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

共 1858 条记录，以下是931-940 订阅

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Painting Style Classification Using Deep Neural Networks

Painting Style Classification Using Deep Neural Networks

引用

IEEE international conference on computer and Communication Engineering Technology (CCET)

作者： Valentin Yu. Kovalev Alexei G. Shishkin Dept. of Computational Mathematics & Cybernetics Moscow State University Moscow Russian Federation

ISBN: (数字)9781728188119

ISBN: (纸本)9781728188126

In this paper we describe the problem of painting style classification into five classes: impressionism, realism, expressionism, post-impressionism and romanticism. While most previous approaches relied on image processing and manual feature extraction from painting images, our model based on the ResNet architecture and pre-trained on the imageNet dataset operates on the raw pixel level. The training has been performed on a large dataset (about 43k images for five class style classification problem). To increase the quality of final model a large number of various augmentations were used: random Affine transform, crop, flip, color jitter (i.e. contrast, hue, saturation), normalization, a scheduler for the optimizer. Finally model weights were pruned which allowed increasing accuracy up to 51.5% and decreasing computation time as well.

关键词： Painting Feature extraction Computational modeling Biological neural networks computer vision computer architecture

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Terminal Bulb Segmentation of Caenorhabditis Elegans under Small Samples Based on Two-stage U-Net Network 2019

Terminal Bulb Segmentation of Caenorhabditis Elegans under S...

引用

Proceedings of the 2019 3rd international conference on computer Science and Artificial Intelligence

作者： Si Cao Jun Liu Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan China

ISBN: (纸本)9781450376273

Convolutional neural network based on machine learning has become one of the most popular methods of image recognition and segmentation, but it needs huge data samples to get better results. In this study, based on U-Net network, a two-stage convolution neural network method for automatic segmentation of Terminal bulb of Caenorhabditis elegans in small samples was proposed. The method solves the problem that traditional single-stage network cannot be implemented in small samples. The Dice coefficient reaches 89.5%, which is higher than that of the baseline U-Net method.

关键词： Convolutional Neural Network computer vision U-Net Caenorhabditis elegans

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An improved saliency detection method based on hypercomplex Fourier transform 3

An improved saliency detection method based on hypercomplex ...

引用

2019 3rd international conference on Electrical, Mechanical and computer Engineering, ICEMCE 2019

作者： Pan, Yongcai Zhang, Yuwei Liu, Qingzheng School of Electrical and Iinformation Engineering Guangxi University of Science and Technology Liuzhou545006 China

Saliency detection is a challenging direction in the field of computer vision. It can help us quickly obtain important information of image and video, and plays an important role in image compression, image segmentation, target recognition and other applications. Based on the Hypercomplex Fourier Transform (HFT) model, median filter and threshold segmentation are employed to obtain the image salient region, which solves the problem that the boundary is not clear and multiple targets cannot be separated, and achieves more accurate and stable detection effect. © Published under licence by IOP Publishing Ltd.

关键词： image compression

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Camera Raw image: A Study, processing and Quality Analysis 3rd

Camera Raw Image: A Study, Processing and Quality Analysis

引用

3rd international conference on Emerging Research in Electronics, computer Science and Technology (ICERECT)

作者： Murugesh, K. Mahesh, P. K. ATME Coll Engn Dept ECE Mysuru 570028 India

ISBN: (纸本)9789811358029;9789811358012

RAW is a digital file contains the camera-captured image data regarding the sensor pixels values and text information. The raw is being highlighted as digital negative and varies with the formats, which depend on hardware manufacturer. The raw processing is significant to ignore the duplication of data, to economize the space needed, to ease image file operations, and to have an uninterrupted capturing. The image quality is the substantial parametric quantity which determines the visual of the captured raw. The most extreme resolution with no inbuilt compression (raw) results in high image from any digital camera. The proposed workflow is to extract the contents of raw sensor information from the raw files and processing and displaying the information in image format. The raw test files were gathered from cameras by different manufactures. The MATLAB R2016a has been used for executing the workflow and analysis purpose. The display quality is ensured by the performance parametric-Quality of image Improvement (QOII), and also the file size reduction ratio was analyzed.

关键词： Raw image Sensor information QOII Reduction ratio

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Variation of Stability Factor of MSERs for Text Detection and Localization in Natural Scene image Using Naive Bayes Classifier 3rd

Variation of Stability Factor of MSERs for Text Detection an...

引用

3rd international conference on Information, Communication and Computing Technology (ICICCT)

作者： Soni, Rituraj Kumar, Bijendra Chand, Satish Netaji Subhas Inst Technol Dept Comp Engn New Delhi India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

ISBN: (纸本)9789811359927;9789811359910

The process of extracting textual regions from the scene images is a significant matter in the field of image processing & computer vision. It is very challenging due to different fonts, variable font size, illumination conditions and complex background etc. In last decade, image segmentation using Maximal Stable Extremal Regions (MSERs) played an important role in this area due to its various advantages. The generation of MSERs is controlled by variation of stability factor delta in deciding the promising stable areas. The aim of this paper is to study the effect of parameter delta and calculate the optimal delta on the different versions of MSER for detection and localization of text in scene images. Four different features Stroke Width Heterogeneity, Perpetual Color Contrast, Histogram of Oriented Gradients at Edges, Occupy Rate are used to evaluate the probability of text using naive Bayes Model for each version of MSERs. The Training is accomplished on the ICDAR 2013 training dataset and experiments for testing our method are carried out on ICDAR datasets to show the importance of delta (optimal value) parameter of MSER in providing the optimum results expressed as f-measure, recall and precision.

