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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15613 条记录，以下是4261-4270 订阅

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Fast algorithms for Poisson image Denoising Using Fractional-Order Total Variation 12th

Fast Algorithms for Poisson Image Denoising Using Fractional...

引用

12th International conference on Genetic and Evolutionary Computing, ICGEC 2018

作者： Zhang, Jun Ma, Mingxi Deng, Chengzhi Wu, Zhaoming Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology NanchangJiangxi330099 China College of Science Nanchang Institute of Technology NanchangJiangxi330099 China

ISBN: (纸本)9789811358401

In this paper, a new Poisson image denoising model based on fractional-order total variation regularization is proposed. To obtain its global optimal solution, the augmented Lagrangian method, the Chambolle’s dual algorithm and the primal-dual algorithm are introduced. Experimental results are supplied to demonstrate the effectiveness and efficiency of the proposed algorithms for solving our proposed model, with comparison to the total variation Poisson image denoising model. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Constrained optimization

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Features extraction and dataset preparation for grading of ethiopian coffee beans using image analysis techniques 3rd

Features extraction and dataset preparation for grading of e...

引用

3rd International conference on Information and Communication Technology for Intelligent systems, ICTIS 2018

作者： Subramanian, Karpaga Selvi Vairachilai, S. Gebremichael, Tsadkan School of Electrical and Computer Engineering Ethiopian Institute of Technology-Mekelle Mekelle University Mekelle Ethiopia Faculty of Science and Technology IFHE University Hyderabad India School of Electrical and Computer Engineering Mekelle University Mekelle Ethiopia

ISBN: (纸本)9789811317415

Coffee is the natural gift of Ethiopia. Generally the export quality washed coffee beans of Ethiopia are classified into two grades and sundried coffee beans into five different grades based on their number of defects. The objective of the research is to extract the features of green coffee beans from the images which would be helpful on classifying the coffee beans to different grades by an automated system. Different image processing techniques are applied on the images to perform preprocessing, segmentation and feature extraction. The extracted features of the coffee beans from images are broadly classified as morphological, textural and color. The morphological feature includes area, perimeter, major axis length, minor axis length, Eccentricity. Energy, Entropy, contrast and homogeneity are the information relevant to texture. Individual color component values of the three primary colors, along with its hue, intensity and saturation values are extracted for color features of the coffee beans. Automated classification and machine learning algorithms needs a data set for further processing. Data mining applications for discovering different patterns for various grades and knowledge discovery need a highly accurate dataset. Hence, preparing such data set by applying image processing techniques is becoming the objective of this research study. The objective is realized with a dataset of 100 observations for each grade with morphological, textural, and color features. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Anomalies detection in social services data in the sphere of digital economy 5

Anomalies detection in social services data in the sphere of...

引用

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

作者： Khripunov, P.V. Minaev, E.Y. Protsenko, V.I. Davydov, N.S. Nikonorov, A.V. Samara National Research University Moskovskoe Shosse 34A Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

This article addresses the study of the anomaly and fraud detection problem in the data from social services. The problem of detecting anomalies is extremely relevant for data-driven processes in the digital economy. In this paper, we propose a two-step approach for the detection of anomalies using auto-encoders and the conjugacy indicator. An experimental study of the efficiency of the proposed algorithms was conducted using open-access data set. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Anomaly detection

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A Survey on Deep Learning-Based Source Code Defect Analysis

A Survey on Deep Learning-Based Source Code Defect Analysis

引用

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

作者： Zhibin Guan Xiaomeng Wang Wei Xin Jiajie Wang Li Zhang China Information Technology Security Evaluation Center Beijing China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

With the rapid development of information technology, various software applications are flooding our daily lives. The development of these application software inevitably generates a lot of source code. How to detect and analyze various defects in the source code, such as API/Function call errors, array misuse, and expression syntax error, etc., which is known as source code defect analysis (SCDA), has attracted the attention of many researchers in the academic field. Since artificial intelligence (AI) technology has achieved excellent results in the field of image processing and natural language processing, researchers have tried to use deep learning algorithms in AI to automatically extract and analyze features of source code. Therefore, we review the recent deep learning-based source code defect analysis methods, including abstract syntax tree-based methods, program dependency graph-based methods, and other deep learning-based methods. Compared to traditional methods, the deep learning-based code defect analysis methods can realize the automatic extraction of source code defect features. This means that there is no longer a need for human experts to pre-define code features, which avoids errors caused by humans to a certain extent. The application research of AI in the source code defect analysis is an interesting and challenging development direction, and we believe it has broad development prospects.

