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

共 1804 条记录，以下是1041-1050 订阅

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A NOVEL EXPERIMENTAL TECHNIQUE FOR RECOVERING CAMERA RESPONSE FUNCTION FOR HIGH DYNAMIC RANGE IMAGING 18

A NOVEL EXPERIMENTAL TECHNIQUE FOR RECOVERING CAMERA RESPONS...

引用

Proceedings of the 2nd international conference on Advances in image processing

作者： Salvador Garcia Bernal Yuanhao Gong Bozhi Liu Guoping Qiu Novel Optics Computer Vision Lab ETH Zurich College of Information Engineering Shenzhen University

ISBN: (纸本)9781450364607

Recovering the camera's response function has wide applica-tion in image processing and computer *** computational approach using multiple differently exposed photographs is based on solving a constrained linear least square problem and can only recover the function up to a scaling *** this paper,we present a novel experiment based technique to recover the true response curves of spe-cific *** method first measures the average pixel intensities of a neutrally-grey cardboard for the full range of the shutter speed and gain control values of the ***-ter a simple data smoothing procedure,the camera's response function is recovered through simple table *** have implemented our technique on different kinds of sensors and show that it works *** new technique recover the actual range of the sensor and is more accurate than the computa-tional *** procedure is easy to set up and simple to implement which will be useful for developing applications such as High Dynamic Ranging(HDR) imaging.

关键词： Exposure Camera Response Curve HDR

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Understanding how adversarial noise affects single image classification 2nd

Understanding how adversarial noise affects single image cla...

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2nd international conference on Intelligent Information Technologies, ICIIT 2017

作者： Adate, Amit Saxena, Rishabh Don, S. VIT University VelloreTamil Nadu632014 India

ISBN: (纸本)9789811076343

In recent trends, computer vision applications have seen massive implementation of supervised learning with convolutional neural networks. In this paper, we have analyzed image classifiers and their classification accuracy. Also, we have measured their robustness upon introduction to various noise layers. Furthermore, we have implemented a generative adversarial network for the generator task of adversarial noise generation and the discriminator task of single image classification on the handwritten digits database. Our experiments are yielding progressive results and we have performed conditional and quantifiable evaluation of the generated samples. © Springer Nature Singapore Pte Ltd. 2018.

关键词： image classification

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Skeleton Formation from Human Silhouette images Using Joint Points Estimation 2

Skeleton Formation from Human Silhouette Images Using Joint ...

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2nd international conference on Advances in Computing, Control and Communication Technology, IAC3T 2018

作者： Quoc, Pham Bao Binh, Nguyen Thanh Tin, Dang Thanh Khare, Ashish Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology VNU-HCM Ho Chi Minh City Viet Nam Department of Electronics and Communication University of Allahabad Allahabad India

ISBN: (纸本)9781538641460

Tracking and identifying human actions are most of the problems that attract many researchers in the field of computer vision. In this problem, it is important to extract features in such a way that the information is minimized for processing and storing, but they still retain basic characteristics of the object. Estimating skeleton model with joints is one of the effective methods to represent the general shape of objects. This method reduces the amount of information that needs to be stored, but it preserves the kinetic characteristics of human parts. This paper proposes improvement in the estimating joint point's method that help to construct the human skeleton model from silhouette images more accurately and effectively. The proposed method uses the gradient of distance transform image to extract the coarse skeleton from the human silhouette image. Then, the distance-based searching method is applied at specific locations to estimate the joint points. Finally, the joint points are connected to give the human skeleton model which has 16 points. The proposed method reduces the number of times in traversing the whole image, and the calculating distances are optimized to speed up the system. The experimental results show that the proposed method gives good results in many cases. © 2018 IEEE.

关键词： image enhancement

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Encoder Grating Engraved-line Detection System Design Based on Machine vision 2

Encoder Grating Engraved-line Detection System Design Based ...

