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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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Siamese Network for Object Tracking in Aerial Video

Siamese Network for Object Tracking in Aerial Video

引用

international conference on image, vision and Computing (ICIVC)

作者： Xiaoli Zhao Shilin Zhou Lin Lei Zhipeng Deng College of Electronic Science National University of Defense Technology (NUDT) Changsha China

ISBN: (纸本)9781538649923

In Unmanned Aerial Vehicle (UAV) videos, object tracking remains a challenge, due to its low spatial resolution and poor real-time performance. Recently, methods of deep learning have made great progress in object tracking in computer vision, especially fully-convolutional siamese neural networks (SiamFC). Inspired by it, this paper aims to investigate the use of SiamFC for object tracking in UAV videos. The network is trained on part of a UAV123 dataset and Stanford Drone dataset. First, exemplar image is extracted from the first frame and search regions are extracted in the following frames. Then, a Siamese network is used for tracking objects by calculating the similarity between exemplar image and search region. To evaluate our method, we test on a challenge VIVID dataset. The experiment shows that the proposed method has improvements in accuracy and speed in low spatial resolution UAV videos compared to existing methods.

关键词： Target tracking Object tracking Training computer vision conferences Unmanned aerial vehicles Visualization

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image Feature Extraction and Recognition of Chinese Herbal Medicine Based on Pulse Coupled Neural Networks

Image Feature Extraction and Recognition of Chinese Herbal M...

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international conference on image, vision and Computing (ICIVC)

作者： Qing Liu Xiao-Long Zha Xiao-Ping Yang Wei-Jun Ling Fei-Ping Lu Yu-Xiang Zhao School of Electronic Information & Electrical Engineering Tianshui Normal University Tianshui China

ISBN: (纸本)9781538649923

In order to effectively extract the characteristic information of microscopic image feature to Chinese herbal medicines (CHM), and improve the recognition accuracy automatically, a novel algorithm using Pulse Coupled Neural Networks (PCNN) is put forward. Firstly, the PCNN model is introduced from suitable for processing image of biological tissue. Secondly, the characteristic of time series with PCNN image processing is formed, and transformed into the feature of one dimensional entropy series, which can behalf the image inherent characteristics. Finally, the automatic identification is taken to the extracted image entropy sequence feature. The experimental results show that the entropy sequence feature has the ability of anti-geometric distortions, the novel method have characteristics of simple extraction approach, little extraction parameter, easy implementation, higher accurate recognition ratio and strong robustness.

关键词： Entropy Time series analysis Feature extraction Neurons image recognition Microscopy Mathematical model

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State Recognition of Electric Control Cabinet Switches Based on CNNs

State Recognition of Electric Control Cabinet Switches Based...

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international conference on image, vision and Computing (ICIVC)

作者： Xingang Mou Conglin Leng Xiao Zhou Yichao Cai School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781538649923

When using the CNNs for image processing, it is more easy to learn the invariant features of the image without too much preprocessing. This is important for the task of image classification and recognition which need to extract deep features of the object. This paper proposes a switch state recognition algorithm which combines digital image processing technique and CNNs for the electrical control cabinet switches in substations. Firstly, use perspective transformation method to rectify the original image which is positioned and segmented using the projections of horizontal and vertical gradient. Then label the segmented images to get sample sets for training CNNs later. After that, design the architecture of CNNs with image processing knowledge and use the designed CNNs to extract the key features of switch state, build and train the CNNs model using labeled “disconnected” and “closed” switches. Finally, the state of the switch is recognized by the trained model and showed in the image. The experimental results show that the algorithm has the characteristics of high recognition accuracy, good robustness and fast recognition speed.

关键词： Training Convolution Feature extraction image recognition image segmentation Kernel Substations

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Crack Detection and images Inpainting Method for Thai Mural Painting images

Crack Detection and Images Inpainting Method for Thai Mural ...

