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International Conference on Computers, Communications, and systems (ICCCS)

作者： PengPeng Jiang Kun Zhang Peijian Zhang Ping Lu Department of electrical engineering Nantong University Nantong China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

As we all know, the large number of counts is a challenging and time consuming task subject because of oversized number and complex conditions. However, the development of deep learning makes deep learning models very competitive in image segmentation. In this paper, we take cigarette filter rods as the research object. we first evaluate the standard Unet for the filter rod target recognition to separate target and background. Secondly, we use the focal loss function instead of the traditional cross-entropy function to solve the problem of imbalance between target and background area. Thirdly, we add a self-attention module in the traditional Unet convolutional layer to enhance the convolution effect. Fourth, we propose structural element detection criteria and round tangency matching strategy based on HMM (Hidden Markov Model) for the geometric relationship of filter rod position, which further improves the accuracy of the algorithm. We used Qu's [1], Mask-R-CNN [2], FCN [3], Deep-lab-v1 [4] and this paper's algorithm to test the performance of 30000 images from the industrial site. The performance of this paper's algorithm is completely better than the performance of the above algorithm.

关键词： Hidden Markov models Matched filters Training Deep learning Filtering algorithms Signal processing algorithms Target recognition

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Solution of overdetermined systems of equations using the conforming subsystem selection 5

Solution of overdetermined systems of equations using the co...

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5th International Conference on Information Technology and Nanotechnology, ITNT 2019

作者： Fursov, v.A. Kotov, A.P. Goshin, Ye v. Samara National Research University Moskovskoye shosse 34 Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya Street 151 Samara443001 Russia

Within this research, we consider an overdetermined system of equations generated on a small number of observations. The degrees of freedom of the overdetermined system and the number of observations are roughly equal. We propose a modification of the Conformed Estimates Method (CEM). The method improvement consists of the usage of an auxiliary system, which is formed from the initial system by reducing the columns. The estimates can be obtained on the higher-level auxiliary subsystem, which is the freest from noise. We consider our method in the problem of computing Rational Polynomial Coefficients (RPCs) from well-established Ground Control Points (GCPs). © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Specular reflection detection algorithm for endoscopic images 17

Specular reflection detection algorithm for endoscopic image...

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17th image processing: algorithms and systems Conference, IPAS 2019

作者： voronin, v. Semenishchev, E. Zelensky, A. Agaian, S. Don State Technical University Dept. of Radio-Electronics Systems Gagarina 1 Rostov on Don Russia Moscow State University of Technology "STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

Endoscopy is a process that allows viewing/ visualize the inside of a human body. In this article, we propose a specular reflection detection algorithm for endoscopic images that utilizes intensity, saturation and gradient information. The proposed algorithm is a two-stage procedure: (a) image enhancement using an adaptive alpha-rooting algorithm and (b) an efficient reflection detection algorithm in the HSv color space. The extensive computer simulations show a significant improvement over state-of-the-art results for specular reflection detection and segmentation accuracy. © 2019, Society for Imaging Science and Technology.

关键词： Endoscopy

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Learning Deep Feature Representation for Face Spoofing 2nd

Learning Deep Feature Representation for Face Spoofing

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2nd International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Srinivasa Perumal, R. Santosh, K.C. Chandra Mouli, P.v.S.S.R. School of Information Technology and Engineering Vellore Institute of Technology VelloreTamil Nadu India Department of Computer Science University of South Dakota Vermillion United States Department of Computer Applications National Institute of Technology JamshedpurJharkhand India

ISBN: (纸本)9789811391804

Biometrics is an emerging research area due to its easiness in identification of the person. Face Spoofing is the challenging task in face recognition systems because the human can easily trickster the system by presenting the video or photograph of the person. Many approaches are providing good results in face spoofing, but still it is challenging in intra and cross database validation. Deep learning algorithms have shown significant results in the intra and cross database. This paper used deep learning for extracting the inclusive and favorable features of the person from the face. The extracted features are used for classifying the face image as a real face or genuine face. The performance of the method is evaluated through statistical measures. The experiments were carried out NUAA and CASIA database. The method attained most promising results than other face spoofing methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image classification

