, hence making it as an ideal real time candidate for passenger anomaly state identification. For the same, a real time emotion detection system using facial features was implemented on FPGA. A Binary Neural Network (BNN) feeded by Local Binary pattern (LBP) output was designed towards the development of an improved and faster emotion recognition system. LBP is configured as a preprocessing step to extract facial features that is passed on to the BNN layer for successful inference. the preprocessing method utilizes Viola-Jones (VJ) algorithm to extract facial data while removing other background information from the image. the LBP-BNN network is modelled using Facial Expression 2013 (FER-2013) data set for training. the custom hardware accelerator or the overlay is synthesized and the designed IP is implemented on FPGA for the inference. Inference is done using the trained model on FPGA to enable faster classified results. Emotion detection using facial expressions is classified to six states namely: angry, disgust, fear, happy, sad, and surprise. the LBP-BNN network is implemented in FPGA, to realize a real time facial emotion recognition by capturing the image of a person from a web camera interfaced to the FPGA acting as edge computing inference device, with acceptable accuracy. the image processing based emotion detection design is highly suitable for other applications including tracking of emotion

关键词： Emotion recognition Embedded systems Neural networks Feature extraction Real-time systems Hardware Safety

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YOLO Based Real-Time Human Detection for Smart Video Surveillance at the Edge 8

YOLO Based Real-Time Human Detection for Smart Video Surveil...

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8th IEEE international conference on Communications and Electronics (IEEE ICCE)

作者： Huy Hoang Nguyen thi Nhung Ta Ngoc Cuong Nguyen Van Truong Bui Hung Manh Pham Duc Minh Nguyen Hanoi Univ Sci & Technol Sch Elect & Telecommun Hanoi Vietnam Vnpt Technol Technol Ctr Hanoi Vietnam

ISBN: (数字)9781728154718

ISBN: (纸本)9781728154718

Recently, smart video surveillance at the edge has become a trend in developing security applications since edge computing enables more image processing tasks to be implemented on the decentralised network note of the surveillance system. As a result, many security applications such as behaviour recognition and prediction, employee safety, perimeter intrusion detection and vandalism deterrence can minimise their latency or even process in real-time when the camera network system is extended to a larger degree. Technically, human detection is a key step in the implementation of these applications. With the advantage of high detection rates, deep learning methods have been widely employed on edge devices in order to detect human objects. However, due to their high computation costs, it is challenging to apply these methods on resource limited edge devices for real-time applications. Inspired by the You Only Look Once (YOLO), residual learning and Spatial Pyramid Pooling (SPP), a novel form of real-time human detection is presented in this paper. Our approach focuses on designing a network structure so that the developed model can achieve a good trade-off between accuracy and processing time. Experimental results show that our trained model can process 2 FPS on Raspberry PI 3B and detect humans with accuracies of 95.05 % and 96.81 % when tested respectively on INRIA and PENN FUDAN datasets. On the human COCO test dataset, our trained model outperforms the performance of the Tiny-YOLO versions. Additionally, compare to the SSD based L-CNN method, our algorithm achieves better accuracy than the other method.

关键词： Edge computing smart video surveillance YOLO human detection

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