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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1830 条记录，以下是1131-1140 订阅

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vision Based Trajectory Following Robot and Swarm 2

Vision Based Trajectory Following Robot and Swarm

引用

2nd international conference on Control and Robotics Engineering (ICCRE)

作者： Haque, Ishraq Ul Al Arabi, Abul Hossain, Shadat Proma, Tanjina Uzzaman, Nafi Amin, M. Ashraful IUB Dept Comp Sci & Engn Comp Vis & Cybernet Grp Plot 16Block B Dhaka 1229 Bangladesh

ISBN: (纸本)9781509037742

Robots based on vision guidance are very important to the future of vehicle navigation. They have the ability to navigate through a predetermined track and avoid obstacles. They operate remotely with a central computer tracking and guiding them. This allows for communication between robots. This can be used to implement traffic based navigation. Where routes can be recalculated depending on traffic flow. Use of server based system paves the way to collaborate among different robots.

关键词： trajectory follower swarm robotics image processing cloud computing

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Case Study for Graph Signal Denoising by Graph Structure Similarity 2

Case Study for Graph Signal Denoising by Graph Structure Sim...

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2nd international conference on image, vision and Computing (ICIVC)

作者： Song, Dao-Bang Zhang, Jiu-Wen Zhou, Jing Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9781509062386

We study the denoising performance of several graph wavelet filter banks and image modeling with graph to enhance understanding of the relationship between graph signals and underlying structure. We design a new metric for measuring graph structure similarity (GSSIM) to evaluate those methods. GSSIM is positively related to PSNR as an index of corrupted signal with an additive Gaussian noise in the references and it is aware of graph structure. Subgraph-Based filter banks are superior to others in graph signal denoising. We introduce the idea of group-based approaches by removing edges between different groups, a set of communities with similar average. It can improve the signal to noise ratio substantially and reduce the high frequency loss. We demonstrate GSSIM is promise through intuitive examples. Group-based analysis improves the denoising effect of all the methods studied.

关键词： graph structure similarity community detection graph signal processing denoising image modeling

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image processing Approaches for Autonomous Navigation of Terrestrial Vehicles in Low Illumination 2

Image Processing Approaches for Autonomous Navigation of Ter...

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2nd international conference On Emerging Computation and Information Technologies, ICECIT 2017

作者： Archana, S. Thejas, G.S. Ramani, Sanjeev Kaushik Iyengar, S.S. Siddaganga Institute of Technology India Florida International University United States

ISBN: (纸本)9781538610947

computer vision can be used as an integral part of any autonomous systems. Visual input and processing enables faster and early decisions. An important challenge in computer vision is detection and recognition of objects. This challenge is more pronounced in low illumination. In this paper, we are proposing a detection and recognition model for road warning signs with voice notification system for both autonomous and usual vehicles considering varied level of illumination. Realtime video from the vehicles was analysed using opencv. The noise from the video was removed using filters. Detection was based on Haar-cascades and training was done with sample positive and negative images. Text recognition was based on pattern matching. Voice notification was done using string to voice converters. The night vision was lightened considering the glare of vehicles headlight. © 2017 IEEE.

关键词： computer vision

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No-reference image quality assessment through transfer learning 2

No-reference image quality assessment through transfer learn...

引用

2nd IEEE international conference on Signal and image processing, ICSIP 2017

作者： Feng, Yeli Yiyu, Cai Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781538609682

Transfer learning has emerged from recent years' great success of applying convolutional neural networks to object recognition tasks. In this work, we describe a transfer learning framework that learns an image quality estimator end-to-end in classification or regression. Experiments show this approach achieves state of the art performance on four publicly available image quality databases. image quality is ignored in visual recognition challenges such as PASCAL VOC and ILSVRC. However, it poses a practical challenge to the design of robust vision applications. This work responds to such challenge without impacting the performance of recognition tasks. © 2017 IEEE.

关键词： image quality

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Real-time Detection and Tracking for Moving Objects Based on computer vision Method 2

Real-time Detection and Tracking for Moving Objects Based on...

引用

2nd international conference on Control and Robotics Engineering (ICCRE)

作者： Hou, Yi-You Chiou, Sz-Yu Lin, Ming-Hung Southern Taiwan Univ Sci & Technol Dept Elect Engn Tainan Taiwan Tung Fang Design Inst Grad Inst Cultural & Creat Design Kaohsiung Taiwan

ISBN: (纸本)9781509037742

In this paper, we will propose a method is to establishing 3D images and tracking objects in a plane space. The system in this paper will use automated. At first, grabbing dynamic environment images that from two CCDs, digitalized them by the image-grabber and then using digital image processing techniques to become 3D environment images. Next step is to do objects recognition and locate them respectively. This action can achieve the function of object tracking. At last, the result of both simulations and practical experiments demonstrate the method that proposed in this paper is feasible.