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An image Thresholding Method Based on Differential Evolution Algorithm and Genetic Algorithm 10

An Image Thresholding Method Based on Differential Evolution...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Zhang, Aixin Cao, Ye Ma, Lie Jin, Can Hu, Xiang Hu, Jiwei Hubei Univ Technol Wuhan Hubei Peoples R China Wuhan Jizhi Technol Co LTD Wuhan Hubei Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781728140681

Thresholding is the simplest but most effecttive segmentation technique for image analysis. However, the computational complexity increases exponentially with the increase of threshold number in order to seek the most appropriate threshold values. Therefore, stochastic optimization algorithm are often used to overcome excessive computational problems, but the single optimization algorithm often falls into the local optimum. In general, hybrid algorithm is able to produce better performance. As a result, a parallel coupled mode(DE_GA in brief) of differential evolution algorithm (DE) and genetic algorithm (GA) is proposed for solving multi-threshold problem and The maximum variance is used as the fitness function. The experimental result displays that compared with a single algorithm, the results of the hybrid algorithm are relatively stable, which means that the parallel coupled DE_GA algorithm combined with Otsu might be an effect and practical image segmentation method.

关键词： Parallel coupled mode Genetic algorithms Differential Evolution Algorithm image segmentation, Thresholding

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Signature analysis for forgery detection 5th

Signature analysis for forgery detection

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5th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

作者： Adithya, Dinesh Rao Anagha, v.L. Niharika, M.R. Srilakshmi, N. Aditya, Shastry K. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811360008

Forgery of signature has become very common, and the need for identification and verification is vital in security and resource access control. There are three types of forgery: random forgery, simple or casual forgery, expert or skilled or simulated forgery. The main aim of signature verification is to extract the characteristics of the signature and determine whether it is genuine or forgery. There are two types of signature verification: static or offline and dynamic or online. In our proposed solution, we use offline signature analysis for forgery detection which is carried out by first acquiring the signature and then using image pre-processing techniques to enhance the image. Feature extraction algorithms are further used to extract the relevant features. These features are used as input parameters to the machine learning algorithm which analyses the signature and detects for forgery. Performance evaluation is then carried out to check the accuracy of the output. © Springer Nature Singapore Pte Ltd 2019.

关键词： Feature extraction

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Microaneurysm Detection in Diabetic Retinopathy Using Genetic Algorithm and SvM Classification Techniques 2nd

Microaneurysm Detection in Diabetic Retinopathy Using Geneti...

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2nd International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Gnaneswara Rao, Nitta Deva Kumar, S. Sravani, T. Ramakrishnaiah, N. Rama Krishna S, v. Department of CSE VFSTR University GunturAndhra Pradesh India TCS Hyderabad India Department of CSE JNTUK Kakinada KakinadaAndhra Pradesh India

ISBN: (纸本)9789811391835

Diabetics is the common disease faced by many of the people in India. It can be detected through microaneurysm. Using genetic algorithm and SvM classification techniques, sores are viewed as the most punctual indications of diabetic retinopathy. The diabetic retinopathy is an infection caused by diabetes and is considered as the significant reason for visual impairment in working age populace. The proposed technique depends on numerical morphology and comprises in expelling parts of retinal life systems to achieve the sores. This strategy comprises of four phases: (a) image acquisition;(b) Preprocessing;(c) Extracting the features using genetic algorithm;(d) Classification. The exactness of the strategy was obtained using genetic algorithm and SvM classifier is also applied to identify whether the person is diabetic or non diabetic patient. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Genetic algorithms

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image reconstruction using the modified texture synthesis algorithm

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IOP Conference Series: Materials Science and Engineering 2021年第1期1029卷

作者： R R Ibadov N v Gapon S R Ibadov S v Kucheryavenko Southern Federal University Taganrog Russia Lab. 'Mathematical methods of image processing and intelligent computer vision systems' Don State Technical University Rostov-on-Don Russia

The article discusses a method for image reconstruction based on the search for similar blocks using a texture synthesis algorithm. The effectiveness of the new approach is shown using several examples for various areas with lost pixels. The subject of the research is methods and algorithms for processing space-time reconstruction of two-dimensional signals based on a geometric model of images. The object of research is a set of test static images. The result of the study is a modification of the image reconstruction method based on the search for similar blocks in order to reduce the error in image reconstruction. The novelty of the work is an algorithm that improves the quality of image restoration. The results obtained make it possible to reduce the root mean square error.

