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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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image fusion using two symmetric exposed images in the JPEG stream

Image fusion using two symmetric exposed images in the JPEG ...

引用

2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

作者： Lee, Geun-Young Lee, Sung-Hak Kwon, Hyuk-Ju Sohng, Kyu-Ik School of Electronics Engineering Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)1601324421

The objective of fusing differently exposed images is to well represent the luminance information of the scene in a single image. In order to fuse multi exposure images, it is necessary to determine overexposed or underexposed areas in each of the captured images using an activity measure which extracts image features. In this paper, we presents image fusion using two symmetric exposed images in the JPEG stream. We find out that the AC coefficients after the quantization in JPEG baseline can describe local image features in the image block. Based on a maximum selection rule, a proposed image fusion is conducted by the comparison of the AC coefficients after the quantization instead of activity measures which need additional computations. This simple method allows multi exposure images to quickly combine a single image in JPEG baseline, therefore, it can be embedded in resource limited imaging platform such as a surveillance system. © CSREA Press.

关键词： image fusion

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DEEP BLIND image QUALITY ASSESSMENT BY EMPLOYING FR-IQA 24

DEEP BLIND IMAGE QUALITY ASSESSMENT BY EMPLOYING FR-IQA

引用

24th IEEE international conference on image processing (ICIP)

作者： Kim, Jongyoo Lee, Sanghoon Yonsei Univ Seoul South Korea

ISBN: (纸本)9781509021758

In this paper, we propose a convolutional neural network (CNN)-based no-reference image quality assessment (NR-IQA). Though deep learning has yielded superior performance in a number of computer vision studies, applying the deep CNN to the NR-IQA framework is not straightforward, since we face a few critical problems: 1) lack of training data;2) absence of local ground truth targets. To alleviate these problems, we employ the full-reference image quality assessment (FR-IQA) metrics as intermediate training targets of the CNN. In addition, we incorporate the pooling stage in the training stage, so that the whole parameters of the model can be optimized in an end-to-end framework. The proposed model, named as a blind image evaluator based on a convolutional neural network (BIECON), achieves state-of-the-art prediction accuracy that is comparable with that of FR-IQA methods.

关键词： Convolutional neural network deep learning image quality assessment no-reference image quality assessment

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Low-level computer vision techniques for processing of extensive air shower track images 14

Low-level computer vision techniques for processing of exten...

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14th IEEE international Scientific conference on Informatics

作者： Vrabel, Michal Genci, Jan Bobik, Pavol Tech Univ Kosice Fac Elect Engn & Informat Technol Letna 9 Kosice 04001 Slovakia Slovak Acad Sci Inst Expt Phys Watsonova 47 Kosice 04001 Slovakia

ISBN: (纸本)9781538608890

An extensive air shower can be observed as a bright spot moving through the field of view of an orbital fluorescence detector. A challenging part of the air shower recognition is segmentation of its track. The issues arise from a low signal to noise ratio. This paper provides a short review of selected low-level computer vision techniques such as filtering and thresholding methods, which are for a demonstration applied to a composite simulated air shower image. The article should provide a shortlist of algorithms that can be applied as a part of more complex event classification or reconstruction procedure.

关键词： Detectors computer vision image edge detection Thresholding (Imaging) image segmentation Feature extraction

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Gabor filter meanPCA feature extraction for gender recognition 1

引用

2nd international conference on computer vision and image processing, cvip 2017

作者： Gupta, Sandeep K. Nain, Neeta Department of Computer Science & Engineering Malaviya National Institute of Technology JaipurRajasthan302017 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

This paper proposed Novel feature extraction techniques as Gabor-meanPCA for automatic gender recognition using faces of person. Feature extraction is the main stage on which accuracy of gender recognition system depended. Male and female have different edge and texture pattern on faces. Gabor filter is able to extract edges and texture pattern of faces but has a problem of huge dimension and high redundancy. In this paper, Gabor filter is used for extraction of edge pattern of faces using different angles. Problem of huge dimension and high redundancy is reduced by proposed two-level feature reduction technique. The proposed technique also provides better accuracy as well as compact feature vector for reducing classification time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Feature extraction

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A fast texture feature extraction method based on Gabor wavelets

A fast texture feature extraction method based on Gabor wave...

