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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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Stixel World Based Long-Term Object Tracking for Intelligent Driving 1

引用

3rd international conference on Cognitive Systems and Information processing (ICCSIP)

作者： Deng, Liuyuan Yang, Ming Wang, Chunxiang Wang, Bing Shanghai Jiao Tong Univ Minist Educ China Dept Automat Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Res Inst Robot Shanghai 200240 Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

Long-term object tracking is key for a higher level of semantic interpretation of driving environment. One of the state-of-the-art approaches for long-term tracking is Tracking-Learning-Detection (TLD), which, however, suffers from variability of on-road objects and moving cluttered background. This paper presents a long-term object tracking method for intelligent driving based on Stixel World to address the drifting problem. First, this method adopts TLD framework, and integrates intensity and depth cues into the detector and learning component. Next, this method introduces Stixel World for compact medium-level representation of the 3D world, and Attention Guiding Filter (AGF) is proposed to focus on relevant areas in the image. Experiments in real traffic scene show the outstanding long-term tracking performance for intelligent driving.

关键词： Object tracking Stixel World Stereo vision TLD

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Segmentation and Extraction for the Diamond Grain image Based on the Gauss-polymerized Enhancement

Segmentation and Extraction for the Diamond Grain Image Base...

引用

2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： Yanfen Lin Congfu Fang Ying Deng Department of Computer Science and Engineering Xiamen Institute of Technology College of Mechanical Engineering and Automation Huaqiao University

To solve the problem of many background noises in image segmentation of diamond grains under the complicated texture background, an image segmentation and extraction method for diamond grains based on the Gauss-polymerized enhancement was proposed in this work. Firstly, the original diamond grain image was pre-processed by the method of histogram equalization in order to enhance the overall contrast effect of the image. Secondly, the image was further processed by the method of Gauss-polymerized enhancement through setting proper Gauss filter parameters, which purpose is to enhance the boundary definition between the grain and the matrix, and reduce the complex background noises. On this basis, the morphological dilation was used to eliminate the non-interested regions of the complex background noises, and obtain the segmentation region of the interested target grain. Finally, the diamond grain can be extracted from the grain image based on the segmented region of the target grain through the operation of image multiplication. The results show that the proposed method can effectively reduce the influence of the complex texture background on the segmentation and extraction of the target grain. The extracted image of the target grain can be further used to analyse the grain size characteristics and grain wear pattern identification in a higher level.

关键词： image Segmentation and Extraction for the Diamond Grain image Based on the Gauss-polymerized Enhancement

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A Space-time Anti-interference Algorithm Based on Antenna Rotation

A Space-time Anti-interference Algorithm Based on Antenna Ro...

引用

2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： DU Ruiyan YANG Jiaqi WANG Xianchao LIU Fulai Engineer Optimization & Smart Antenna Institute Northeastern University at Qinhuangdao School of Computer Science and Engineering Northeastern University

To inhibit interference, the conventional space-time adaptive processing technology can form nulling at both the interference coming angle and the interference frequency, inhibiting the interference in the development of satellite navigation anti-jamming technology. When the frequency of the signal source is equal or similar to the central frequency of the interference source and they are adjacent in the spatial domain, the desired signal will be suppressed in the traditional algorithms, which will lead to the decrease of the output signal-tointerference-noise ratio(SINR).To overcome this issue, a space-time joint anti-interference algorithm based on planar array steering is proposed in this paper. Specifically, the plane array steering is adjusted by calculating the spatial correlation coefficient of the desired signal and the interference signal. Simulation results show that the proposed algorithm can increase the output SINR by about 10 dB under the same frequency, and the anti-interference ability of the system is enhanced.

关键词： domain A Space-time Anti-interference Algorithm Based on Antenna Rotation

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A Novel Approach for Mammogram Classification

A Novel Approach for Mammogram Classification

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IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Deepika Nagthane Archana Rajurkar Computer Science and Engineering Research Scholar at MGM's College of Engineering Nanded India Computer Science and Engieering Professor and Head at MGM's College of Engineering Nanded India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

One of main reason for increase in mortality rate of woman is breast cancer. Accurate early detection of abnormalities in breast tissues seems to be the only solution for diagnosis. These detected abnormalities need to be classified for further investigation. In breast cancer research various classification techniques have been proposed to reduce the diagnostic test false positives because of the subtle appearance of the breast cancer tissues. Accurate early classification of mammogram is essential to reduce diagnostic test false positives. Hence, an attempt is made to presents an automated classification technique for mammogram. This work is divided into four steps: Pre-processing, Feature Extraction, Feature Selection and classification. In this approach cropping and resizing is used as a preprocessing technique. In next step Texture, wavelet and PCA features are extracted form mammogram, cuckoo search algorithm is enhanced to select optimal feature split points. Finally, classification is achieved using association rule agreement-based classifier model. It classifies mammogram as normal or cancerous using association rules generated from selected features. The experiments are carried out on Mammographic image Analysis Society (MIAS) database and performance of proposed method is validated over existing work using accuracy, sensitivity and specificity measures. The proposed technique has achieved accuracy 0.8289, sensitivity 0.9333 and specificity 0.7273. Results indicate the usefulness of proposed method in classification task.

