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SPIE 12th International Symposium on Multispectral image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

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Advancing 3D Mesh Segmentation with Regularization Losses: A Semi-Supervised Approach Using MeshCNN

Advancing 3D Mesh Segmentation with Regularization Losses: A...

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International Conference on Digital Home (ICDH)

作者： Yan Xing Shuaijun Lv Daolun Li Wanliang Zhuang Jieqing Tan Rushi Lan School of Mathematics Hefei University of Technology Hefei China Guangxi Key Lab of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

The application of deep learning in 3D mesh segmentation has attracted increasing attention. In supervisedlearning based mesh segmentation, back propagation is often based on cross entropy loss. Some prior knowledge, such as the structure similarity or geometric consistency of data, can be used to improve the segmentation effect. However, in the literature for mesh segmentation, such prior knowledge is usually either ignored in deep learning or only considered at post-processing stage. In this work, we add prior knowledge as a regularization term into the loss function for optimization, which is applied in the semi-supervised 3D mesh segmentation based on MeshCNN for the first time. A large number of experiments show that, for both semi-supervised segmentation with 30% and 70% labeled data and fully supervised segmentation, the proposed regularization term improves the segmentation performance.

关键词： Deep learning Backpropagation Three-dimensional displays Propagation losses Entropy Optimization

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A novel computational approach to predict activity of nuclear export signals

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Journal of Computational and Theoretical Nanoscience 2015年第12期12卷 6016-6019页

作者： Chen, Zhihua Zhang, Pan Wu, Tingfang Shi, Xiaolong Wang, Xun Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Computer and Communication Engineering China University of Petroleum QingdaoShandong266580 China

A nuclear export signal (NES) is a sequence of amino acids, which is a protein localization signal, and contributes to regulate localization of cellular proteins. In recent peering works, activity of NESs were introduced in computational methods, which perform significantly well in identifying unknown NESs even from huge datasets, but suffer from a shortage that only a small number of NESs are associated with experimental verified active scores. In this work, we propose a novel computational method to predict active score of NESs. Specifically, we firstly abstract relevant features to NES activity, and then construct a mathematical model based on principal component analysis (PCA) to find each feature's contribution to activity. In this way, a method to calculate active score of unknown NESs is achieved. In data experiments, it is obtained that our method can achieve accuracy rate 80%. The activity of an unknown NES candidate can give us a preliminary understanding of it, and can be used to identify positive NESs from the negative ones. To the best acknowledge of the authors, this is the first time computational method is proposed to predict NESs' activity. Copyright © 2015 American Scientific Publishers All rights reserved.

关键词： Computational methods

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Quality Assessment for AI Generated images with Instruction Tuning

arXiv

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arXiv 2024年

作者： Wang, Jiarui Duan, Huiyu Zhai, Guangtao Min, Xiongkuo Institute of Image Communication and Network Engineering China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Artificial Intelligence Generated Content (AIGC) has grown rapidly in recent years, among which AI-based image generation has gained widespread attention due to its efficient and imaginative image creation ability. However, AI-generated images (AIGIs) may not satisfy human preferences due to their unique distortions, which highlights the necessity to understand and evaluate human preferences for AIGIs. To this end, in this paper, we first establish a novel image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+, which provides human visual preference scores and detailed preference explanations from three perspectives including quality, authenticity, and correspondence. Then, based on the constructed AIGCIQA2023+ database, this paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning. Specifically, the MINT-IQA model first learn and evaluate human preferences for AI-generated images from multi-perspectives, then via the vision-language instruction tuning strategy, MINT-IQA attains powerful understanding and explanation ability for human visual preference on AIGIs, which can be used for feedback to further improve the assessment capabilities. Extensive experimental results demonstrate that the proposed MINT-IQA model achieves state-of-the-art performance in understanding and evaluating human visual preferences for AIGIs, and the proposed model also achieves competing results on traditional IQA tasks compared with state-of-the-art IQA models. The AIGCIQA2023+ database and MINT-IQA model are available at: https://***/IntMeGroup/MINT-IQA Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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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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On Wireless Local Area Networks Network Traffic