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international conference on image, vision and Computing (ICIVC)

作者： Salinee Jaidilert Ghulam Farooque School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781538649923

Thailand frescoes are an important art heritage in the world. However, the erosion of history has resulted in the color loss, stain and scratches of many mural paintings. How to repair the Thailand murals has become an urgent problem. It is an important scientific problem to use computer image inpainting technology to simulate and eliminate the missing pixels in the murals and obtain beautiful and intact murals. In this paper, a computer aided semi-automatic repair framework is proposed by combining a scratch detection procedure and a model optimization based inpainting procedure. To this end, we propose a scratch semi-automatic detection method. In this method, a small number of seed points are given by users, and the location of scratches is then computed by region growing method and morphological operation. After that, the pixel filling and color restoration in the missing region can be obtained by using different variational inpainting methods. The experiment shows that the proposed method is effective.

关键词： Painting Mathematical model Transforms image restoration TV Morphology Numerical models

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Hybrid CPU and GPU computation to detect lung nodule in computed tomography images 3

Hybrid CPU and GPU computation to detect lung nodule in comp...

引用

3rd international conference on Informatics and Computing, ICIC 2018

作者： Sentana, I. Wayan Budi Jawas, Naser Wardani, Anggun Esti Department of Information Systems Bali State Polytechnic J1. Raya Bukit Jimbaran Badung Bali Indonesia Department of Computer System STMIK STI and KOM Bali Jalan Raya Puputan No. 86 Renon Denpasar Indonesia Radiology Unit Airlangga University Hospital Jalan Mayjen Dr. Moestopo No. 6-8 Surabaya Indonesia

ISBN: (纸本)9781538669204

Lung Nodule is a white patch on the thorax medical image, usually used as an early marker of lung cancer. Although there were some research deals with lung nodule detection, but none of those researches tailoring Graphical processing Unit (GPU) to assist the computing process. This research aims to produce algorithms that can detect lung nodules automatically in CT images, by utilizing a combination of hybrid computing between Central processing Unit (CPU) and Graphical processing Unit. The framework used is Compute Unified Device Architecture, which consists of platform and programming model. The algorithm consists of several steps: read dicom and data normalization, lung segmentation, candidate nodule extraction, and classification. Normalization is required to facilitate calculation by changing the data type ui16 to ui8. Furthermore, segmentation is used to separate the lung parts with other organs, where at this stage the Otsu Algorithm and Moore Neighborhood Tracing (MNT) are used. The next step is Lung Nodule Extraction, which aims to find the nodule candidate. The last step is a classification that utilizes the Support Vector Machine (SVM) to distinguish which one is nodule or not. The algorithm successfully detects near round nodules that are free-standing or not attached to other parts of organs. After undergoing ground truth tests, it was found that under some conditions, the algorithm has not been able to distinguish nodules and other strokes that resemble nodules. While in terms of computing speed is found a very surprising result because overall single CPU computing provides better results compared to hybrid CPU and GPU computing. © 2018 IEEE.

关键词： Program processors

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A Self-Adaptive and Dynamic image Encryption Based on Latin Square and High-Dimensional Chaotic System

A Self-Adaptive and Dynamic Image Encryption Based on Latin ...

引用

international conference on image, vision and Computing (ICIVC)

作者： Huajian Mou Xinyan Li Gao Li Dianjun Lu Rui Zhang College of Computer Engineering Yangtze Normal University Chongqing School of Mathematics and Statistics Yangtze Normal University Chongqing Unit 75841 of PLA Changsha School of Mathematics and Statistics Qinghai Normal University Xining China College of Computer Science Chongqing University Chongqing

ISBN: (纸本)9781538649923

This paper proposes a self-adaptive and dynamic image encryption scheme based on Latin square and high-dimensional chaotic system. In proposed scheme, high-dimensional chaotic system and a combination of permutations generate a novel Latin square, which called SLS for short. In image encryption, SLS can be used as scrambling key and diffusion key during encrypting the image. And SLS contains original features and possess self-adaptive, which is employed with dynamic encryption repeatedly. The experimental results and security analysis demonstrate that this scheme is of high security and good recovery result, which can resist brute-force attack, and differential attacks.