关键词： Feature extraction Mammography Classification algorithms Breast cancer Sensitivity Decision trees

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Research on Large Screen Visualization Based on Docker

Research on Large Screen Visualization Based on Docker

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2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： Xin Liu Wenfeng Shen Baohua Liu Qin Li Rong Deng Xuehai Ding Shanghai Institute for Advanced Communication and Data Science School of Computer Engineering and Science Shanghai University

Docker is a system-level virtualization container technology, which is commonly used to package application software and provide rapid deployment services based on nongraphical interfaces to resolve dependency conflicts and version iteration problems. However, there are applications that require graphical user interfaces and user interactions, such as largescreen visualization. This work proposes a solution to use Docker and GPU to deploy largescreen visualization applications to display images, videos, WebGL applications, etc. We built a Docker cluster and deployed a large-screen visualization system by launching a microservice, which contains 28 containers, and mapping the host's X11(X11 is widely used to provide a graphical interface to Unix/Linux users) Unix socket. At the same time, we tested the performance and compared it with the circumstance that applications ran on local host. It is concluded that the solution can achieve a good result when applied to large-screen visualizations with acceptable performance degradation.

关键词： Research on Large Screen Visualization Based on Docker service

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An Automated Tomato Quality Grading using Clustering based Support Vector Machine

An Automated Tomato Quality Grading using Clustering based S...

引用

international conference on Communication and Electronics Systems

作者： Shweta S. Deulkar Sunita S. Barve School of Computer Engineering and Technology MIT Academy of Engineering Pune India

ISBN: (纸本)9781538647660

This paper focus on representing the technique of fruit grade classification, automated machine vision based technology has become more potential and important to many areas like agricultural sector and food processing industry. The proposed system which calculates the grade of fruit based on its external features. Grading of fruits is one of the most important processes in post harvesting, but this procedure is mostly carried out manually which is not efficient as it requires enormous number of employment, and tends to human error. The grading process is carried out by capturing the fruit image using digital camera and this image is interpreted using image various processing techniques. Color is very prominent feature for recognizing defect and ripeness of the fruit. The major objective is to check the fruit quality with high speed for analyzing maximum number of fruits in least amount of time. The conventional process of fruit quality assessment needs new tools deciding the quality of fruit. This system performs color features and size of fruit and captures the fruit side view image. The Otsu thresholding and K-Means clustering algorithms are used to extract the features of fruit. This system achieve the fruit quality sorting using Support Vector Machine and gives a few advantages over conventional techniques. This system will help in the advancement of an automated non destructive grading system with high speed, high accuracy and low cost. Implementation of this system will have applications in fruit quality grading in field like food science and trades where standardization is essential.

关键词： Tomato grading Support vector machine computer vision Classification Clustering

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A New Method to Detect Moving Targets

A New Method to Detect Moving Targets

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2018 3rd international conference on Education, Management and Systems Engineering(EMSE 2018)

作者： Yang-ming HE Ya-fen WU School of Computer Jiangxi University of Traditional Chinese Medicine Office of Academic Research Jiangxi University of Traditional Chinese Medicine

In the moving target detection, there are a lot of methods. But the effects of those algorithms are limited, especially in complex environment. This paper putts forward a new method to detect moving targets, which uses virtual binocular disparity. It is called virtual because the environment uses one camera but the theory is about two cameras. Virtual binocular disparity algorithm regards moving targets as static targets under two cameras. It uses two neighboring images, the current frame image and the next frame image. The algorithm looks the two images as two static images under two cameras, then uses stereo match theory to calculate the disparity of the neighboring images. Because the background of the two images is the same, the disparity of their background is about zero. And the disparity of moving targets is positive or negative. The sign of disparity means the direction of targets moving. So we can use the sign of disparity to distinguish the targets moving in different directions. The direction detected out will be helpful for subsequent work, for example, targets tracking and counting etc. This algorithm fully uses the continuity property of moving targets, so it can have good effect. In the end of the paper, experiments are used to prove the algorithm.

