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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1642 条记录，以下是121-130 订阅

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Single Foggy image Restoration under Hardy Space 3

Single Foggy Image Restoration under Hardy Space

引用

3rd IEEE international conference on image, vision and Computing (ICIVC)

作者： Wang, Shiping Liu, Kun Bi, Duyan He, Linyuan Wang, Chen Ma, Shiping Gao, Shan Air Force Engn Univ Sch Aeronaut & Astronaut Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781538649916

The foggy image recovery is one of hot topics in the image processing. Considering that the haze images fade the colors and reduce the contrast in the process of defogging, in this paper, we propose a novel regularization method of image defogging based on Hardy space transformation. Our method uses model analysis and mathematical derivation to use the transformed pseudo-differential operator, which obtains more statistical features and defines the extent of image defogging The effective comparison algorithm is preferred. Extensive quantitative and qualitative evaluations have been demonstrated that our algorithm improves effectively the defogging of real and simulated images.

关键词： image processing image defogging Hardy space pseudo-differential operator

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An Improved U-Net image Segmentation Network for Crankshaft Surface Defect Detection 13

An Improved U-Net Image Segmentation Network for Crankshaft ...

引用

13th Iranian/3rd international Machine vision and image processing conference (MVIP)

作者： Moosavian, Ashkan Bagheri, Elmira Yazdanijoo, Alireza Barshooi, Amir Hossein Tech & Vocat Univ TVU Dept Agr Engn Tehran Iran Iran Univ Sci & Technol IUST Sch Automot Engn Tehran Iran Irankhodro Powertrain Co IPCo Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Crankshaft is one of the mechanical components of the vehicle engine, and quality control of it holds significant importance in the production line. In this paper, a vision-based system was developed to detect apparent structural defects on the crankshaft surface. By examining the different approaches in computer vision tasks, the semantic segmentation technique was chosen to solve this problem. In the first stage, a dataset consisting of 400 crankshaft experimental images with structural defects such as scratch, pitting, and grinding were collected. Then, the Convolutional Neural Network (CNN) with MobileNet architecture was trained to detect apparent defects, and an Intersection over Union (IoU) evaluation criteria of 64.7% was obtained. In the third stage, some image processing techniques were used to increase the performance. By applying the DexiNed edge detection filter on the train-set images, the IoU was increased by 8.4%. Considering the importance of this issue in the automotive industry, it has been tried again to boost the performance by augmenting the dataset images. On the other hand, this can also prevent overfitting of the model. By training the model under the same conditions as the previous stages, the IoU in this stage increased by 13.2% and reached 86.3%.

关键词： defect detection crankshaft deep learning segmentation pitting scratch

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image Registration Based on Multi-Scale SIFT for Remote Sensing images

Image Registration Based on Multi-Scale SIFT for Remote Sens...

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3rd international conference on Signal processing and Communication Systems

作者： El Rube, Ibrahim A. Sharks, Maha A. Salem, Ashor R. Arab Acad Sci & Technol & Maritime Transport Coll Engn &Technol Alexandria Egypt

ISBN: (纸本)9781424444731

image registration is the first step in many application areas such as computer vision, remote sensing and medical image processing. image registration is achieved by aligning two or more images according to the estimated transformation between them. In this paper, we present an image registration algorithm, which combines the multi-scale wavelet transform with Scale Invariant Feature Transform (SIFT). First, images are decomposed into multiple scales using Wavelet Transform (WT), then the low frequency (approximation) image at certain level is input to SIFT algorithm. The proposed algorithm speeds up the calculation of the correspondences between images. Experimental results with different remote sensing images illustrate the accuracy of proposed algorithm.

关键词： image registration Scale Invariant Feature Transform correspondence points undecimated wavelet transform

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Integration of Deep Learning and Industrial computer vision Library for Motorcycle and Vehicle License Plate Recognition 2020

Integration of Deep Learning and Industrial Computer Vision ...

