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Selective Multi-Convolutional Region Feature Extraction based Iterative Discrimination CNN for Fine-Grained Vehicle Model recognition 24

Selective Multi-Convolutional Region Feature Extraction base...

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24th International conference on pattern recognition (ICPR)

作者： Tian, Yanling Zhang, Weitong Zhang, Qieshi Lu, Gang Wu, Xiaojun Minist Educ Key Lab Modern Teaching Technol Beijing Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China Chinese Acad Sci Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Inst Adv Technol Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538637883

With the rapid rise of computer vision and driverless technology, vehicle model recognition plays a huge role in the common application and industry field. While fine-grained vehicle model recognition is often influenced by multi-level information, such as the image perspective, inter-feature similarity, vehicle details. Furthermore, pivotal regions extraction and fine-grained feature learning have become a vital obstacle to the fine-grained recognition of vehicle models. In this paper, we propose an iterative discrimination CNN (ID-CNN) based on selective multi-convolutional region (SMCR) feature extraction. the SMCR features, which consist of global and local SMCR features, are extracted from the original image with higher activation response value. As for ID-CNN, we use the global and local SMCR features iteratively to localize deep pivotal features and concatenate them together into a fully-connected fusion layer to predict the vehicle categories. We get better results and improve the accuracy to 91.8% on Stanford Cars-196 dataset and to 96.2% on CompCars dataset.

关键词： Feature extraction

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Emotional Health: A data driven approach to understand our emotions and improve our health 22

Emotional Health: A data driven approach to understand our e...

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22nd IEEE International conference on Computational Science and Engineering (IEEE CSE) / 17th IEEE International conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Vuppalapati, Chandrasekar Kedari, Sharat Ilapakurti, Anitha Kedari, Santosh Shankar, Jaya Hanumayamma Innovat & Technol Inc 628 Crescent Terrace Fremont CA 94536 USA Hanumayamma Innovat & Technol Inc Hig 2Block 2Flat 7 Hyderabad 500044 TN India

ISBN: (纸本)9781728116648

Human emotions play an important role in everyone's daily life. People who have good emotional health pose to have good health. However, many things that happen in our life can disrupt our emotional health. these can lead to strong feelings of sadness, stress, or anxiety. In today's world many things that happen in our life can disrupt our emotional health. Emotions are also associated with problematic changes in the cardiovascular, hormonal and immune system that may lead to allergies, headache, migraine, Arthritis, blood pressure, diabetes and may penetrate its way to the heart which gives you heart attacks. In fact, recent studies show, for over 40 million people in the United States have been diagnosed with depression, the number of people coping (or trying to cope) with loneliness is unknown, and that feeling of not belonging is depressing all by itself. Emotions like anger, fear, Disgust triggers strong sensations in the upper body. Other emotions like depression, sadness and shame shows diminished sensation in the lower body, while some emotions combined both type of response. though emptions are created in the brain, they can be manifest as physical feelings throughout the body. the most common way to understand human emotion is by capturing and understand signals provided by the human body. With help of IoT, NLP, Machine learning, Social Analytics and Artificial Intelligence we can capture and understand physiological signals and develop an emotion recognition system based on information provided by physiological signals.

关键词： computer vision Electronic Health Records EHR Mobile Sensors Voice Service APIs Amazon Alexa Apple Siri Kit Microsoft Cortana Internet of things IoT CEP Complex Event Processing Health iOS Android IoT architecture real-time stream analytics Decision ID3 Tree Sanjeevani Electronic Health Records Machine Learning Qualitative Metrics Outpatient

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Transductive Label Augmentation for Improved Deep Network Learning 24

Transductive Label Augmentation for Improved Deep Network Le...

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24th International conference on pattern recognition (ICPR)

作者： Elezi, Ismail Torcinovich, Alessandro Vascon, Sebastiano Pelillo, Marcello Univ Venice Venice Italy ZHAW Datalab Winterthur Switzerland DAIS Venice Italy DAIS ECLT Venice Italy

ISBN: (纸本)9781538637883

A major impediment to the application of deep learning to real-world problems is the scarcity of labeled data. Small training sets are in fact of no use to deep networks as, due to the large number of trainable parameters, they will very likely be subject to overfitting phenomena. On the other hand, the increment of the training set size through further manual or semi-automatic labellings can be costly, if not possible at times. thus, the standard techniques to address this issue are transfer learning and data augmentation, which consists of applying some sort of "transformation" to existing labeled instances to let the training set grow in size. Although this approach works well in applications such as image classification, where it is relatively simple to design suitable transformation operators, it is not obvious how to apply it in more structured scenarios. Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a label augmentation approach. We start from a small, curated labeled dataset and let the labels propagate through a larger set of unlabeled data using graph transduction techniques. this allows us to naturally use (second-order) similarity information which resides in the data, a source of information which is typically neglected by standard augmentation techniques. In particular, we show that by using known game theoretic transductive processes we can create larger and accurate enough labeled datasets which use results in better trained neural networks. Preliminary experiments are reported which demonstrate a consistent improvement over standard image classification datasets.

