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

共 1646 条记录，以下是391-400 订阅

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Hand Down, Face Up: Innovative Mobile Attendance System Using Face Recognition Deep Learning 3rd

Hand Down, Face Up: Innovative Mobile Attendance System Usin...

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3rd international conference on computer vision and image processing (cvip)

作者： Agrawal, Aditi Garg, Mahak Prakash, Surya Joshi, Piyush Srivastava, Akhilesh M. Indian Inst Technol Indore Discipline Comp Sci & Engn Indore 453552 India

ISBN: (纸本)9789813292918;9789813292901

computer vision is considered as the science and technology of the machines that see. When paired with deep learning, it has limitless applications in various fields. Among various applications, face recognition is one of the most useful real-life problem-solving applications. We propose a technique that uses image enhancement and facial recognition technique to develop an innovative and timesaving class attendance system. The idea is to train a Convolutional Neural Network (CNN) using the enhanced images of the students in a certain course and then using that learned model, to recognize multiple students present in a lecture. We propose the use of deep learning model that is provided by OpenFace to train and recognize the images. This proposed solution can be easily installed in any organization, if the images of all persons to be marked this way are available with the administration. The proposed system marks attendance of students 100% accurately when captured images have faces in right pose and are not occluded.

关键词： Automated attendance management Mobile biometrics Face recognition image enhancement OpenFace Convolutional neural network (CNN)

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Artistic image Enhancement Based on Iterative Contrastive Learning

Artistic Image Enhancement Based on Iterative Contrastive Le...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： YiRan Tao RongJuan Wang Kyonggi University Suwon Gyeonggi-do Korea

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

This research work presents a novel artistic image enhancement algorithm based on iterative contrastive learning to address the challenges of image degradation in computer vision systems. The proposed methodology integrates efficient de-noising, oversampling weight incorporation, and probability distribution control for comprehensive image enhancement. The algorithm is trained on a diverse data-set of low-light images and corresponding references, demonstrating its adaptability to different scenarios. Simulation results demonstrate the effectiveness of the algorithm in improving image quality, highlighting its potential for advancements in computational visual systems. The proposed approach contributes to ongoing research in image processing and enhancement, and offers a promising solution to the challenges posed by diverse operational environments and degradation issues in optical representations

关键词： Degradation Sentiment analysis Simulation Noise reduction Contrastive learning Visual systems Optical imaging

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A Real-Time Multimodal Deep Learning for image-to-Cartoon Conversion

A Real-Time Multimodal Deep Learning for Image-to-Cartoon Co...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Raja Pavan Karthik Kalla Yadu Vamsi Veeramreddy Sourya Tejarsha Reddy S Abhishek T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Rolls-Royce Inc Bangalore India

In the era of digital imagery, there is a great interest in finding new and creative ways to express ourselves and make our images look beautiful. One such fascinating method is cartoonization, a process that transforms ordinary images into visually appealing cartoon images. This paper explores the integration of cutting-edge computer vision algorithms, traditional image processing methods, and Neural Networks to achieve cartoonization. The main focus is on combining object segmentation with cartoonization in a smooth and seamless way, which offers a unique and innovative approach to improving images. By thoroughly considering various techniques and how they can be used together, our research not only gives a complete understanding of these methods but also highlights how they can transform the field of digital artistry. By exploring the integration between methods, the study sheds light on how these techniques contribute to the evolving landscape of digital artistry. The research suggests that the fusion of computer vision, traditional image processing, and Machine Learning techniques holds promising potential for pushing the boundaries of creative expression in the digital realm, offering new ways for creating efficient cartoon images.

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Research on athletics image Classification and Application Based on Feature Extraction Algorithm 22

Research on athletics Image Classification and Application B...