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2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

作者： Wang, X.L. Wang, X. Hu, L. School of Information Engineering Changan University Xi’an Shaanxi China School of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)1601324421

With the development of computer vision, robots need to detect target objects from image sequence for autonomous navigation. To identify targets, the perceptual system of autonomous robots first needs to segment the images into nonoverlapping but meaningful regions based on low-level features such as color, texture measures and shapes etc.. Being an important component, Gabor wavelets are often used to extract the texture features due to its being a mathematical approximation to the spatial receptive field of a simple cell in the V1 area of human brain. The problem with these Gabor texture measures is the high computational cost involved in the convolution in the feature extraction process. To partially solve this problem, in this paper, we carefully study the behaviors of the Gabor wavelets used to form the texture features and find out that only a small subset of the filters have important contributions to the identification process. Experimental results show that, by removing redundant filters, better performance can be achieved in a much shorter time. © CSREA Press.

关键词： image segmentation

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Exploring the Role of Adversarial Attacks in image Anti-forensics 6th

Exploring the Role of Adversarial Attacks in Image Anti-fore...

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6th international conference on computer vision and image processing, cvip 2021

作者： Sharma, Krishan Gopal Singh, Gurinder Goyal, Puneet Huazhong University of Science and Technology Wuhan China Indian Institute of Technology Ropar Rupnagar India

ISBN: (纸本)9783031113482

Deep learning (DL) has grown significantly in the field of image forensics. A lot of research has been going on to develop deep learning based image manipulation detection techniques. On the contrary, researchers are also challenging the robustness of these DL-based image forensic techniques by developing efficient anti-forensic schemes. This paper reveals the role of adversarial attacks in image anti-forensics with better human imperceptibility against recent general-purpose image forensic techniques. We propose an image anti-forensic framework by using recent adversarial attacks, i.e., Fast Gradient Sign Method (FGSM), Carlini and Wagner (C &W), and Projected Gradient Descent (PGD). Firstly, we have trained recent image forensic models on the BOSSBase dataset. Then, we generate adversarial noise by using the gradient of these image forensic models corresponding to each adversarial attack. Afterward, the obtained noise is added to the input image, resulting in the adversarial image corresponding to the particular attack. These adversarial images are generated by using the BOSSBase dataset and tested on the recent image forensic models. The experimental results show that the performance of the recent forensic models has decreased rapidly in the range from 50–75% against the different adversarial attacks, i.e., FGSM, C &W, and PGD. Furthermore, the high human imperceptibility of generated adversarial images is confirmed from the PSNR values. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo 29

A Benchmark Dataset and Evaluation for Non-Lambertian and Un...

引用

2016 IEEE conference on computer vision and Pattern Recognition (CVPR)

作者： Shi, Boxin Wu, Zhe Mo, Zhipeng Duan, Dinglong Yeung, Sai-Kit Tan, Ping AIST Artificial Intelligence Res Ctr Tokyo Japan Singapore Univ Technol & Design Singapore Singapore Natl Univ Singapore Singapore Singapore Simon Fraser Univ Burnaby BC Canada Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781467388511

Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions. However, due to the lack of suitable benchmark data with ground truth shapes (normals), quantitative comparison and evaluation is difficult to achieve. In this paper, we first survey and categorize existing methods using a photometric stereo taxonomy emphasizing on non-Lambertian and uncalibrated methods. We then introduce the 'DiLiGenT' photometric stereo image dataset with calibrated Directional Lightings, objects of General reflectance, and 'ground Truth' shapes (normals). Based on our dataset, we quantitatively evaluate state-of-the-art photometric stereo methods for general non-Lambertian materials and unknown lightings to analyze their strengths and limitations.