关键词： Games Labeling Standards Neural networks computer vision Feature extraction Training

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Weld seam type recognition system based on structured light vision and ensemble learning 15

Weld seam type recognition system based on structured light ...

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15th IEEE International conference on Mechatronics and Automation, ICMA 2018

作者： Wang, Zhe Jing, Fengshui Fan, Junfeng Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538660720

In this paper, we propose a weld seam recognition system based on structured light vision and ensemble learning. the proposed system consists of an industrial robot, a structured light vision sensor and a computer. the recognition procedures of proposed system include weld seam feature extraction and weld seam classification. In feature extraction part, the input images are processed by the following steps: noise filtration, laser stripe pattern extraction, main line extraction, edge points detection and feature computation. In classification part, ensemble learning models including BP-Adaboost and KNN-Adaboost are established to classify the images by the feature extracted. the experiment results validate the effectiveness and robustness of the proposed recognition system. © 2018 IEEE.

关键词： Feature extraction

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In-Line Burr Inspection through Backlight vision 2nd

In-Line Burr Inspection Through Backlight Vision

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2nd International Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st International Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st International Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, International Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th International conference on Image Analysis and Processing, ICIAP 2019

作者： Fitti, Matteo Castellini, Paolo Paone, Nicola Zannini, Marco Zitti, Saverio Gambini, Marco Chiariotti, Paolo Università Politecnica delle Marche via Brecce Bianche Ancona60131 Italy Zannini SpA Via Che Guevara 63 Castelfidardo60022 Italy Zannini Poland Wojkowicka 60 Czeladź41-250 Poland

ISBN: (纸本)9783030307530

this paper presents a vision-based quality control system for detecting burrs (miniature metal filaments) in transverse holes of high precision turned hollow cylinders. the system performs 100% in-line quality control at the turning station. It exploits a camera with telecentric optics framing the sample from the outside in back-light condition. A specifically developed cylindrical illuminator provides radial diffuse back-light illumination over 360° and can be inserted within the part to be inspected. the possibility to detect burrs placed on both the outer and the inner surface of target holes is achieved by exploiting a customized rotating device integrated to a commercial gripping device. Overall, the system mimics the manual inspection normally performed by operators. Burrs are detected as modifications of the circular shape of each hole, through algorithms that identify the holes on grayscale images, perform circle identification by geometric matching and identify burrs through analysis of local deviations of the edge from circularity. Results acquired in a real production line over a batch of 2000 parts showed no false-positive or false-negative diagnosis. © 2019, Springer Nature Switzerland AG.

关键词： Inspection

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Skeleton-Based Action recognition with Spatial Reasoning and Temporal Stack Learning 15th

Skeleton-Based Action Recognition with Spatial Reasoning and...

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15th European conference on computer vision (ECCV)

作者： Si, Chenyang Jing, Ya Wang, Wei Wang, Liang Tan, Tieniu Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Ctr Excellence Brain Sci & Intelligence Technol C Inst Automat Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China

ISBN: (纸本)9783030012465;9783030012458

Skeleton-based action recognition has made great progress recently, but many problems still remain unsolved. For example, the representations of skeleton sequences captured by most of the previous methods lack spatial structure information and detailed temporal dynamics features. In this paper, we propose a novel model with spatial reasoning and temporal stack learning (SR-TSL) for skeleton-based action recognition, which consists of a spatial reasoning network (SRN) and a temporal stack learning network (TSLN). the SRN can capture the high-level spatial structural information within each frame by a residual graph neural network, while the TSLN can model the detailed temporal dynamics of skeleton sequences by a composition of multiple skip-clip LSTMs. During training, we propose a clip-based incremental loss to optimize the model. We perform extensive experiments on the SYSU 3D Human-Object Interaction dataset and NTU RGB+D dataset and verify the effectiveness of each network of our model. the comparison results illustrate that our approach achieves much better results than the state-of-the-art methods.

关键词： Skeleton-based action recognition Spatial reasoning Temporal stack learning Clip-based incremental loss

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Proceedings of the 2018 7th International conference on computer and Communication Engineering, ICCCE 2018

Proceedings of the 2018 7th International Conference on Comp...