关键词： Encryption Ciphers Heuristic algorithms Histograms Correlation

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Attention Prediction Using Partial Differential on Color Spaces

Attention Prediction Using Partial Differential on Color Spa...

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international conference on image, vision and Computing (ICIVC)

作者： Zhiyong He Song Lin School of Mechanical and Electric Engineering Soochow University Suzhou China

ISBN: (纸本)9781538649923

Human attention prediction has attracted significant interest in recent years due to its promising contributions to various computer vision applications. In this paper, we present a simple and effective framework for extracting meaningful features from the color space. The feature extraction is based on computing the second order partial differential for the input image. The features extracted from the RGB and LUV spaces are combined to form a saliency map of natural images. Based on the public dataset used in previous works, we compare our proposed algorithm with several competitive approaches presented in the literature and the results demonstrate that the proposed method is effective. We also apply the feature based on the LUV space to improve some of the previous approaches, and our experimental results show that the application is meaningful for enhancing the accuracy of human attention prediction.

关键词： Feature extraction Visualization Computational modeling image color analysis Estimation Sun Brain modeling

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Research of image Main Objects Detection Algorithm Based on Deep Learning

Research of Image Main Objects Detection Algorithm Based on ...

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international conference on image, vision and Computing (ICIVC)

作者： Liyan Yu Xianqiao Chen Sansan Zhou School of Computer Science and Technology Wuhan University of Technology Wuhan China

ISBN: (纸本)9781538649923

images have many objects in complex background. How to identify these objects and identify the main objects therein and understand the relationship between the main objects and other objects are the focus of this parper. There are many ways to object recognition, but most of them cannot mark the main objects of the image. In this paper, we use improved RCNN [1] network to detect and recognize multi-object in the image. Then we put forward the main objects scoring system to mark the image main objects. The experimental results show that the algorithm not only maintains the superiority of RCNN, but also detects the main objects of the image. We found that the image main objects were related to the size of candidate region and the rarity of objects.

关键词： Feature extraction Object detection Convolution Support vector machines Object recognition Neural networks image annotation

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Surface Defect Detection Based on Gradient LBP

Surface Defect Detection Based on Gradient LBP

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international conference on image, vision and Computing (ICIVC)

作者： Xiaojing Liu Feng Xue Lu Teng College of Electronic Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781538649923

The LBP histogram obtained based on the local binary pattern (LBP) method usually has a higher dimension, and not conducive to calculation. The LBP method adopts the gray difference value between single points as the LBP output value, which is not robust to noise and illumination. Therefore, this paper improves the traditional LBP method and proposes a surface defect detection method based on gradient local binary pattern (GLBP), which uses image sub-blocks to reduce the dimensionality of the LBP data matrix. The method adopts weighted binary output values in eight directions within the neighborhood to indicate local gray changes, which suppresses the effects of light and noise on the detection results. Experiments show that the method can determine the defect location well and provide good feature information for subsequent defect classification.

关键词： image segmentation Surface cracks computer vision Presses Electronic mail Gray-scale Surface treatment

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A Fast Iterative Shrinkage Thresholding Algorithm for Single Particle Reconstruction of Cryo-EM

A Fast Iterative Shrinkage Thresholding Algorithm for Single...

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international conference on image, vision and Computing (ICIVC)

作者： Huan Pan You Wei-Wen Tieyong Zeng College of Mathematics and Computer Science Hunan Normal University Changsha Hunan P. R. China

ISBN: (纸本)9781538649923

Single particle reconstruction (SPR) from cryo-electron microscopy (cryo-EM) is an emerging technique for determining the three-dimensional (3D) structure of macromolecules. A major challenge in single particle reconstruction from cryo-electron microscopy is to establish a reliable ab initio three-dimensional model using two-dimensional projection images. In this paper we introduce a fast proximal gradient method (FISTA) to solve the corresponding optimization problem of Single particle reconstruction. Numerical experiments with simulated images demonstrate that the proposed methods significantly reduce the estimation error and improved reconstruction quality.

关键词： image reconstruction Power capacitors Periodic structures Iterative algorithms Microscopy Gradient methods

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