关键词： Feature extraction Syntactics Deep learning Semantics Natural language processing Data mining Data models

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Semantic processing of radar spectral information for air object recognition

Semantic processing of radar spectral information for air ob...

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2019 IEEE International Scientific-Practical conference: Problems of Infocommunications Science and Technology, PIC S and T 2019

作者： Solonska, Svitlana Zhyrnov, Volodymyr Holovin, Oleksiy Kharkiv National University of Radio Electronics Kharkiv Ukraine Equipment of the Armed Forces of Ukraine Central Research Institute of Armaments and Military Kiev Ukraine

ISBN: (纸本)9781728141848

The results of the study of semantic processing of spectral information in the intelligent radar systems are presented. A method for formalizing the processes of perception and transformation of spectral images based on algebra of finite predicates is developed. A model for semantic processing of spectral image for surveillance radars has been developed and implemented. To analyze the features of clutter spectra and air object signals authors investigate the decision making algorithms of a human-operator. Spectral pattern is described as a predicate on the set of spectral channels that exceed the certain threshold value. The predicates-signs are introduced to indentify spectral types. Instantaneous spectrum is corresponded to one type of spectrum by a combination of these predicates. A functional diagram for automatic identification of spectral types, based on these equations, is built. The algorithm for analyzing of the clutter spectrum and recognizing of the air objects, based on algebra of finite predicates, which is proposed in the paper, allows to define all signs and automatically recognize air objects in real time. © 2019 IEEE.

关键词： Algebra

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A Real-time Tracking System Used for Surface Detection

A Real-time Tracking System Used for Surface Detection

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International conference on Information Science and Control Engineering (ICISCE)

作者： Xiao Wang Bo Li Zhankuan Zhang Chinese Academy of Forestry Research Institute of Wood Industry Beijing China

In industrial production, the high speed of object on conveyor always causes motion blur when computer vision is used to achieve appearance inspection, results in difficulties in image processing. In this study, a real-time tracking system based on cam transmission was applied to eliminate the relative movement between camera and objects. The outline of the cam was specifically designed to meet the requirement of real-time tracking and simplified the motor control. To test the system, the velocities of both camera and object were collected using rotary encoders. It was found that the camera velocity would be almost equal to that of object in a certain period when cam rotated uniformly. Also, the images were captured by triggering the camera at the suitable moment, and it was shown that the object in image was very clear. Compared with conventional restoration algorithms, the proposed method resulted in clearer images without any distortion, and could save computation time, which was especially suitable for on-line inspection in industrial production such as boards.

关键词： Tracking Process control Production Inspection Cameras Distortion Real-time systems

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Building 3D Map Based on Monte Carlo Localization and Feature Extraction

Building 3D Map Based on Monte Carlo Localization and Featur...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Zhun Fan Ying Chen Chong Li Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou Guangdong China School of Electrical and Information Engineering Shantou University Shantou Guangdong China

ISBN: (数字)9781728190105

ISBN: (纸本)9781728190112

In this project, we mainly explore and construct the 3D indoor map. The goal of this paper is to merge the data from the laser and kinect sensor with Monte Carlo Location(MCL) in 2D map. We use the laser range sensor to construct the 2D map and get the robot's pose transformation matrix. Then, we obtain the color and depth image by using the kinect sensor and build 3D point cloud map by using the feature extraction method, getting the kinect's pose transformation matrix. After that, we get the optimal pose transformation by using the Kalman Filter to calibrate the robot's pose transformation matrix and the kinect's pose transformation matrix. Finally, the optimal pose transformation matrix is employed to accomplish the local 3D map and construct the global 3D indoor map. To show the superiority of our method, we make some experiments and compare with some other algorithms. Experimental results show that our method has a better superiority.

关键词： Three-dimensional displays Robots Robot sensing systems Two dimensional displays Kalman filters Feature extraction Buildings

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Patch-based image despeckling using low-rank hankel matrix approach with speckle level estimation 17

Patch-based image despeckling using low-rank hankel matrix a...