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7th International conference on computer and Communication Engineering, ICCCE 2018

ISBN: (纸本)9781538669914

the proceedings contain 93 papers. the topics discussed include: flood disaster warning system on the go;modifying cleaning method in big data analytics process using random forest classifier;comparative analysis of UWB balance antipodal Vivaldi antenna for array configuration;finger vein identification based on transfer learning of AlexNet;analysis of thUG: a low-interaction client honeypot to identify malicious websites and malwares;development of voice control and home security for smart home automation;a rectangular inset-fed patch antenna with defected ground structure for ISM band;selection of android smartphones with built-in dual lens camera for stereo vision android app development;electropalatography contact pattern in the production of consonant /m/ among Malay speakers;and simulation of a modified dual Mach-Zehnder interferometric perimeter intrusion detection system.

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A Graph-based Approach for Static Ensemble Selection in Remote Sensing Image Analysis 24

A Graph-based Approach for Static Ensemble Selection in Remo...

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24th International conference on pattern recognition (ICPR)

作者： Faria, Fabio Augusto Sarkar, Sudeep Univ Fed Sao Paulo UNIFESP Inst Sci & Technol Sao Jose Dos Campos Brazil USF Dept Comp Sci & Engn Tampa FL USA

ISBN: (纸本)9781538637883

Many works in the literature have used machine learning techniques to solve their classification problems in different knowledge areas, e.g., medicine, agriculture, and remote sensing. Since there is no a single machine learning technique that achieves the best results for all kind of applications, a good alternative is the fusion of classification techniques, also known as multiple classifier systems ({MCS). A common challenge in MCS is the selection of a few classifiers among many classifiers that are available in the literature;using all possible classifiers is not a feasible alternative. the choice of the classifiers becomes an essential factor, i.e., we need an ensemble selection approach. In this work, we propose a novel graph-based approach for static ensemble selection ({GASES) to find or choose the best classifier set for remote sensing image classification. Experiments demonstrate that GASES improves performance by up to 70% over different baseline approaches when fusing classifiers. It decreases the number of classifiers used while retaining the effectiveness of using all of the classifiers. Furthermore, our proposed method is a more straightforward and intuitive technique for static ensemble selection scheme than other baseline approaches such as Consensus and Kendall.

关键词： Support vector machines Remote sensing Gases Task analysis Agriculture Bagging computer architecture

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Multi-task Learning of Cascaded CNN for Facial Attribute Classification 24

Multi-task Learning of Cascaded CNN for Facial Attribute Cla...

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24th International conference on pattern recognition (ICPR)

作者： Zhuang, Ni Yan, Yan Chen, Si Wang, Hanzi Xiamen Univ Sch Informat Sci & Engn Xiamen 361005 Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China

ISBN: (纸本)9781538637883

Recently, facial attribute classification (FAC) has attracted significant attention in the computer vision community. Great progress has been made along with the availability of challenging FAC datasets. However, conventional FAC methods usually firstly pre-process the input images (i.e., perform face detection and alignment) and then predict facial attributes. these methods ignore the inherent dependencies among these tasks (i.e., face detection, facial landmark localization and FAC). Moreover, some methods using convolutional neural network are trained based on the fixed loss weights without considering the differences between facial attributes. In order to address the above problems, we propose a novel multi-task learning of cascaded convolutional neural network method, termed MCFA, for predicting multiple facial attributes simultaneously. Specifically, the proposed method takes advantage of three cascaded sub-networks (i.e., S_Net, M_Net and L_Net corresponding to the neural networks under different scales) to jointly train multiple tasks in a coarse-to-fine manner, which can achieve end-to-end optimization. Furthermore, the proposed method automatically assigns the loss weight to each facial attribute based on a novel dynamic weighting scheme, thus making the proposed method concentrate on predicting the more difficult facial attributes. Experimental results show that the proposed method outperforms several state-of-the-art FAC methods on the challenging CelebA and LFWA datasets.

关键词： Facial features Task analysis Feature extraction Face Training Testing

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DENSE CHAINED ATTENTION NETWORK FOR SCENE TEXT recognition 25

DENSE CHAINED ATTENTION NETWORK FOR SCENE TEXT RECOGNITION

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25th IEEE International conference on Image Processing (ICIP)

作者： Gao, Yunze Chen, Yingying Wang, Jinqiao Tang, Ming Lu, Hanqing Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781479970612

Reading text in the wild is a challenging task in computer vision. Scene text suffers from various background noise, including shadow, irrelevant symbols and background texture. In order to reduce the disturbance of background noise, we propose a dense chained attention network with stacked attention modules for scene text recognition. Each attention module learns the attention map that is adapted to corresponding features to enhance the foreground text and suppress the background noise. Besides, the attention branch is designed with the convolution-deconvolution structure which rapidly captures global information to guide the discriminative feature selection. We stack multiple attention modules to gradually refine the attention maps and capture both the low-level appearance feature and the high-level semantic information. Extensive experiments on the standard benchmarks, the Street View Text, IIIT5K, and ICDAR datasets validate the superiority of the proposed method. the dense chained attention network achieves state-of-the-art or highly competitive recognition performance.

关键词： text recognition attention convolution-deconvolution

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