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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

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Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti 1

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25th International conference on Neural Information processing (ICONIP)

作者： Gordienko, Nikita Gang, Peng Gordienko, Yuri Zeng, Wei Alienin, Oleg Rokovyi, Oleksandr Stirenko, Sergii Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine Huizhou Univ Sch Informat Sci & Technol Huizhou Peoples R China

ISBN: (数字)9783030042219

ISBN: (纸本)9783030042219;9783030042202

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St. Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.

关键词： Machine learning Explanatory data analysis t-distributed stochastic neighbor embedding Stone carving dataset notMNIST Multinomial logistic regression Convolutional neural network Deep learning Data augmentation

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Feature Selection approach using KNN supervised learning for Content-Based image Retrieval

Feature Selection approach using KNN supervised learning for...

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First International conference of Intelligent Computing and Engineering (ICOICE)

作者： Fahd A. Alqasemi Hamza Q. Alabbasi Fathi G. Sabeha Ahmed Alawadhi Sanad Kahlid Ammar Zahary University of Science and Technology Yemen Sana'a Yemen Faculty of computer and IT Sana'a University And Faculty of Engineering and IT Azal University for Human Development Sana'a Yemen

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144887

Digital images have a serious influence over all the world, since, images have a very tangible importance, which impacts several people needs, this gave image retrieval researches this importance. So, Content-based image retrieval (CBIR) became one of the challenges in information retrieval and image processing fields, its services needs to apply for both business and science domains. In this paper we have proposed a CBIR approach that based on statistical methods feature selection via k-nearest neighbor (KNN) technique. Hence, supervised learning is employed for CBIR to train WANG database images, then the query image was the tested sample, and among predicted class we have filtered the final results. Number of randomly selected query images have been tested via this approach, with a variation of CBIR criteria, which have varied among image types, category query, and threshold distances. The average results have presented in three result groups, some RGB images results have showed good evaluation, in term of precisions measure, also grayscale and RGB images have varied values; on the evaluation of f-measures results, which have changed according some different CBIR factors. Proposed feature selection have made CBIR simpler and effective, this have been showed in high precision evaluation in final results.

关键词： Feature extraction image retrieval image color analysis Gray-scale Task analysis Euclidean distance

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image encryption using chaotic 3-D Arnold’s cat map and logistic map 2nd

Image encryption using chaotic 3-D Arnold’s cat map and log...

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2nd International conference on Computer Vision and image processing, CVIP 2017

作者： Musanna, Farhan Rani, Asha Kumar, Sanjeev Department of Mathematics IIT Roorkee Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811078972

image encryption is different from that of traditional texts or binary data because of some inherent properties of images such as large data capacity, i.e., enormous size and high redundancy (statistical and psycho-visual), making them difficult to handle by traditional methods. In recent years, chaos theory has been explored to find efficient ways to develop secure image cryptosystems. Due to the desirable properties of mixing and sensitivity to initial conditions and parameters (butterfly effect), chaotic systems have found great deal in the domain of image encryption. In this paper, the 2-D chaotic cat map is generalized to its 3-D map counterpart for designing a real-time secure and reliable symmetric encryption design. This new scheme deploys the 3-D Arnold’s cat map to shuffle the positions of image pixels and uses the other chaotic logistic map to perplex the relationship between the plain-image and the cipher-image, thereby significantly enhancing the robustness to differential and statistical attacks. Experimental tests are carried out with comprehensive analysis, demonstrating the high security of the scheme. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Chaotic systems

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statistical Bayesian method for reliability evaluation based on ADT data 2

Statistical Bayesian method for reliability evaluation based...

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2nd International conference on Reliability Engineering (ICRE)

作者： Lu, Dawei Wang, Lizhi Sun, Yusheng Wang, Xiaohong Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Beihang Univ Unmanned Syst Inst Beijing Peoples R China

Accelerated degradation testing (ADT) is frequently conducted in the laboratory to predict the products' reliability under normal operating conditions. Two kinds of methods, degradation path models and stochastic process models, are utilized to analyze degradation data and the latter one is the most popular method. However, some limitations like imprecise solution process and estimation result of degradation ratio still exist, which may affect the accuracy of the acceleration model and the extrapolation value. Moreover, the conducted solution of this problem, Bayesian method, lose key information when unifying the degradation data. In this paper, a new data processing and parameter inference method based on Bayesian method is proposed to handle degradation data and solve the problems above. First, Wiener process and acceleration model is chosen;Second, the initial values of degradation model and parameters of prior and posterior distribution under each level is calculated with updating and iteration of estimation values;Third, the lifetime and reliability values are estimated on the basis of the estimation parameters;Finally, a case study is provided to demonstrate the validity of the proposed method. The results illustrate that the proposed method is quite effective and accuracy in estimating the lifetime and reliability of a product.

