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

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An Analysis of Different image Preprocessing Techniques for Determining the Centroids of Circular Marks Using Hough Transform 2

An Analysis of Different Image Preprocessing Techniques for ...

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2nd international conference on Frontiers of Signal processing (ICFSP)

作者： Adatrao, Sagar Mittat, Mayank Indian Inst Technol Dept Mech Engn Madras Tamil Nadu India

ISBN: (纸本)9781509083152

In this paper, different image preprocessing methods are compared based on their ability to remove noise and to segment the images. Two filters, namely, Median filter and Wiener filter, and seven image segmentation methods, namely, Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny edge detection, basic global thresholding (BGT) and Otsu's global thresholding (OGT) are applied on the images of eight different signal-to-noise ratios (SNRs) ranging from 2.7 to 17.8. First, the preprocessing results are qualitatively compared by visual inspection for image SNRs of 17.8 (high) and 2.7 (low). Then the effects of different preprocessing methods are quantitatively analyzed by determining the accuracy of centroid detection of circular marks using Hough transform. The quantitative comparison showed that Median filter plus BGT or OGT give better results than other methods for low SNRs, and Wiener filter plus LoG detector provided higher accuracy compared to other methods for high SNRs. The application of this work is in many areas, for example, biomedical imaging, flow diagnostics and computer vision, where we detect the sizes or locations of circular objects in images.

关键词： image preprocessing centroids Hough transform thresholding filters

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Densely Connected AutoEncoders for image Compression 19

Densely Connected AutoEncoders for Image Compression

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2nd international conference on image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Song Zebang Sei-ichiro, Kamata Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781450360920

image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computer vision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.

关键词： Lossy image compression U-net like structure Densely AutoEncoders convolutional neural networks (CNNs)

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Research on the Application of computer vision Technology in the Adversarial Judgment System in Competitive Sports Competitions 2

Research on the Application of Computer Vision Technology in...

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2nd international conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2023

作者： Tang, Yong Hunan University of Humanities Science and Technology Hunan Changsha China Hunan Normal University Hunan Changsha China

ISBN: (纸本)9798350339765

In recent years, computer vision technology has experienced a period of rapid development, and it has achieved remarkable results on core problems in various fields. At the same time, with the iterative development of 5G communication, mobile Internet, terminal equipment and other related technologies, massive amounts of video data will continue to be generated every minute and every second in the world. The large-scale video data has prompted the research carrier of computer vision to gradually develop from static pictures to video sequences, and the socially concerned objects are also shifting from target instance information in single-frame images to target motion information in consecutive multi-frame images. The automatic tracking technology of complex motion and posture based on machine vision technology effectively utilizes the image data processing ability of computer vision technology, and provides a strong data foundation for the application in the confrontation judgment system in competitive sports competitions. © 2023 IEEE.

关键词： computer vision

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Performance improvement of enhancement methods for underwater images 2

Performance improvement of enhancement methods for underwate...

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2nd international conference on Communication and Electronics Systems, ICCES 2017

作者： Kaur, Jasleen Sohi, Neelofar Department of Computer Engineering Punjabi University Patiala India

ISBN: (纸本)9781509050130

Underwater images are highly important for studying various oceanic objects and underwater vegetation & life. Underwater scenes are usually veiled by the light interaction with the medium: low luminosity, absorption and many more. Capturing a clear and good quality underwater image is not an easy work, since underwater imaging usually suffers from unbalanced illumination, unbalanced pixel distribution, over-enhanced and under-enhanced areas, contrast and appearance of colors. Hence, study of underwater image enhancement techniques is necessary for oceanic application of computer vision. A large number of methods for enhancement of underwater images are available. In this study, few prominent enhancement methods are applied for enhancement of underwater images. Two methods are proposed based on BBHE, DSIHE, Gamma Filter, Butterworth Filter and color correction. Comparison of results produced by proposed methods is done with existing methods using performance metrics like RMSE, PSNR, Contrast, Entropy, Gradient. The proposed methods outperform the other methods. © 2017 IEEE.

关键词： Butterworth filters

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A Saliency Detection Method via Color Contradistinction and Background Similarity 2

A Saliency Detection Method via Color Contradistinction and ...

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2nd IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Zhang, Xufan Wang, Dianhong China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781728151021

image saliency detection helps the computer quickly analyze the surrounding environment, locate the interested objects and extract the salient regions from the background. Conventional image saliency detection algorithms usually have high computational complexity, and the detection results seems to be less than satisfactory under complex application circumstances. In this paper, a novel image saliency detection method via color contradistinction and background similarity is proposed, which is effective. In our method, the input image is reconstructed according to block-based compressed sensing for reducing the computational complexity. Then, a weighted local contrast principle and a background similarity calculation framework are designed to obtain two different primary saliency maps. Finally, a weighted fusion strategy is used to combine the two saliency maps to get the final result which has the best detection performance. The experimental results show that the proposed method has good detection performance in terms of accuracy and running time.

关键词： computer vision saliency detection compressed sensing

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Balanced Two-Stage Residual Networks for image Super-Resolution 30

Balanced Two-Stage Residual Networks for Image Super-Resolut...

