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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1700 条记录，以下是131-140 订阅

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Design of Inclinometer Calibration Data Acquisition System Based on LabVIEW 3

Design of Inclinometer Calibration Data Acquisition System B...

引用

3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Yi, Zhaozang Chen, Juanjuan Liu, Rongming Chen, Hao Shanghai institute of measurement and testing technology Shanghai200000 China

ISBN: (纸本)9781510681880

This article introduces an automatic data acquisition system for the calibration device of the inclinometer using a circular grating encoder as the standard device. The system utilizes LabVIEW to develop a complete host computer system and employs vision modules to process, segment, and recognize the captured images of the inclinometer for indication. Through this system, data acquisition for inclinometers and angular encoders can be achieved, while automating the calibration process. © 2024 SPIE.

关键词： Calibration LabVIEW Optical character recognition Equipment Education and training Data acquisition Design image processing Human-machine interfaces Target recognition

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image Style Transfer-A Critical Review 3

Image Style Transfer-A Critical Review

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Zhang, Yakun George Washington University School of Engineering and Applied Science WashingtonDC United States

ISBN: (纸本)9781665472784

Style Transfer refers to the conversion of images into two different domains. Specifically, it provides a style image, converts any image into this style, and preserves the content of the original image as much as possible. image style transfer is now often employed in image processing applications. It offers a number of evaluation techniques for contrasting numerous prevalent and significant applications of image style transfer algorithms on various level. The examination of numerous Neural Style Transfer applications and unresolved issues for further study comes to a close in this paper. © 2023 IEEE.

关键词： image transfer neural network style transfer

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Improving image Dehazing Performance of Outdoor Scenes Using Contrast Enhancement Techniques 3

Improving Image Dehazing Performance of Outdoor Scenes Using...

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3rd international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Abin, Deepa Deshpande, Rohit Tidke, Atharva Chougule, Soham Dubey, Aditya Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350306989

image dehazing, a crucial computer vision task, aims to improve hazy images by removing the degradation brought on by atmospheric haze. However, the complexity of haze frequently limits the performance of traditional dehazing models, making it challenging to precisely figure out a transmission map approximation. The study involves a novel approach to augment the performance of image dehazing by combining various contrast enhancement techniques with a state-of-the-art dehazing model FFA-Net. The effectiveness of contrast enhancement techniques such as CLAHE, AGCWD, BBHE, DSIHE is investigated on the quality of the dehazed images. Then these techniques were combined with the popular FFA-Net model to form a new hybrid dehazing model. The experimental findings demonstrate that, with respect to no-reference picture quality metrics NIQE, PIQE, BRISQUE, and Average Entropy, the proposed hybrid model of CLAHE+FFA-Net slightly improves the current dehazing approach by slightly improving the Average Entropy and BRISQUE values, while also maintaining satisfactory NIQE and PIQE values. © 2023 IEEE.

关键词： Entropy

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A Nighttime Monitoring System for Combined Structural Stability based on Infrared image Recognition 3

A Nighttime Monitoring System for Combined Structural Stabil...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Li, Jieqi Guo, Shaoqiang Li, Wei Xijing University School of Civil Engineering Xi'an710123 China

ISBN: (纸本)9781665462167

Monitoring the combined structural stability under different scenario is an essential task for the security, hence, this paper designs the novel nighttime monitoring system for combined structural stability based on the infrared image recognition. Firstly, this study discusses the infrared image recognition algorithm considering the different features and methods. Then, the combined structural feature is discussed to further assist the processing of images. Finally, the nighttime monitoring system is implemented with the designed image processing algorithm. The proposed model utilizes the SURF algorithm to select the features. Based on the integral image, the SURF algorithm uses the Hessian operator to detect and obtain feature points. Through the testing, the performance before and after the processing for the original image is presented. And through the analysis on ratio of the number of correctly tested samples, the model is proven to be effective. © 2023 IEEE.