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2018 2nd international Symposium on Resource Exploration and Environmental Science, REES 2018

作者： Wang, Zhihong Zhang, Dandan College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China

The detection of encoder grating engraved-line uniformity, is important in the encoder production debugging. Aiming at the shortcomings of the traditional method of signal processing using the encoder output, the real-time performance of the encoder is not strong, the operation is complex, the precision is not high, the debugging is inconvenient and so on, a black and white ratio detection method based on image processing is proposed. Using the CCD industrial camera to image acquisition and the edge image of the grating engraved-line is obtained by the image processing, and the edge of the grating line is obtained by the least squares straight line fitting. The actual black and white ratio is obtained by the ratio of the adjacent straight lines distance from the point to the two sides, Compared with the design value obtained by the black and white margin error to evaluate its uniformity. The experimental results show that the method can effectively to test the grating engraved-line uniformity. © Published under licence by IOP Publishing Ltd.

关键词： computer vision

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Detection of fruit ripeness using image processing 2nd

Detection of fruit ripeness using image processing

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2nd international conference on Advances in Computing and Data Sciences, ICACDS 2018

作者： Mande, Anuprita Gurav, Gayatri Ajgaonkar, Kanchan Ombase, Pooja Bagul, Vaishali Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789811318122

Humans depend on their vision quality to check whether the fruit is ripe or unripe. They grade the maturity level of a fruit based on their vision based features that lead to inaccuracy, inconsistency and inefficiency in the results. There are numerous methods to detect the ripeness of fruits. The proposed work consists of two main steps;one is color image segmentation and other is comparison. For comparison purpose, we have used correlation and histogram matching techniques. The main technique used for image segmentation is region of interest based color segmentation, in which the red color region of image is separated. All the techniques use color images of fruits as input data. In the proposed work, all the images are first converted into LAB color space and then segmented both, the reference image and the input data image. With the help of correlation and histogram comparison of these images, the maturity level of a given fruit is determined. © Springer Nature Singapore Pte Ltd 2018.

关键词： image segmentation

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CNN based technique for systematic classification of field photographs 2

CNN based technique for systematic classification of field p...

引用

2nd international conference on Design Innovations for 3Cs Compute Communicate Control, ICDI3C 2018

作者： Kavitha, D. Hebbar, Ramachandra Vinod, P.V. Harsheetha, M.P. Jyothi, L. Madhu, S.H. Department of ECE MVJ College of Engineering Bangalore Karnataka India ISRO RRSC-S Bangalore India

ISBN: (纸本)9781538675236

computer vision is the science that aims to contribute a similar, if not better, capability to a machine or computer. It is also interested with the idea and technology for building artificial systems that secure information from images. The digital image processing, image analysis and computer vision have become a principle part of artificial intelligence and the interface between the human and the machine grounded theory and enforced technologies. Implementing machine learning such as deep neural networks on agricultural data as immense attention in recent times. Convolution Neural Networks (CNN) are most commonly used in pattern and image detection problems as they have a number of advantages compared to other techniques. This paper proposes CNN architecture to segregate different plant images from the sequences collected. Following the preprocessing steps that will be including the elimination of blurriness or introducing the illumination change;CNN architecture is employed to extract the traits of the images that are present in the dataset that is created. image classification using CNN involves dataset creation, training CNN, validating, testing CNN, prediction and at last classification. Here we use Keras software with backend as Theano and Tensor flow and we are able to predict the overall classification report with the accuracy of 43.98% for one cast of field photographs. © 2018 IEEE.

关键词： image classification

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image Classification Algorithm Based on Depth Neural Network and Multi-Level Feature Learning

Image Classification Algorithm Based on Depth Neural Network...

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2019 2nd international conference on computer Science and Advanced Materials(CSAM 2019)

作者： Qian Sun Li Xin Faliang Chang Zengshun Zhao College of Electronics Communications and Phisics Shandong University of Science and Technology School of Control Science and Engineering Shandong University Department of Electrical& Computer Engineering University of Florida

image classification being widely applied to computer vision is an im-age processing method to distinguish the different category targets according to the different features reflected by the image information. BOW-SVM is a relative-ly typical image classification method with higher precision, however, it's unsatis-factory in operation performance. To improve the performance and precision more efficiently, a high-efficiency image classification method based on HOG-PCA is proposed. First of all, it is to make the feature whitening by extracting the Histogram of Oriented Gradients(HOG) features, secondly, make the random down-sampling for the scale unification, afterwards, adopt the principal component analysis(PCA) for feature mapping and finally make the nearest neighbor classification through the minimum two-order norm determination. In the experiment, the proposed method is realized and tested on the P ASCAL 2012 data set through C++ on the basis of OPENCV and Darwin to compare the precision and operation performance of this method and BOW-SVM method;according to the experiment, the proposed has higher precision and better operation performance.

