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Vehicle Color Recognition Based on Superpixel Features

Vehicle Color Recognition Based on Superpixel Features

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2019第十一届数字图像处理国际会议

作者： Qiuli Lin Feng Liu Qiang Zhao Ran Xu Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications College of Educational Science and Technology Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications

In this paper,a novel methodology is presented to settle the region of interest(ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color *** order to make full use of the local color and spatial information,vehicle images are divided into different superpixels at *** spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle *** comparing with the vehicle window clustering centroids obtained by k-means,the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are ***,a linear Support Vector Machine classifier is trained for color *** experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.

关键词： vehicle color recognition region of interest superpixel universal color characteristics Support Vector Machine

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Effective Scheme for Global Abnormal Event Detection for Surveillance Video

Effective Scheme for Global Abnormal Event Detection for Sur...

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2016 International Conference on communications, Information Management and Network Security (CIMNS2016)

作者： Fangxu Dong Dong Hu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Jiangsu Province's Key Lab of Image Processing and Image Communications Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781510830981

An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific feature extract methods are designed for corresponding illumination conditions. In the case of non-uniform illumination, features are extracted directly on the original image;in the case of uniform illumination, features are extracted on the binary image obtained by threshold segmentation on the difference image, where the thresholds are computed by the Otsu's method. The features extracted on normal video are used to learn an over-complete dictionary. Then, the sparse reconstruction cost over the dictionary is used to detect abnormal events. Experiments on the open global abnormal dataset and the comparison to the state-of-the-art methods validate effectiveness and quickness of our algorithm.

关键词： abnormal detection illumination change binary images sparse reconstruction cost

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Analysis of Beta Wave Epileptic EEG Signals Based on Symbolic Transfer Entropy

Analysis of Beta Wave Epileptic EEG Signals Based on Symboli...

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2015 International Conference on Artificial Intelligence and Industrial Engineering(AIIE 2015)

作者： Xiang Ye Tian Tian Tianpei Xu Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm

ISBN: (纸本)9781510806450

Epilepsy is a common neurological diseases caused by abnormal discharge of neurons in the brain. the attack is sudden and repeated characteristics. Therefore, in order to advance seizure prediction has important meaning for patients to take timely measures in this paper, the seizures in patients with EEG by using the method of symbolic transfer entropy are research and analysis, Through the EEG signal of epilepsy patients during attack and normal human alpha wave is extracted, By using the method of symbolic transfer entropy for analysis and research, Prior to the transfer characteristics which have been analyzed under entropy alpha wave component, this paper starts from beta wave components. then make a study by using the method of symbolic transfer entropy, the study found that using this method can differentiate the normal EEG and EEG in patients with epilepsy, Also found that the existence of nonlinear large amount of time series of EEG. This method is also proved symbolic transfer entropy based algorithm can be used to analyze the EEG signals fully, reveals the difference between epileptic EEG and normal EEG, clinical contribution made certain detection and prediction of epilepsy.

关键词： symbolic transfer entropy beta wave epileptic EEG detection prediction

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Multiscale Relative Transition Entropy Analysis of Electroencephalogram

Multiscale Relative Transition Entropy Analysis of Electroen...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Ying Wang Fengzhen Hou Jiafei Dai Jin Li Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. School of Science China Pharmaceutical University Nanjing General Hospital of Nanjing Military Command College of Physics and Information Technology Shaanxi Normal University

ISBN: (纸本)9781510823808

Recently,quantifying the complexity of the physiological time series has become more and more *** one of the most complex physiological signals,electroencephalogram(EEG) including a large number of physiological and pathological information attracts widespread ***,many traditional algorithms fail to account for the multiple times scales inherent in physiologic *** this paper,we proposed multiscale relative transition entropy algorithm(MRTE) to analyze the white noise and pink noise,the adolescent and adults EEG as well as normal and epileptic *** results indicate that there are distinct tendency among different types EEG which indicating that the multiscale relative transition entropy can distinguish different physiological and pathological signals.

关键词： multiscale relative transition entropy complexity

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Attributes and Semantic Constrained GAN for Face Sketch-Photo Synthesis

Attributes and Semantic Constrained GAN for Face Sketch-Phot...

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International Conference on Wireless communications and Signal processing (WCSP)

作者： Jieying Zheng Haoxian Li Feng Liu Jiangsu Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China School of Geographic and Biologic Information Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781665450867

Face sketch-photo synthesis aims at generating a facial photo conditioned on a given photo. It has attracted wide attention in computer vision and has been widely applied in law enforcement and entertainment. However, precisely synthesizing high-quality face photos is still challenging due to the missing color information in face sketches. To alleviate the problem, we propose an attribute and Semantic constrained Generative Adversarial Networks (ASGAN), which introduces face attribute and semantic constraints to improve the quality and accuracy of synthesized photos. Specifically, We first annotate the key attributes in the dataset and form a 14-dimensional attribute vector for each face. Then, the attribute features and semantic features obtained by face parsing are fused. Finally, the generator takes the fused constrain features and sketches as input and constructs two-stream encoders to synthesize high-quality photos. Extensive experimental results demonstrate that our method can significantly outperform state-of-the-art methods. It can synthe-size higher-quality face photos while maintaining the identity, attributes, and structure. Meanwhile, it can alleviate the problem of background and skin color synthesis errors.