引用

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

作者： Kim, Hansol Oh, Paul Lee, Sangyoon Kang, Moon G.I. School of Electrical and Electronic Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of

In this study, we propose an image despeckling method based on low-rank Hankel matrix approach and speckle level estimation. Annihilating filter-based low-rank Hankel matrix, so called ALOHA approach is very useful to various areas, such as image inpainting and impulse noise reduction. The proposed method utilizes this approach because it provides high performance in completing irregularly subsampled images. Speckled image are subsampled using patch-based speckle level estimator which selects pixels with low speckle level and abandon the others. The subsampled image is reconstructed using low-rank structured matrix completion. Our experimental results demonstrate that precisely estimated speckle level improves despeckling performance significantly. The accuracy of the proposed speckle estimator validates better despeckling performance of the proposed method compared with conventional despeckling methods. © 2019, Society for Imaging Science and Technology.

关键词： Speckle

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Benchmarking Machine-Learning-Based Object Detection on a UAV and Mobile Platform

Benchmarking Machine-Learning-Based Object Detection on a UA...

引用

IEEE Wireless Communications and Networking conference (WCNC)

作者： Martinez-Alpiste, Ignacio Casaseca-de-la-Higuera, Pablo Alcaraz-Calero, Jose Grecos, Christos Wang, Qi Univ West Scotland Sch Engn & Comp Paisley Renfrew Scotland Univ Valladolid Lab Proc Imagen Valladolid Spain Cent Washington Univ Comp Sci Dept Washington DC USA

ISBN: (纸本)9781538676462

Object detection systems mounted on Unmanned Aerial Vehicles (UAVs) have gained momentum in recent years in light of the widespread use cases enabled by such systems in public safety and other areas. Machine learning has emerged as an enabler for improving the performance of object detection. However, there is little existing work that has studied the performance of the machine learning approach, which is computationally resource demanding, in a portable mobile platform for UAV based object detection in user mobility scenarios. This paper evaluates an integrated real-world testbed for this scenario, by employing commercial-off-the-shelf devices including a UAV system and a machine-learning-enabled mobile platform. It presents benchmarking results about the performance of popular machine learning and computer vision frameworks such as TensorFlow and OpenCV and the associated algorithms such as YOLO, embedded in a smartphone execution environment of limited resources. The results highlight opportunities and provide insights into technical gaps to be filled to realize real-time machine-learning-based object detection on a mobile platform with constrained resources.

关键词： Machine learning object detection image processing mobile platform UAV

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Review on Deep Learning Methods Used for Computer-aided Lung Cancer Detection and Diagnosis 19

Review on Deep Learning Methods Used for Computer-aided Lung...

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Proceedings of the 2019 2nd International conference on algorithms, Computing and Artificial Intelligence

作者： Firdaous Essaf Yujian Li Seybou Sakho Mesmin J. Mbyamm Kiki School of Computer Science and Technology Beijing University of Technology Beijing China School of Artificial Intelligence Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (纸本)9781450372619

Cancer is one of the diseases with high mortality rates in the 21st century, with lung cancer being the first in all cancer morbidity and mortality rates. In recent years, with the large rise of data and artificial intelligence researches, the auxiliary diagnosis of lung cancer based on deep learning has gradually become a hot research *** the available and public datasets for lung cancer are mainly CT scans images with lung nodules annotations, the work on the assisted diagnosis of lung cancer using deep learning is mainly based on image data preprocessing, Pulmonary nodule segmentation, and lesion analysis and ***-aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. So our aim is to help the development of a new CAD system with higher performance than the existent ones to assist lung cancer detection in early stages. This paper presents an overview of the deep learning methods used for computer-aided lung cancer detection and diagnosis. It is mainly focused on the important processing and analyzing methods for the pulmonary image data obtained by medical instrument imaging, and which we can summarize into these 4 steps: Medical image data preprocessing, Pulmonary nodule segmentation, pulmonary nodule detection, and finally lesion diagnosis A full description of the CAD systems steps is given along with an overview of the state of art deep learning medical image processing methods.

关键词： computer-aided diagnosis Deep learning Medical Imaging processing Lung cancer

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