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3rd international conference on image and Graphics processing (ICIGP)

作者： Wu, Ting-Yu Liao, Hsien-Chou Lim, Zi-Yi Chaoyang Univ Technol 168 Jifeng E Rd Taichung Taiwan

ISBN: (纸本)9781450377201

The license plate recognition is widely used in daily life. Basically, the license plate recognition process is divided into two phases: detection and recognition. Most of the previous studies are simply use conventional pattern recognition techniques or deep learning. Therefore, the industrial computer vision library (Euresys Open evision) and deep learning are integrated to fulfill license plate recognition and presented in this paper. The proposed approach is divided into three phases: the first phase is to apply deep learning to detect the license plate in the image with a complicated background. The second phase is the license plate correction. A perspective transformation is used to correct the angle of the license plate. Finally, optical character recognition (OCR) is used to recognize the license plate. According to the experimental results, the accuracy of the proposed approach can reach 96.7% with an average identification time of 63.4ms. It shows that the proposed approach is feasible in a practical environment.

关键词： License plate recognition deep learning pattern recognition OCR

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Hyperspectral image Denoising Based on Multi-Resolution Gated Network with Wavelet Transform 3

Hyperspectral Image Denoising Based on Multi-Resolution Gate...

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3rd international conference on computer vision, image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Kengpeng Zhong, Fenfa Sun, Lei Sun Yat-sen University School of Systems Science and Engineering Guangzhou China

ISBN: (纸本)9781665459112

Hyperspectral image denoising is an essential pre-processing task. In this paper, a multi-resolution gated network based on wavelet transform (WMRGNet) is proposed for removing mixed noise of hyperspectral images. Firstly, based on the fact that hyperspectral images have strong spectral correlation, a spatial-spectral information extraction module is designed to use the current noisy band and its adjacent bands as the input of WMRGNet. Secondly, aim to fully consider the spatial local and global information of hyperspectral images, a multi-resolution feature extraction module is proposed, applying the discrete wavelet transform to divide the resolution into four scales, and the residual blocks to extract information of different resolutions. In addition, a gated layer is introduced for cross-resolution information interaction to enhance the feature fusion. Finally, a high-resolution image reconstruction module with multiple residual blocks is employed to extract high-resolution features. In the simulated data set experiments, WMRGNet removes Gaussian, stripe and deadline noise and preserves the detailed information of the hyperspectral images. © 2022 IEEE.

关键词： image denoising

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Research on Some Key Technologies of Deep Learning in the Field of computer vision 3

Research on Some Key Technologies of Deep Learning in the Fi...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Mao, Yirong Mao, Rui Yunnan Communications Vocational and Technical College 650500 China

ISBN: (纸本)9798350381931

The role of computer vision technology in the field of artificial intelligence development is very important, but there is a problem of poor application effect of key technologies. Traditional neural network algorithms cannot solve the problems of image classification and inaccurate image detection in computer visual perception tasks. In the tide of artificial intelligence, the combination of computer vision and deep learning has become an important force to promote technological progress. By imitating the processing mechanism of images and videos by human brain, deep learning has not only made a breakthrough in understanding complex visual information, but also demonstrated its incomparable ability in many fields. © 2024 IEEE.

关键词： image classification

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Whose album is this?

Whose album is this?

引用

2011 3rd National conference on computer vision, Pattern Recognition, image processing and Graphics, NCVPRIPG 2011

作者： Goel, Abhinav Jawahar, C.V. Centre for Visual Information and Technology International Institute of Information Technology Hyderabad 500032 India

ISBN: (纸本)9780769545998

We present a method to identify the owner of a photo album taken off a social networking site. We consider this as a problem of prominent person mining. We introduce a new notion of prominent persons, and propose a greedy solution based on an eigenface representation. We mine prominent persons in a subset of dimensions in the eigenface space. We present excellent results on multiple datasets downloaded from the Internet. © 2011 IEEE.

关键词： Apriori Clustering Eigenface Frequent Itemset Mining Prominent Person Mining

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Enhancing Multi-view Mammography image Classification: By using Breast Region Extraction Method and Statistical Features 13

Enhancing Multi-view Mammography Image Classification: By us...