关键词： Reliability

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image Enhancement Method Based on Adaptive Fraction Gamma Transformation and Color Restoration for Wireless Capsule Endoscopy

Image Enhancement Method Based on Adaptive Fraction Gamma Tr...

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IEEE Biomedical Circuits and Systems conference (BioCAS) - Advanced Systems for Enhancing Human Health

作者： Long, Mingzhu Lan, Zehua Xie, Xiang Li, Guolin Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Univ Elect Sci & Technol China Glasgow Coll Chengdu Sichuan Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781538636039

Wireless capsule endoscopy (WCE) plays an important role in gastrointestinal (GI) tract observation and disease diagnosis. However, due to the limited illumination and complex environment in GI tract, endoscopic images are usually not clear enough to be interpreted by doctors or used for further processing. Although many algorithms have been proposed to improve image quality from different aspects, the balance of algorithm complexity and image quality is still a challenge worth exploring. In this paper, we propose a novel function named adaptive fraction gamma transformation (AFGT) to enhance the quality of images captured by the WCE. Characteristics of AFGT are adaptive to different luminance in an image and self-tunable to the statistical characteristics of different images. Besides, HSI color space is adopted for color restoration, namely, the saturation component is linearly scaled according to the enhanced intensity component. Experimental results show that performances of the proposed method in both visual quality and objective assessments are better than that of the state-of-art methods.

关键词： wireless capsule endoscopy image enhancement fraction gamma transformation color restoration

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The analysis of image characteristics on the base of energy features of the wavelet transform 4

The analysis of image characteristics on the base of energy ...

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4th International conference on Information Technology and Nanotechnology - Session: image processing and Earth Remote Sensing, IPERS-ITNT 2018

作者： Lyasheva, S.A. Medvedev, M.V. Shleymovich, M.P. Mokshin, V.V. Kazan National Research Technical University-KAI K. Marks 10 Kazan420111 Russia

The article shows the relevance of creating models and methods that provide effective solutions to image processing and analyzing problems in computer vision systems. We consider models of an average level of image representation. They are constructed on the basis of their characteristic features (contours, regions and points of interest). To construct such models, we suggest using the procedure of forming energy features based on the wavelet transform. As a result, the original image will be transformed to a view where different points will have different weights. That characterize their contribution to the overall energy of the image. It is also possible to provide a selection of tuning coefficients. It allows to take into account the interrelations between the wavelet coefficients of various scales. After receiving the weight images, they can be processed to form the required characteristics. For example, to obtain contours, you can perform binarization of a weight image with a certain threshold. To get singular points, you can define a specified number of the most significant weights in different areas of the weight image. For texture analysis, you can use statistical characteristics calculated by the histogram of the scale. © 2018 CEUR-WS. All rights reserved.

关键词： Wavelet transforms

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Incremental Structural Modeling Based on Geometric and statistical Analyses 18

Incremental Structural Modeling Based on Geometric and Stati...

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18th IEEE Winter conference on Applications of Computer Vision (WACV)

作者： Roberto, Rafael Lima, Joao Paulo Uchiyama, Hideaki Arth, Clemens Teichrieb, Veronica Taniguchi, Rin-Ichiro Schmalstieg, Dieter Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Informat & Estat Recife PE Brazil Kyushu Univ Lab Image & Media Understanding Fukuoka Fukuoka Japan Graz Univ Technol Inst Comp Graph & Vis Graz Austria

ISBN: (纸本)9781538648865

Finding high-level semantic information from a point cloud is a challenging task, and it can be used in various applications. For instance, it is useful to compactly represent the scene structure and efficiently understand the scene context. This task is even more challenging when using a hand-held monocular visual SLAM system that outputs a noisy sparse point cloud. In order to tackle this issue, we propose an incremental primitive modeling method using both geometric and statistical analyses for such point cloud. The main idea is to select only reliably-modeled shapes by analyzing the geometric relationship between the point cloud and the estimated shapes. Besides that, a statistical evaluation is incorporated to filter wrongly-detected primitives in a noisy point cloud. As a result of this processing, our approach largely improved precision when compared with state of the art methods. We also show the impact of segmenting and representing a scene using primitives instead of a point cloud.

