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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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Saliency detection with relative location measure in light field image 2

Saliency detection with relative location measure in light f...

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

作者： Li, Chao Zhan, Bin Shuo, Zhang Hao, Sheng School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781509062379

Saliency detection becomes a crucial requirement for numerous computer vison application. Conventional manifold ranking models have been widely used for saliency detection because it can measure similarity efficiently between the regions, but most of them make use of color and texture information and location information of objects didn't be well exploited, so it cannot work properly when objects is low-contrast. Based on manifold ranking model, this paper proposes a relative location measure on object in light field image. Then an adaptive foreground selection and background selection method using relative location is adopted to get preliminary saliency maps. Finally, an optimization model is presented to combine the saliency maps to get saliency detection results. Quantitative evaluations are carried out on public saliency detection dataset. The results demonstrate that the proposed method can outperform the other state-of-the-art method by using relative location measure. © 2017 IEEE.

关键词： image fusion

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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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A robust tracking method based on the correlation filter and correcting strategy 2

A robust tracking method based on the correlation filter and...

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

作者： Liu, Changhong Yao, Xuwen Zhu, Zhihong Shaohu, Peng Weiping, Zheng School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China School of Computer South China Normal University Guangzhou China

ISBN: (纸本)9781509062379

Visual tracking integrates the technology of image processing and pattern recognition, etc., which has a lot of potential applications, such as automatic driving, safety monitoring, etc. This paper analyzes the advantages and disadvantages of the Kernelized Correlation Filter (KCF) and Tracking-Learning-Detection (TLD), which are two kinds of trackers. TLD tracker has correcting capability whereas its performance highly depends on the tracker, which is not robust to some cases, such as tracking non-grid objects. Inversely, KCF achieves good performance in tracking non-grid objects. However, KCF behaves badly in the presence of occlusion and out-of-view and it cannot correct errors during the tracking process. According to the characteristics of the KCF and TLD, this paper proposes a robust tracking method based on the correlation filter and correcting strategy. By using the advantages of the KCF and TLD, the proposed method achieves high tracking accuracy and correcting capability. Experimental results show that the proposed method outperforms other methods (KCF, TLD, Struck, SCM, ASLA, MTT and DFT) according to the success and precision plots of OPE, SRE, and TRE, respectively. © 2017 IEEE.

关键词： image processing

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Design and implementation of high resolution face image acquisition system under low illumination based on the open source computer vision library 2

Design and implementation of high resolution face image acqu...

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

作者： Luo, Min Luo, Yadong Li, Hui Xia, Zhang Yongkui, Yang Department of Information Engineering Sichuan Water Conservancy Vocational College Chongzhou China Department of Information Technology Chengdu Agricultural College China Chengdu Research Institute of Huawei Technology Co. Ltd. China

ISBN: (纸本)9781509062379

Super-resolution face image acquisition system is indispensable in people's life. Under the condition of low illumination, the illumination environment difference is too big or the light is insufficient, which leads to the traditional image acquisition system can not collect the high quality face image, and the limitation is poor. Based on open source computer vision library (OpenCV) in the C++ environment configuration, the use of Three Dimension (3D) face recognition technology algorithm, design a set of low illumination conditions of the super resolution face image acquisition system. Experiments show that the design scheme with real-time focusing speed), fast (single acquisition 0.05 seconds), accurate (facial recognition rate of 99.3%) etc. characteristics, be able to fully meet the needs of low illumination conditions for super-resolution of face image acquisition. © 2017 IEEE.

关键词： Face recognition

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

Microscopic Image Thresholding using Restricted Equivalence ...

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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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image morphing using deformation and patch-based synthesis 2

Image morphing using deformation and patch-based synthesis

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

作者： Chen, Jiaxu Long, Zhang Liu, Baoen Li, Xiaoxu Ye, Zhongfu Department of Electronic Engineering and Information Science National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781509062379

image morphing creates a smooth transformation between images. Traditional morphing techniques may fail when the two input images have large displacements. In this paper, a two-step scheme is proposed to deal with this challenging task. Firstly, a series of deformed images is generated through optimizing an adaptation of deformation objective that composed of as-rigid-as-possible energy, group lasso penalty, and smoothness penalty. Then, the forward and backward deformed sequences are aligned utilizing the patch-based synthesis, satisfying source similarity and temporal coherence. Guided by a few user-specified correspondences, the proposed method works well in many morphing scenes. © 2017 IEEE.

关键词： Deformation

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No reference image quality assessments based on edge-blur measure and its applications in printed sheet blurs classification 2

No reference image quality assessments based on edge-blur me...

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

作者： Rixing, Huang College of Computer Science and Engineering University of Electronic Science and Technology of Chinaname of Organization Chengdu China

ISBN: (纸本)9781509062379

No reference image quality assessments based on edge are important research methods, but these algorithms do not consider blur direction. This does not meet the needs of our work, printed sheet image blur classification. This paper includes two main parts. First, analyzing rules of image blurs and design a novel no reference image quality analysis based on edge, edge-blur measure (EBM). It not only takes overall-blur of image assessment, but also takes image blur assessment in some directions. Second, use these methods to classify the printed sheet blurs (sharp, defocused and shake). Extract the image blur features from EBM. Then by regression analysis, all blur features are discretized into three values. Then the discrete blur features will be input into naive Bayes classifier for blur classification. Finally, our experimental results demonstrate the effect of EBM and classification algorithm. © 2017 IEEE.

关键词： Regression analysis

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computer vision system for robotic complex based on Arduino and Raspberry Pi 2

Computer vision system for robotic complex based on Arduino ...

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2nd IEEE international conference on Control in Technical Systems (CTS)

作者： Petrova, K. A. St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9781538607770

This paper is devoted to traffic sign recognition problem in real time. The recognition process consists of three steps. The first step is a search of image parts which probably contain a traffic sign. The second step is about parts extraction and simple classification by shape. The last step consists of classification of extracted parts with previously learned multilayered neural net. The results of experimental setup based on real data show feasibility of the proposed methods.

关键词： traffic sign recognition image processing neural net classification

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