关键词： CCD camera dynamic image processing techniques object tracking

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An Approach to Get Overall Emotion from Comment Text towards a Certain image Uploaded to Social Network Using Latent Semantic Analysis 2

An Approach to Get Overall Emotion from Comment Text towards...

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2nd international conference on image, vision and Computing (ICIVC)

作者： Wickramaarachchi, Wiraj Udara Kariapper, R. K. A. R. Wuhan Univ Technol Sch Comp Sci & Engn Wuhan Hubei Peoples R China Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka

ISBN: (纸本)9781509062386

One significant method of expressing the opinion of the users of social network is expressing true feelings or emotions through chats and comments for images, status or videos that has been uploaded to social network. This will increases the effectiveness of the communication among users since they have no face-to-face interaction. Text processing techniques are used to identify emotion which expressed through text. The research proposes an approach to get overall emotion from comment text towards an image, which uploaded to social network. The new methodology was developed as an enhanced extension of the previous works and using appropriate improvement and extension to them with Latent Semantic Analysis (LSA) because previous researches have proven that LSA is a light weight approach. And it is believed that, online emotion predicting systems must be light weight. The research achieved more efficiency than previous works due to light weight of the methodology, also introduced a prototype of GUI. Also the topic is open for future enhancement.

关键词： latent semantic analysis natural language processing emotion identification text processing social network

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Autofocus Window Selection Algorithm Based on Visual Saliency 2

Autofocus Window Selection Algorithm Based on Visual Salienc...

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2nd international conference on image, vision and Computing (ICIVC)

作者： Wei, Zhaoqi Su, Xiuqin Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Shaanxi Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781509062386

Aiming at the problem of Autofocus window selection in imaging system, a new algorithm based on visual saliency for Autofocus window selection is proposed, which provides a new solution. A re-designed Itti model is used to predict the salient region in the visual scene. By choosing the local maxima in the saliency map to be the seed, the most salient region can be obtained by growing around it and a minimum enclosing rectangle can be found as the focus window. In this paper, the focus window selection based on visual saliency can efficiently capture the visual salient region and the position of general target well, highlight potential focus targets and improves the accuracy of focusing. Compared with the common focusing window selection algorithm, the method proposed in this paper can improve the focusing performance of the imaging system and has wider applicability in the general scene.

关键词： auto-focus window selection algorithm visual saliency computer vision self-adaptability

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Face Recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

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2nd international conference on image, vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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Content-Based Mammogram Retrieval Using k-means Clustering and Local Binary Pattern 2

Content-Based Mammogram Retrieval Using k-means Clustering a...

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2nd international conference on image, vision and Computing (ICIVC)

作者： Kulshreshtha, Devang Singh, Vibhav Prakash Shrivastava, Ayush Chaudhary, Arpit Srivastava, Rajeev Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

ISBN: (纸本)9781509062386

Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient computer-based mammogram retrieval system is proposed, which helps in early diagnosis of breast cancer by comparing the current case with past cases. The proposed steps include cropping of mammograms, feature extraction using local binary pattern (LBP) and k-mean clustering. Using LBP, k-mean generates the clusters based on the visual similarity of mammograms. Further, query image features are matched with all cluster representatives to find the closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Experiments on benchmark mammography image analysis society (MIAS) database confirm the effectiveness of this work.

关键词： mammography content-based image retrieval k-means clustering local binary pattern CAD

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Microscopic image Thresholding using Restricted Equivalence Function based Fuzzy Entropy Minimization and Bat Algorithm

Microscopic Image Thresholding using Restricted Equivalence ...

引用

international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Hiranmoy Roy Soumyadip Dhar Poushali Choudhury Abhirup Biswas Abhrak Chatterjee Department of Information Technology RCC Institute of Information Technology Kolkata India

Ubiquitous use of microscope in the field of medical diagnosis influences the development of automated systems. The inbuilt noise, illumination and contrast variations make microscopic image processing an emerging field of computer vision applications. This paper presents a novel and fast thresholding technique for microscopic data. To represent the inherent image vagueness, we use a fast processing fuzzy membership value generation technique using restricted equivalence function (REF). Then, a fuzzy entropy value is used to measure the total fuzziness present in the object and the background of the image. Finally, to search the optimal threshold value we use the well popular Bat algorithm. We have also implemented a multilevel thresholding technique for processing some complex fluorescence microscopy images. Experimental results on microscopic data and also on normal images show the superiority of the proposed thresholding technique. Experimental results on microscopic data and also on normal images show the superiority of the proposed thresholding technique. Proposed method is superior than other state-of-the-art methods not only in processing time but also in quantitative results. The proposed method is superior than other state-of-the-art methods not only in processing time, but also in quantitative results.

关键词： Thresholding (Imaging) Entropy Microscopy image segmentation Fuzzy sets Medical diagnosis

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