关键词： Shape Three-dimensional displays Semantics Reliability Visualization Simultaneous localization and mapping Noise measurement

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image Quality Assessment for Selfies with and without Super Resolution 10

Image Quality Assessment for Selfies with and without Super ...

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10th International conference on Machine Vision (ICMV)

作者： Kubota, Aya Gohshi, Seiichi Kogakuin Univ Dept Informat Tokyo Japan

ISBN: (数字)9781510619425

ISBN: (纸本)9781510619425

With the advent of cellphone cameras, in particular, on smartphones, many people now take photos of themselves alone and with others in the frame;such photos are popularly known as "selfies". Most smartphones are equipped with two cameras: the front-facing and rear cameras. The camera located on the back of the smartphone is referred to as the "out-camera," whereas the one located on the front of the smartphone is called the "in-camera." In-cameras are mainly used for selfies. Some smartphones feature high-resolution cameras. However, the original image quality cannot be obtained because smartphone cameras often have low-performance lenses. Super resolution (SR) is one of the recent technological advancements that has increased image resolution. We developed a new SR technology that can be processed on smartphones. Smartphones with new SR technology are currently available in the market have already registered sales. However, the effective use of new SR technology has not yet been verified. Comparing the image quality with and without SR on smartphone display is necessary to confirm the usefulness of this new technology. methods that are based on objective and subjective assessments are required to quantitatively measure image quality. It is known that the typical object assessment value, such as Peak Signal to Noise Ratio (PSNR), does not go together with how we feel when we assess image/video. When digital broadcast started, the standard was determined using subjective assessment. Although subjective assessment usually comes at high cost because of personnel expenses for observers, the results are highly reproducible when they are conducted under right conditions and statistical analysis. In this study, the subjective assessment results for selfie images are reported.

关键词： Super resolution nonlinear signal processing subjective assessment

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Two-Phase Transmission Map Estimation for Robust image Dehazing 25th

Two-Phase Transmission Map Estimation for Robust Image Dehaz...

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25th International conference on Neural Information processing (ICONIP)

作者： Shu, Qiaoling Wu, Chuansheng Liu, Ryan Wen Chui, Kwok Tai Xiong, Shengwu Wuhan Univ Technol Dept Math Wuhan Peoples R China Wuhan Univ Technol Sch Nav Wuhan Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Hubei Key Lab Transportat Internet Things Wuhan Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9783030042240;9783030042233

A robust two-phase transmission map estimation framework is proposed in this paper for single image dehazing. The proposed framework first estimates the coarse transmission map through the statistical assumption of dark channel prior (DCP). To refine the coarse transmission map, a novel image-gradient-guided high-order variational method is then proposed in the second phase. The resulting L1-regularized high-order nonsmooth optimization problem will be effectively solved using the primal-dual algorithm. Once the fine transmission map is accurately obtained, the final haze-free image could be restored based on the haze imaging model of Koschmieder. To further enhance dehazing performance, an improved tolerance mechanism is incorporated into the proposed method to suppress the undesirable artifacts usually produced by DCP in large sky regions. Numerous experiments on both synthetic and realistic images were performed to compare our proposed method with several state-of-the-art dehazing methods. Dehazing results have illustrated the superior performance of the proposed method.

关键词： image dehazing image restoration Dark channel prior Total generalized variation Primal-dual algorithm

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A Vision-Based Strategy to Segment and Localize Ancient Symbols Written in Stone

A Vision-Based Strategy to Segment and Localize Ancient Symb...

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3rd Iberian Robotics conference (Robot)

作者： Duque-Domingo, Jaime Javier Herrera, P. Cerrada, Carlos Cerrada, Jose A. UNED ETSI Informat Dept Ingn Software & Sistemas Informat C Juan del Rosal 16 Madrid 28040 Spain

ISBN: (纸本)9783319708362;9783319708355

This work proposes an automatic method to detect ancient symbols written in stone. The proposed method takes into account well-known techniques used in computer vision to identify the contour of the symbols in the image. The two-stage method consists of segmentation and localization processes. Segmentation process includes a pre-processing step, edge detection and thresholding. Localization process is based on two conditions that take into account several parameters, like the distance between points, and the orientation and the continuity of the edges. This proposal has been applied to localize Egyptian cartouches (borders enclosing the name of a king) and stonemason's marks from images obtained under varying lighting conditions (controlled and natural lighting). The proposed method is compared favorably against other methods based on chain coding, neural networks and statistical correlation. The promising results give new possibilities to identify and recognize complex symbols and ancient texts.

关键词： image segmentation Edge detection Object localization

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