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E-discover State-of-the-art Research Trends of Deep Learning for Computer vision

E-discover State-of-the-art Research Trends of Deep Learning...

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IEEE International Conference on systems, Man and Cybernetics (SMC)

作者： Li, Annie A. S. Trappey, Amy J. C. Trappey, Charles v. Fan, C. Y. Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Natl Chiao Chiao Tung Tung Univ Dept Management Sci Hsinchu Taiwan Natl Appl Res Lab Sci & Technol Policy & Informat Ctr Taipei Taiwan

ISBN: (纸本)9781728145693

Computer vision (Cv) attempts to mimic human eyes for image processing and identifications of detailed visual information, such as object positions, features of appearances, and even human emotions and behaviors. In this research, more than one hundred literatures, relating to applying deep learning (DL) methodologies in advanced computer visions (2010 similar to 2018), are reviewed and analyzed. The objective is to discover the state-of-the-art DL methods, topics, and trends for Cv and their practical applications. DL algorithms aim at representing multilevels of distributed neural networks. Because of the enhancement of high speed computational power, DL modeling, based on accumulated big data analytics, has found practical applications for non-supervised intelligent decision supports, such as detection of product defects and prognosis of machine malfunctions based on real-time signal or feature data analyses. There are a vast number of literature, describing DL related researches, developments, and implementations for problem solving. For the comprehensive mining of the related literature, we integrate Latent Dirichlet Allocation (LDA), K-means (Clustering), and normalized term frequency-inverse document frequency (NTF-IDF) approaches to discover, or called technology mining, of the major trends in DL for computer visions, specifically for key applications in object detection, semantic segmentation, image retrieval, and human pose estimation.

关键词： deep learning latent dirichlet allocation k-means normalized term frequency-inverse document frequency

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Rapid Artificial Intelligence Solutions in a Pandemic - The COvID-19-20 Lung CT Lesion Segmentation Challenge

Research Square

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Research Square 2021年 rs3rs-571332页

作者： Roth, Holger R. Xu, Ziyue Diez, Carlos Tor Jacob, Ramon Sanchez Zember, Jonathan Molto, Jose Li, Wenqi Xu, Sheng Turkbey, Baris Turkbey, Evrim Yang, Dong Harouni, Ahmed Rieke, Nicola Hu, Shishuai Isensee, Fabian Tang, Claire Yu, Qinji Sölter, Jan Zheng, Tong Liauchuk, vitali Zhou, Ziqi Moltz, Jan Hendrik Oliveira, Bruno Xia, Yong Maier-Hein, Klaus H. Li, Qikai Husch, Andreas Zhang, Luyang Kovalev, vassili Kang, Li Hering, Alessa vilaça, João L. Flores, Mona Xu, Daguang Wood, Bradford Linguraru, Marius George NVIDIA BethesdaMD United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Division of Diagnostic Imaging and Radiology Children’s National Hospital WashingtonDC United States Radiology and Imaging Sciences National Institutes of Health Clinical Center BethesdaMD United States School of Computer Science and Engineering Northwestern Polytechnical University China Heidelberg Germany Lynbrook High School San JoseCA United States Shanghai Jiao Tong University China Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg School of Informatics Nagoya University Japan Biomedical Image Analysis Department United Institute of Informatics Problems Belarus Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany School of Medicine University of Minho Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga/Guimarães Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal Heidelberg Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany 2Ai – Polytechnic Institute of Cávado and Ave Barcelos Portugal School of Medicine and Health Sciences George Washington University WashingtonDC United States

Artificial intelligence (AI) methods for the automatic detection and quantification of COvID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A;n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COvID-19 Lung CT Lesion Segmentation Challenge - 2020. © 2021, CC BY.

关键词： COvID-19

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