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13th Iranian/3rd international Machine vision and image processing conference (MVIP)

作者： Bidabadi, Neda Shirany Mahmoudzadeh, Elham Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9798350350494;9798350350500

Breast cancer is one of the most dangerous diseases among women. Different methods are used to diagnose this cancer that among these, imaging and computer-aided systems are more common. In these systems, one of the most important step is preprocessing and removing unnecessary areas of the images, as well as extracting the chest area. In this paper, we present a method that consists of preprocessing, feature extraction, and using a machine learning classifier. In the preprocessing step, we propose a method to extract the region of interest in both angles of mammography images. The proposed novel method includes applying gamma correction thresholding to the images and obtaining two binary images based on the proposed threshold using the Otsu method. Results show the proposed method successfully removes the chest muscle with 98% accuracy. In the next, for feature extraction phase, we utilize three different methods for extracting features. Finally, by employing an Extra tree model classifier, we classify mammography images into normal and abnormal. By incorporating the block-based feature extraction method, we achieve 98% accuracy in classification. Overall, our approach demonstrates the effectiveness of preprocessing and feature extraction for diagnosing breast cancer using mammography images.

关键词： Mammography image Region of Interest Extraction Breast Cancer Statistical Feature Extraction

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vision-based 2D Vibration Displacement Measurement of Hoisting Vertical Rope in Mine Hoist 20

Vision-based 2D Vibration Displacement Measurement of Hoisti...

引用

3rd international conference on Artificial Intelligence and Pattern Recognition (AIPR)

作者： Wu, Ganggang Xiao, Xingming Ma, Chi China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9781450375511

In this paper, a non-contact, unmarked computer vision measurement method is presented and applied to measure the two-dimensional (2D) vibration displacement of hoisting vertical ropes. In this method, the primary work is to perform camera calibration of monocular vision using a neural network (NN) model. Then, in the image sequence, a straight line perpendicular to the hoisting rope is added by digital image processing (DIP) method, and their intersection region is regarded as the measuring target. Digital image correlation (DIC) algorithm at sub-pixel level is applied to locate the measuring target in image sequence. This method is used to measure the vibration displacement of an actual hoisting rope in mine, and the measurement results of three targets on the rope are consistent with tiny amplitude differences, which indicates that this method is feasible for the vibration measurement of hoisting vertical rope.

关键词： Hoisting vertical rope 2D vibration displacement computer vision neural network

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A Fast Contour Model-based Localization Method for Robotic Picking in Shrimp Production Line 18

A Fast Contour Model-based Localization Method for Robotic P...

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3rd international conference on Robotics, Control and Automation (ICRCA) / 3rd international conference on Robotics and Machine vision (ICRMV)

作者： Chen, Niya Ruan, Jiayang Yang, Wei ABB Corp Res 103 BEZ IT Pk Beijing Peoples R China ABB Engn Shanghai Ltd 4528 Kangxin Highway Shanghai Peoples R China

ISBN: (纸本)9781450365307

Irregular-shape food processing by robotic arms like shrimp picking is a common problem in industrial automation, which can be summarized as localization of particular points on an image, emphasizing on both good accuracy and high speed with relatively very limited hardware resources. In most cases, the points do not have a distinct visual characteristic in color or size. In this paper, first we outline the suspicious search range resorting to intelligently learning the coarse mapping function between shrimp shape and target points, based on the proposed contour model of shrimp body, which significantly simplifies numerical representation of the original image. Next, priori knowledge of the shrimp body is used for more accurate fine localization of the target points. More specifically, in this step, the shrimp body pose is normalized for edge extraction after proper rotation and projection. The extracted edge curve on the back of the shrimp is then analyzed to accurately pick out the target corner point. During validation, in the search region detection step, the method is able to efficiently avoid wrong search in neighboring joints of shrimp body. After finer localization of the target points, the final detection rate turns out to be 93%.

关键词： Robot vision robot food picking contour model neural network

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