关键词： image recognition

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Low light scene enhancement for object detection and tracking using multiscale decomposition and fusion 3

Low light scene enhancement for object detection and trackin...

引用

3rd international conference on Range Technology, ICORT 2023

作者： Jana, Mayukhmala Roy, Pradipta University of Calcutta Dept.of RadioPhysics and Electronics Kolkata India DRDO Optronics Centre Integrated Test Range Chandipur India

ISBN: (纸本)9781665493314

Object tracking in low light condition for visual camera in CCTV, Surveillance and tracking applications is really challenging task. Many methods are proposed which has limited success of augmenting the scene for human vision, but not very effective for both human and machine vision. In this paper, we have proposed a way for improving the video's picture frames using a fusion approach, then applying a tracking methodology to demonstrate how the improved segmentation and tracking are both made possible by the enhancement. After comparison with other state-of-art techniques that improve lowlight images, our proposed methodology performs better both subjectively and objectively. © 2023 IEEE.

关键词： image enhancement

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Simple Industrial Cutting Machine Safety System Based on Computer vision 3

Simple Industrial Cutting Machine Safety System Based on Com...

引用

3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chiang, Chien-Lin Peng, Ming-Yuan Lin, I-Long Chou, Yu-Wei Fong, Ji-Men Chiang, Yi-Yuan Department of Computer Science and Engineering Tatung University Taipei Taiwan Graduate Institute of National Development and Mainland China Studies Chinese Culture University Taipei Taiwan Department of Computer Science and Information Engineering Vanung University Taoyuan Taiwan

ISBN: (纸本)9798350333862

Safety devices are often not fitted to many simple panel-cutting machines due to cost. Designing and installing low-cost safety devices on the simple panel-cutting machine improves the safety of operators. We propose a computer vision-based safety system with a camera to capture images of the operator and machine from above. Each captured image is analyzed by a neural network. If the operator's hands are too close to the cutting tool, the safety system sends a warning signal and stops the machine. The neural network used in the vision system is a ten-layer convolutional neural network. In addition to the input and output layers, there are five convolutional layers in the middle, followed by three fully connected layers. The input image undergoes five convolution operations to obtain feature maps of different granularity. With three fully connected layers, a combination of features is used to determine the positions of the operator's hands as a guideline for stopping the machine. To improve the generalization of the model, we collect thousands of training data. The experimental results show that the model has a low bias, low variance, and high accuracy characteristics. The accuracy of the verification data is over 98%. © 2023 IEEE.

关键词： Convolutional neural networks

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From Depth Sensing to Deep Depth Estimation for 3D Reconstruction: Open Challenges

From Depth Sensing to Deep Depth Estimation for 3D Reconstru...

引用

3rd international conference on image processing and vision engineering, improve 2023

作者： Hamesse, Charles Luong, Hiep Haelterman, Rob XR Lab Department of Mathematics Royal Military Academy Belgium imec - IPI - URC Ghent University Belgium

ISBN: (纸本)9789897586422

For a few years, techniques based on deep learning for dense depth estimation from monocular RGB frames have increasingly emerged as potential alternatives to 3D sensors such as depth cameras to perform 3D reconstruction. Recent works mention more and more interesting capabilities: estimation of high resolution depth maps, handling of occlusions, or fast execution on various hardware platforms, to name a few. However, it remains unclear whether these methods could actually replace depth cameras, and if so, in which scenario it is really beneficial to do so. In this paper, we show that the errors made by deep learning methods for dense depth estimation have a specific nature, very different from that of depth maps acquired from depth cameras (be it with stereo vision, time-of-flight or other technologies). We take a voluntarily high vantage point and analyze the state-of-the-art dense depth estimation techniques and depth sensors in a hand-picked test scene, in the aim of better understanding the current strengths and weaknesses of different methods and providing guidelines for the design of robust systems which rely on dense depth perception for 3D reconstruction. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Stereo image processing

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Research on image Enhancement processing Algorithms in Computer Art Education 3

Research on Image Enhancement Processing Algorithms in Compu...