关键词： Wireless communication Law enforcement Semantics Entertainment industry Color Signal processing Generative adversarial networks

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Remote Sensing image Scene Classification Based on SURF Feature and Deep Learning

Remote Sensing Image Scene Classification Based on SURF Feat...

引用

Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Jinxiang Liang Jianwu Dang Yangping Wang Jingyu Yang Zhenhai Zhang Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing Gansu Provincial Key Lab of System Dynamics and Reliability of Rail Transport Equipment Lanzhou Bocai Technology Co. Ltd.

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Remote sensing image scene classification is one of the key points in remote sensing image interpretation. The traditional remote sensing image scene classification feature performance is not strong, and the deep learning extraction semantic feature process is complex. This paper proposes a fusion feature remote sensing image scene classification method which is based on artificial features and deep learning semantic features. Firstly, the SURF feature of the remote sensing image is extracted and encoded by the VLAD algorithm. The semantic feature of a remote sensing image is extracted by transfer learning. Then the feature reduction is performed by PCA algorithm and feature fusion is performed. Finally, the scene classifier is trained by using the random forest algorithm. The experimental results show that the classification accuracy and Kappa coefficient of this method are higher and the method is effective.

关键词： Feature extraction Remote sensing Machine learning Semantics image analysis Classification algorithms Forestry

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On the Training of DNN-based Average Voice Model for Speech Synthesis

On the Training of DNN-based Average Voice Model for Speech ...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference

作者： Shan Yang Zhizheng Wu Lei Xie Shaanxi Provincial Key Lab of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China The Centre for Speech Technology Research University of Edinburgh UK

ISBN: (纸本)9781509024018

Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this work, we perform a systematic analysis of the training of multi-speaker average voice model (AVM), which is the foundation of adaptability and controllability of a DNN-based speech synthesis system. Specifically, we employ the i-vector framework to factorise the speaker specific information, which allows a variety of speakers to share all the hidden layers. And the speaker identity vector is augmented with linguistic features in the DNN input. We systematically analyse the impact of the implementations of ivectors and speaker normalisation.

关键词： Training DNN-based Average Voice Model Speech Synthesis

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Controlling Expressivity using Input Codes in Neural Network based TTS

Controlling Expressivity using Input Codes in Neural Network...

引用

Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Xiaolian Zhu Lei Xie Xiao Chen Xiaoyan Lou Xuan Zhu Xingjun Tan Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi’an Hebei University of Economics and Business Shijiazhuang China Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi’an China Language Computing Lab Samsung R&D Institute of China Beijing China

ISBN: (纸本)9781538653128

This paper presents a study on the use of input codes in the neural network acoustic modeling for expressive TTS. Specifically, we use different kinds of input codes, augmented with the linguistic features, as the input of a BLSTM-based acoustic model, to control the expressivity of the synthesized speech. The input codes, in one-hot representation, include dialogue code, sentiment code and sentence position code. The dialogue code indicates whether the text is a dialogue or narration in an audiobook story. The sentiment code is obtained from a sentiment analysis tool, which labels each sentence as positive, negative and neutral. The sentence position code indicates the position of the sentence in the paragraph. We believe these codes are highly related to the expressiveness of the audiobook speech. Experiments on the data from the Blizzard Challenge 2017 demonstrate the effectiveness of the use of input codes in the neural network approach for expressive TTS.

关键词： Neural networks Linguistics Acoustics Hidden Markov models Speech synthesis Adaptation models Speech coding

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Analyzing Occupational Stress Based On Inner Composition Alignment Algorithm

Analyzing Occupational Stress Based On Inner Composition Ali...

引用

International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Zhen Shi Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm China Nanjing Brain Hospital China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China

ISBN: (纸本)9781509037117

To distinguish different level of occupational stress of students and nurses, this paper applied inner composition alignment (IOTA) algorithm to analyse differences between the students' Electroencephalogram (EEG) and the nurses' EEG and to explore the influence of occupational stress on EEG. First, we selected nine cases of students EEG data and the same quantity of nurses EEG data from General Hospital of Nanjing Military Region, then, constructed two different brain networks by utilizing IOTA method and analyzed the networks characteristics which include IOTA coefficient, Clustering coefficient and Average degree. In order to verify the accuracy of the results, we used SPSS to analyse the significant difference hypothesis testing. The IOTA coefficients are inconsistent between students' EEG and nurses' EEG. Compared with the students, nurses' brain network is more complex. The level of occupational stress of nurses is higher than students. The IOTA algorithm can significantly distinguish occupational stress between students and nurses, and all results demonstrated the validity of the algorithm.

关键词： Brain Network Occupational Stress EEG IOTA Complex Network

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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