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30th IEEE/CVF conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Fan, Yuchen Shi, Honghui Yu, Jiahui Liu, Ding Han, Wei Yu, Haichao Wang, Zhangyang Wang, Xinchao Huang, Thomas S. Univ Illinois Beckman Inst Urbana IL 61801 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

ISBN: (纸本)9781538607336

In this paper, balanced two-stage residual networks (BT SRN) are proposed for single image super-resolution. The deep residual design with constrained depth achieves the optimal balance between the accuracy and the speed for super-resolving images. The experiments show that the balanced two-stage structure, together with our lightweight two-layer PConv residual block design, achieves very promising results when considering both accuracy and speed. We evaluated our models on the New Trends in image Restoration and Enhancement workshop and challenge on image super-resolution (NTIRE SR 2017). Our final model with only 10 residual blocks ranked among the best ones in terms of not only accuracy (6th among 20 final teams) but also speed (2nd among top 6 teams in terms of accuracy).

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Novel Edge Detection Using BP Neural Network Based on Threshold Binarization

Novel Edge Detection Using BP Neural Network Based on Thresh...

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2nd international conference on computer and Electrical Engineering

作者： Mehrara, Hamed Zahedinejad, Mohammad Pourmohammad, Ali Iran Ctr Elect Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9780769539256

One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the Edge of image where the preciseness and reliability of its results will affect directly the comprehension machine system made for objective world. Several edge detectors have been developed in the past decades, although no single edge detectors have been developed satisfactorily enough for all application. In this paper, a new edge detection technique is proposed basis on the BP neural network. Here, it is classified the edge patterns of binary images into 16 possible types of visual patterns. In the following, After training the pre-defined edge patterns, the BP neural network is applied to correspond any type of edges with its related visual pattern. The results demonstrated that the new proposed technique provides the better results compared with traditional edge detection techniques while improved the computations complexity.

关键词： image processing Edge-detection image binarization threshold Neural Networks

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A Seam Deviation Real-time Detection Method based on Laser vision Technology

A Seam Deviation Real-time Detection Method based on Laser V...

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2nd international conference on computer Science and Applications (CSA)

作者： Zhang, Wen-ming Liu, Xiao-xi Xu, Jian Shenyang Univ Welding Dept Mech Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781479999613

This paper introduces a method of monitoring seam center real-time, and it can overcome the environment factor effectively. The seam image processing procedure including: Firstly, the seam image is tested by machine vision system based on structured-light. Then, the laser stripe direction is divided reliably by threshold segmentation and filtering based on MATLAB. Thirdly, the seam center is tested accurately by extremum method. Lastly, the laser stripe, corresponded by welding gun, is located by averaging method. The results suggest that the discrepancy fits the welding requirement.

关键词： real-time monitor threshold segmentation extermum method averaging method

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Intelligent Traffic Monitoring and Management Based on computer vision Technology 2

Intelligent Traffic Monitoring and Management Based on Compu...

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2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Chen, Chunmei Shandong Transport Vocational College Department Of Vehicle Engineering Shandong Weifang China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

Traditional traffic monitoring systems are limited by the number of devices and the efficiency of location data processing and analysis, making it difficult to achieve timely perception and response to traffic conditions, and cannot adapt to dynamic changes in traffic conditions. By utilizing machine vision technology, it can compensate for the shortcomings in traditional traffic monitoring systems and improve the efficiency of intelligent traffic monitoring and management. By using cameras and other devices to collect image data from roads, preprocessing operations such as image denoising are performed on the collected image data to improve image quality and extract traffic information. This article uses the YOLO (You Only Look Once) object detection algorithm to detect and recognize objects in the collected images, and uses a Kalman filter for vehicle motion analysis. It can predict its direction and speed of travel, display the results of traffic flow and road condition analysis to traffic workers, facilitate relevant personnel to understand the traffic situation, and adjust and modify traffic management strategies. After using computer vision technology to achieve intelligent traffic monitoring and management, the average traffic diversion time was reduced by 48 minutes and the average accident rate was reduced by 0.3%. Intelligent traffic monitoring and management based on computer vision technology can provide functions such as illegal behavior monitoring and traffic flow optimization by processing and analyzing image data in traffic roads, improving the level of traffic management and road traffic safety. © 2024 IEEE.

关键词： Efficiency

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Method of Detecting Logistics Driver's Fatigue State based on computer vision

Method of Detecting Logistics Driver's Fatigue State based o...

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2nd international conference on computer Science and Applications (CSA)

作者： Zhao, Guo-fang Han, Ai-xia Hebei Coll Ind & Technol Dept Comp Technol Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781479999613

This paper proposes a more accurate way to detect the logistics driver fatigue state. In logistics transportation vehicle driving process, there are differences between the blinking frequency and the closing time of logistics driver's eyes. According to the difference, the logistics driver's fatigue state can be able to be detected. Using the computer, it can strengthen the processing to the image acquisition of logistics driver. And the improving the resolution of the image can provide the basis for logistics driver fatigue detection. The experimental results show: using a fuzzy detection method in this paper, can greatly improve the accuracy of detection, and overcome the drawbacks of traditional algorithm. That obtained the satisfactory results.

关键词： fatigue state computer vision logistics driver a fuzzy detection

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