关键词： Text region extraction Natural scene images Delta MSERs Naive bayesian

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vision-based Mobile Robot's Environment Outdoor Perception 19

Vision-based Mobile Robot's Environment Outdoor Perception

引用

3rd international conference on computer Science and Application Engineering (CSAE)

作者： Jing, Punan Zheng, Wanqiang Xu, Qingyang Shandong Univ Sch Mech Electrica & Informat Engn Weihai 264209 Shandong Peoples R China

ISBN: (纸本)9781450362948

Scene perception of mobile robot is that the robot realizes the perception and understanding of the surrounding environment through a series of sensors configured by itself, and the perception technology based on vision has always been a hot and difficult point in research. Therefore, in terms of visual environment perception algorithms, there have been numerous valuable research achievements in recent years. In particular, the object detection and segmentation algorithm based on convolutional neural network has shown good performance in simple scene, but there are still some limitations when these algorithms are directly applied to actual scenes. In this paper, we study the practical application of vision-based environmental perception of mobile robots in complex scenes, presents a unified algorithm architecture of object detection and road segmentation, and build a vision-based mobile robot's environment perception system. Firstly, the image acquisition of the surrounding environment is completed by the computer camera mounted on the robot, and then the obstacle detection and the segmentation of the drivable area are achieved by using the target detection and segmentation algorithm. In order to meet the real-time requirements, the detection and segmentation algorithms share the same feature extraction network, and are jointly trained as one framework. Finally, according to the detection and segmentation results, the robot can automatically avoid obstacles and move in the drivable area.

关键词： Target detection Semantic segmentation Multi-task learning Automatic obstacle avoidance

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Research on Occlusion Relationship Recognition Based on Distance Measurement of Moving Objects in Video 19

Research on Occlusion Relationship Recognition Based on Dist...

引用

Proceedings of the 3rd international conference on Video and image processing

作者： Yandong Li Zhaogong Zhang Zongchao Huang School of Computer Science and Technology Heilongjiang University China

ISBN: (纸本)9781450376822

Aiming at the problem of moving objects occluded in the field of automatic driving, in this paper, we propose a framework for occlusion relationship recognition based on distance measurement of the moving objects in video. The framework performs moving objects detection by YOLOv3 and occlusion recognition of moving objects by the information entropy. When the occlusion occurs, the framework will obtain the distance between the moving objects and the camera by binocular stereo vision distance measurement. Finally, the framework completes occlusion relationship recognition using a principle derived from this paper. The experimental results show that distance measurement of moving objects in video is effective and feasible for occlusion relationship recognition, which can provide some help for visual technology in the field of automatic driving.

关键词： Object detection Binocular stereo vision Occlusion relationship recognition

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Determining Lead-Lag Structure between Sentiment Index and Stock Price Returns 2019

Determining Lead-Lag Structure between Sentiment Index and S...

引用

Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Alex Momotov Xianghua Xie Department of Computer Science Swansea University UK

ISBN: (纸本)9781450376259

This research contrasts and compares the state-of-the-art techniques of the two approaches within the domain of news sentiment analysis, as well as, investigates a novel document encoding representation of the 'TF-IDF momentum matrix'. The presented lexicon-based methodology is centred around Loughran & McDonald financial sentiment word lists and reaches 86.4% explained stock momentum variance, whereas the classification approach follows a thematic analysis pipeline implementing Latent Dirichlet Allocation and achieves that of 94.8%. As an additional element of model evaluation, the research implements Thermal Optimal Path method which relies on a dynamic programming approach for performance optimisation.

关键词： Lexicon Lda Stock Price Sentiment Analysis

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A Novel Simulation System Oriented Description Language of Worm Propagation 3

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

引用

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

作者： Zhang, Zhigang Wang, Tianbo Xia, Chunhe Ren, Honglin Beihang Univ Beijing Key Lab Network Technol Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Guangxi Normal Univ Sch Comp Sci & Informat Technol Guilin 541004 Peoples R China

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： computer simulation languages

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computer vision based turmeric leaf disease detection and classification: A step to smart agriculture 3

Computer vision based turmeric leaf disease detection and cl...

引用

3rd international conference on Trends in Electronics and Informatics, ICOEI 2019

作者： Kuricheti, Gayatri Supriya, P. Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781538694398

Disease identification plays a vital role in agricultural sector. Turmeric being a rhizomatous crop and well known for its therapeutic effects, monitoring such crops is crucial. The turmeric leaves are mainly exposed to diseases like Leaf Spot and Leaf Blotch. The paper develops an algorithm for detecting and preventing the spreading of diseases to the whole crop and results in high quality crop production. The data base of different leaf images was created and processed using k-Means image segmentation and leaf images textural analysis was carried out using GLCM. SVM classifier is used to classify the feature extracted images after ranking their attributes using an information gain algorithm. A GUI has been created to portray the various stages of the image processing algorithm and detect the two leaf diseases. © 2019 IEEE.

关键词： Crops

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