关键词： Stereo image processing

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Robust ellipse detection via duality principle with a false determination control 1

引用

2nd international conference on computer vision and image processing, cvip 2017

作者： Dong, Huixu Chen, I-Ming Prasad, Dilip K. Robotics Research Centre Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

In this paper, we propose a novel ellipse detection approach that eliminates false detection-based parameter space decomposition, principal of symmetric tangents, and a novel geometric constraint utilizing properties of tangents of ellipses. The principle of symmetric tangents provides better computational efficiency through confirmation of the ellipse center in the decomposed parameter space. The geometric constraint is used for alleviating the false detection probability. The experimental results confirm that the approach detects ellipse with an excellent accuracy at a high speed. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Computational efficiency

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Omega-3 Chicken Egg Detection System using a Mobile-based image processing Segmentation Method 8

Omega-3 Chicken Egg Detection System using a Mobile-based Im...

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8th international conference on Graphic and image processing (ICGIP)

作者： Nurhayati, Oky Dwi Teguh, Kurniawan M. Amalia, Cintya P. Diponegoro Univ Engn Fac Multimedia Lab Comp Engn Semarang Indonesia

ISBN: (纸本)9781510609518;9781510609525

An Omega-3 chicken egg is a chicken egg produced through food engineering technology. It is produced by hen fed with high omega-3 fatty acids. So, it has fifteen times nutrient content of omega-3 higher than Leghorn's. Visually, its shell has the same shape and colour as Leghorn's. Each egg can be distinguished by breaking the egg's shell and testing the egg yolk's nutrient content in a laboratory. But, those methods were proven not effective and efficient. Observing this problem, the purpose of this research is to make an application to detect the type of omega-3 chicken egg by using a mobile-based computer vision. This application was built in OpenCV computer vision library to support Android Operating System. This experiment required some chicken egg images taken using an egg candling box. We used 60 omega-3 chicken and Leghorn eggs as samples. Then, using an Android smartphone, image acquisition of the egg was obtained. After that, we applied several steps using image processing methods such as Grab Cut, convert RGB image to eight bit grayscale, median filter, P-Tile segmentation, and morphology technique in this research. The next steps were feature extraction which was used to extract feature values via mean, variance, skewness, and kurtosis from each image. Finally, using digital image measurement, some chicken egg images were classified. The result showed that omega-3 chicken egg and Leghorn egg had different values. This system is able to provide accurate reading around of 91%.

关键词： chicken egg P-Tile computer vision morphology feature extraction mobile

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image processing Based Leaf Rot Disease, Detection of Betel Vine (Piper BetleL.)

Image Processing Based Leaf Rot Disease, Detection of Betel ...

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international conference on Computational Modelling and Security (CMS)

作者： Dey, Amar Kumar Sharma, Manisha Meshram, M. R. Bhilai Inst Technol Dept Elect & Telecommun Durg 491001 CG India GEC Dept Elect & Telecommun Jagdalpur 494001 CG India

This paper deals with leaf rot disease detection for betel vine (Piper betel L.) based on image processing algorithm. The measurement of plant features is a fundamental element of plant science research and related applications. The information related to plant features is especially useful for its applications in plant growth modeling, agricultural research and on farm production. Few methods have been applied in leaf rot disease detection for betel vine leaf (Piper Betel L.). Traditional direct measurement methods are generally simple and reliable, but they are time consuming, laborious and cumbersome. In contrast, the proposed vision-based methods are efficient in detecting and observing the exterior disease features. In the present investigation, image processing algorithms are developed to detect leaf rot disease by identifying the color featureof the rotted leaf area. Subsequently, the rotted area was segmented and area of rotted leaf portion was deduced from the observed plant feature data. The results showed a promising performance of this automatic vision-based system in practice with easy validation. This paper describes the steps to achieve an efficient and inexpensive system acceptable to the farmers and agricultural researchers as well for studying leaf rot disease in betel vine leaf. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Segmentation Betel vine Digital image processing HVS Thresholding

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