引用

3rd international conference on Optics and Machine vision, ICOMV 2024

作者： Li, Xiaojing Shanghai Donghai Vocational and Technical College Shanghai China

ISBN: (纸本)9781510680319

This research paper explores the application of image enhancement processing algorithms in the context of computer art education. The paper acknowledges the significance of high-quality images in art education and addresses the challenges in enhancing image quality due to factors like equipment performance, environmental conditions, and photographic techniques. It delves into various image enhancement methods, such as those based on physical models for dehazing and underwater image enhancement, including the dark channel prior theory and methods that do not require prior knowledge of camera parameters. The study also covers the importance of objective and precise evaluation of image quality changes before and after processing, highlighting the necessity for reliable image quality evaluation methods. It introduces the Unsupervised Low-Light Enhancement Algorithm (ULEA) based on attention mechanisms, detailing its network structure, loss functions, and the impact of these functions on low-light image enhancement processing. Through experimental research and comparative analysis, the paper demonstrates the effectiveness of ULEA in enhancing low-light images, significantly improving overall brightness and image texture layers compared to traditional and deep learning-based methods. In conclusion, the paper presents a compelling case for the practical application of advanced image enhancement algorithms in computer art education, proving their effectiveness in enhancing the visual quality of images and their applicability in a variety of conditions. © 2024 SPIE.

关键词： image enhancement

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The Application of Computer vision Target Recognition Technology in Autonomous Driving 3

The Application of Computer Vision Target Recognition Techno...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Wang, Yaning Bi, Xin TongJi University Shanghai200000 China

ISBN: (纸本)9798350376173

In recent years, the rapid growth of Internet of Things (IoT) technology and computer vision (CV) technology has provided strong support for the application of deep learning (DL) in various fields. Among them, DL based object detection algorithms have attracted much attention due to their excellent performance. Vehicle target detection, as an important branch of object detection, is crucial for the construction of intelligent driving and intelligent transportation systems. In autonomous driving technology, using machines to detect and recognize traffic signs, vehicles, and pedestrians is of great significance for improving driving safety, reducing labor costs, and demonstrating broad application prospects. In this paper, a target recognition algorithm based on CV and DL is proposed to improve the target detection capability of auto drive system. This algorithm combines the image processing technology of CV and the feature extraction and classification ability of DL to achieve fast and accurate recognition of targets such as vehicles and pedestrians in road environments. Experimental results show that through training and optimizing the DL model, the algorithm can perform well in complex and changeable traffic scenes and provide reliable target detection support for the auto drive system. © 2024 IEEE.

关键词： Traffic signs

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Meter image Enhancement Method in High Light Substation Based on improved CycleGAN 3

Meter Image Enhancement Method in High Light Substation Base...

引用

3rd international conference on Optics and image processing, ICOIP 2023

作者： Chen, Xi Fan, Shaosheng Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9781510667426

When the patrol robot recognizes the instrument under strong light, the collected instrument image has serious exposure, details loss and other phenomena. Aiming at the above problems, an improved CycleGAN method for enhancing the instrument image in the substation under strong light is proposed. First of all, for the generator, the CSD network and the self-attention mechanism ACmix module are improved to improve the lighting processing effect. Secondly, the idea of Patch-GAN is adopted in the discriminator, and the feature graph is mapped to an N×N matrix at the end to improve the processing ability of the details. The experimental results show that the peak signal-to-noise ratio of the image is increased from 15dB to 20dB after the above improvement;The index of structural similarity increased from 0.5 to 0.8;The recognition accuracy after enhancement has also been improved by 6%. In conclusion, the method in this paper can eliminate the influence of illumination on instrument recognition, and has strong robustness. © 2023 SPIE. All rights reserved.

关键词： image enhancement

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