关键词： Virtual binocular disparity Stereo match Moving targets image processing

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An Automated Approach for Diagnosing Diabetic Retinopathy in Retinal Fundus images

An Automated Approach for Diagnosing Diabetic Retinopathy in...

引用

IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Vidya Prasannan C. Sathish Kumar V Deepa Rajiv Gandhi Institute of Technology Kottayam India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

Diabetes mellitus and its associated complications, including diabetic retinopathy (DR) have been identified as a growing global public health problem. Early treatment and constant monitoring of DR can prevent 90 percent of visual loss. Manual screening of fundus images by ophthalmologists is tedious and time consuming. Hence, a computer-aided diagnosis system can significantly act as an efficient solution for the time consuming detection process. The propounded algorithm includes various stages such as pre-processing, feature extraction and classification. In literature, different approaches for preprocessing such as dual guassian, second derivative of Guassian and Gabor filter, mathematical morphology, K-mean clustering algorithms etc. are employed. In the proposed algorithm, preprocessing was done using Adaptive Histogram Equalization (AHE) and image de-noising using non-local means (NLM) filter. NLM takes the means of all the neighbouring pixels and weightage is given by checking the similarity between these pixels to the target pixel. Laplacian edge detection, by identifying the zero crossings of the second derivatives of the image intensity was used in both the methods. In the feature extraction stage, features like HOG, area, perimeter, mean and standard deviation are extracted. Lastly, Support Vector Machines (SVM) algorithm, k-nearest neighbours (KNN) and Artificial Neural Network (ANN) was used for the categorization of the images to healthy and diseased. The comparison on the functioning of AHE and NLM validates that NLM outperforms AHE. Parameters like accuracy, error rate, specificity, and precision were used to analyze the working of the classifiers. SVM with 0.875 accuracy, 0.12 error rate, 0.75 specificity and 0.80 precision outperformed well when analogized to the other methods.

关键词： Feature extraction Retina Support vector machines Diabetes image edge detection Retinopathy Standards

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A Novel Simulation System Oriented Description Language of Worm Propagation

A Novel Simulation System Oriented Description Language of W...

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2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： Zhigang Zhang Tianbo Wang Chunhe Xia Honglin Ren Beijing Key Laboratory of Network Technology Beihang University School of Computer Science and Engineering Beihang University School of Cyber Science and Technology Beihang University School of Computer Science and Information Technology Guangxi Normal University

Software simulation is one of the most common methods to study worm propagation. However, simulation process and software interface, which lack unified description, cause inefficiencies of worm propagation simulation. In order to achieve a unified description for worm propagation simulation process, we propose a Worm Propagation Simulation Description Language(WPSDL), which characterizes the software interfaces and parameters of each module in simulation process in a unified way. Meanwhile, we develop a simulation system of worm propagation oriented our presented language. In this system, we use related commands of the description language as interactive instructions, and then implement worm propagation simulation for different types of network worms. In addition, we conduct a series of experiments to analyse the impacts of worm propagation mechanism and network topology structure. The experiments show that increasing infection ability and scanning rate can improve the propagation scale significantly, and the propagation delay can influence the outbreak time and explosion intensity of worm propagation. The experiments not only provide support for the study of worm propagation mechanisms, but also verify the practicability of the description language and the simulation system.

关键词： simulation A Novel Simulation System Oriented Description Language of Worm Propagation Simulation

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A Survey Paper on Plant Disease Identification Using Machine Learning Approach

A Survey Paper on Plant Disease Identification Using Machine...

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international conference on Communication and Electronics Systems

作者： Jyoti Shirahatti Rutuja Patil Pooja Akulwar Computer Science & Engineering Department Sanjay Ghodawat Group of Institutions Computer Science & Engineering Department Sanjay Ghodawat University

ISBN: (纸本)9781538647660

Agriculture plays an important role in farmer's life. Sometimes manual identification of disease is time consuming and need of labor is more. One of the most important facts that reduce the growth of plants is disease attack. Overall study about agriculture shows that quality and quantity of agricultural products may be reduced due to various factors of plant diseases. These diseases can be more easily identified by using machine learning approach as compared to manual method. Hence machine learning method can be used to identify the affected leaf images. images captured by camera will be processed using different image processing technique. These techniques will help in identifying plant diseases thereby increasing the yield of plants. This survey paper describes plant disease identification using Machine Learning Approach and study in detail about various techniques for disease identification and classification is also done.

关键词： Feature extraction image processing techniques SVM PNN ANN and GA

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