关键词： Deep Neural Network Multi-Layer Feature Learningl Minimum Two-Order Norm image Classification image Features

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Deep Neural Network for image Recognition Based on the Caffe Framework 2

Deep Neural Network for Image Recognition Based on the Caffe...

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2nd IEEE international conference on Data Stream Mining and processing (DSMP)

作者： Komar, Myroslav Yakobchuk, Pavlo Golovko, Vladimir Dorosh, Vitaliy Sachenko, Anatoliy Ternopil Natl Econ Univ Res Inst Intelligent Comp Syst Ternopol Ukraine Brest State Tech Univ Dept Intelligent Informat Technol Brest BELARUS Kazimierz Pulaski Univ Technol & Humanities Radom Radom Poland

ISBN: (纸本)9781538628744

Deep Leaning of the Neural Networks has become one of the most demanded areas of Information Technology and it has been successfully applied to solving many issues of Artificial Intelligence, for example, speech recognition, computer vision, natural language processing, data visualization. This paper describes the developing the deep neural network model for image recognition and a corresponding experimental research on an example of the MNIST data set. Some practical details for creating the Deep Neural Network and image recognition in the Caffe Framework are given as well.

关键词： Deep Neural Network Information Technology image Recognition Artificial Intelligence Caffe Framework

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Facial emotion recognition: A comparative analysis using 22 LBP variants 18

Facial emotion recognition: A comparative analysis using 22 ...

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2nd Mediterranean conference on Pattern Recognition and Artificial Intelligence (MedPRAI)

作者： Slimani, K. Kas, M. El Merabet, Y. Messoussi, R. Ruichek, Y. Ibn Tofail Univ LASTID Lab Dept Phys Fac Sci Kenitra Morocco Univ Bourgogne Franche Comte Arts & Metiers CNRS Le2i FRE2005 Belfort France

ISBN: (纸本)9781450352901

Facial expression is a significant form of non-verbal communication for human being. It includes much important information about the feeling, the mental and the emotional state of a person which can be useful in several real-world applications and fields like image processing and computer vision. Face can be seen as a composition of micro-patterns of textures. Over the last decades, LBP operator, which shown its robustness in extracting useful features characteristics from an image, has been successfully applied in diverse range of problems including facial expression recognition. Nowadays, many LBP variants have been proposed in the literature. This paper reviews 22 LBP-like descriptors and provides a comparative analysis on facial expression recognition problem using two benchmark databases, the Japanese female facial expression (JAFFE) and Cohn-Kanade (CK) databases. The experiments show that several of the evaluated methods achieve performances that are better than those recorded by the state-of-the-art systems. Recognition rates of 97.14% and 100% have been reached on JAFFE and Cohn-Kanade databases respectively.

关键词： Local binary patterns (LBP) KNN feature extraction basic emotions

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Agricultural IoT System Based on image processing and Cloud Platform Technology 2nd

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2nd international conference on Machine Learning and Intelligent Communications, MLICOM 2017

作者： Zheng, Yaxin Liu, Chungang Communication Research Center Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783319734460

Detection of crop disease and growth state have always been the key to ensure the yield and quality of agricultural products. The algorithms, which are in the field of pattern recognition or image recognition, have been using to crop-disease detection and growth-state detection, these algorithms undoubtedly have great significance, and with the development of IoT technology in recent years, the Internet of things technology combining with the existing technology will be the future direction of intelligent agriculture. This paper proposed an agricultural system, which based on the image processing technology and cloud platform of the Internet of things technology. The system can complete image recognition process real-time detection and recording of crop growth status and alarm crop disease in time based on the mutual connection with the cloud platform, and truly realize the unmanned detection in the field of intelligent agricultural system. © 2018, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image recognition

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