On Wireless Local Area Networks Network Traffic

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Chinese Control Conference (CCC)

作者： Rong Hong Liang Yiming Wu Ronghui Xu Cheng Yu Fe College of Electrical and Information Engineering Hunan University Changsha China College of Computer & Communication Hunan University Changsha China Provincial Key Laboratory of Image Processing & Imagecom Communication Nanjing University of Posts and Telecommunications Nanjing China

Through a series of real tests, this paper analyses the characteristic of IEEE 802.11 traffic, explains the results of the tests. More importantly, the results indicate that the characteristic of IEEE 802.11 traffic exhibits obviously self-similarity. As the first step to set up a 802.11 traffic math-model based on self-similarity theory.

关键词： Wireless LAN Telecommunication traffic Traffic control Educational institutions Testing communication system traffic control image processing Telecommunication computing

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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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PORTABLE HEALTH SCREENING DEVICE OF RESPIRATORY INFECTIONS

arXiv

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arXiv 2020年

作者： Jiang, Zheng Hu, Menghan Zhai, Guangtao Institute of Image Communication and Information Processing Shanghai Jiao Tong University China Shanghai Key Labora. of Multidim. Infor. Proce East China Normal University China

The COVID-19 epidemic was listed as a public health emergency of international concern by the WHO on January 30, 2020. To curb the secondary spread of the epidemic, many public places were equipped with thermal imagers to check the body temperature. However, the COVID-19 pneumonia has concealed symptoms: the first symptom may not be fever, and can be shortness of breath. During epidemic prevention, many people tend to wear masks. Therefore, in this demo paper, we proposed a portable non-contact healthy screening system for people wearing masks, which can simultaneously obtain body temperature and respiration state. This system consists of three modules viz. thermal image collection module, health indicator calculation module and health assessment module. In this system, the thermal video of human faces is first captured through a portable thermal imaging camera. Then, body temperature and respiration state are extracted from the video and are imported into the following health assessment module. Finally, the screening result can be obtained. The results of preliminary experiments show that this syetem can give an accurate screening result within 15 seconds. This system can be applied to many application scenarios such as community and campus. The demo videos of the proposed system are available at: https://***/10.6084/***.12028032. Copyright © 2020, The Authors. All rights reserved.

关键词： Epidemiology

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Graph Convolutional Network-based Functional-structural Sub-region Framework for Head and Neck Cancer Prognosis with PET/CT Imaging

Graph Convolutional Network-based Functional-structural Sub-...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Zhou, Zidong Peng, Junyi Lin, Guoyu Wu, Huiqin Lv, Wenbing Lu, Lijun Southern Medical University School of Biomedical Engineering Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangzhou510515 China Yunnan University School of Information Science and Engineering Kunming650504 China Pazhou Lab Guangzhou510330 China

ISBN: (纸本)9781665488723

In order to improve the behavior of the prognostic model for patients with head and neck cancer (HNC), we constructed a graph convolutional network (GCN), characterizing the interaction of functional and structural information among different sub-regions of tumors in both PET and CT images. Sub-regions were obtained by using the Watershed-based method and were regarded as vertexes in the graph. The element of 1 in the adjacency matrix indicates corresponding two vertices are spatially contiguous, while the element of 0 indicates they are separated. The selected radiomics features were used to characterize each vertex. 642 HNC patients from 8 different centers were used. The best C-index of 0.767 was achieved by the combination of GCN and clinical parameters compared to the radiomics model (0.747) or GCN model (0.702). the proposed approach has great potential in aiding treatment decision-making. © 2022 IEEE.

关键词： Graph convolutional neural network Head and neck cancer Prognosis Sub-region

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