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3rd Asia-Pacific conference on image processing, Electronics and computers, IPEC 2022

作者： Zhao, Jun Wuhan430205 China

ISBN: (纸本)9781450395786

As a form of popular culture, athletics can promote people's physical and mental health, and more and more athletics visual images are deeply rooted in people's hearts through various media, which has a positive and extensive impact on modern society. With the rapid development of multimedia technology and Internet, it is an urgent problem to combine digital image processing, computer vision technology and database technology to create an effective description and retrieval technology for athletics images. All kinds of visual images in the field of athletics should be studied by athletics visual culture, which is of certain value for explaining their cultural ideographic function in contemporary public life and improving the public's aesthetic and appreciation ability of athletics. All kinds of visual images in the field of athletics should be studied by athletics visual culture, which is of certain value for explaining their cultural ideographic function in contemporary public life and improving the public's ability to appreciate and appreciate athletics. Evolution makes the visual system adaptive to the external environment. It looks for the extreme points in the spatial scale, and extracts their position, scale and rotation invariants, so as to form a 128 dimensional feature vector. In this paper, the feature extraction algorithm is applied to athletics image classification, which can effectively reduce the amount of data and have an important impact on improving the efficiency of image classification. © 2022 Association for Computing Machinery. All rights reserved.

关键词： image classification

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Fast vision Transformer via Query Vector Decoupling 3

Fast Vision Transformer via Query Vector Decoupling

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3rd IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Sun, Donghao Liu, Jiashun Liu, Jiaxing Zhang, Zixuan School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (纸本)9781665425186

Although transformer structure has become the defacto standard of the natural language processing task, it still has limited application in computer vision. In vision, attention is either combined with or replaces certain modules in a convolutional network, while keeping its overall framework intact. It proves that this reliance on CNNs is unnecessary for the pure transformer directly applied to image patch sequence can successfully classify the images. When large amounts of data are pre-trained and transmitted to multiple small and medium-sized image recognition benchmark (imageNet, CIFAR-100, VTAB, etc.), the vision Transformer (ViT) achieves remarkable results with less computational resources required than existing convolutional networks. Unlike the previous calculation that weighs attention, the model in this paper has been optimized, reducing the time complexity from 0(n2) to 0(n • logn), largely improving the model's speed. Therefore, a new model is developed-Fast vision Transformer (Fast VIT). © 2021 IEEE

关键词： computer vision

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A vision Algorithm for Robot Seam Tracking Based on Laser Ranging

A Vision Algorithm for Robot Seam Tracking Based on Laser Ra...

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computer, Control and Robotics (ICCCR), international conference on

作者： Minghao Dong Xiansheng Qin Yuru Fu Hao Xu Jian Wang Di Wu Zhanxi Wang School of Mechanical Engineering Northwestern Ploytechnical University Xi'an China

In automatic welding processing, high temperature likely induces local deformation of the weldment, resulting in accuracy deviation and welding quality. Therefore, the research on weld tracking technology is significant. Most current weld trackers are installed at the end of the welding torch. Due to the tracker volume, interference often occurs in welding operations under complex conditions. This paper designs and develops a seam tracking system for human-computer interactive mobile robots. The spatial coordinates were obtained by camera calibration, line laser calibration, and hand-eye calibration. The seam feature points are extracted by image processing. A seam tracking experimental platform is built, and the results are compared with the seam's actual fitting curve to verify the system's feasibility.

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Optimization and Implementation of image Defogging Algorithm Based on Field Programmable Gate Array

Optimization and Implementation of Image Defogging Algorithm...

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computer Science and Blockchain (CCSB), international conference on

作者： Zhenzhong Hao Yao Yu Jing Sun Anhui Xinhua University School of Electronic Engineering/School of Intelligent Manufacturing Hefei China Wuxi University School of Electronics and Information Engineering Wuxi China

images collected under severe weather conditions have problems such as poor contrast and reduced clarity. The deterioration of image quality limits the accuracy of computer vision and the efficiency of automated tasks. This article proposes an image dehazing algorithm based on contrast limited adaptive histogram equalization (CLAHE) and improved multi-scale retinex (MSR). In this algorithm, the input foggy degraded image is first processed by the contrast limited adaptive histogram equalization (CLAHE) algorithm and then the MSR algorithm. When processing the image with the MSR algorithm, the Gamma correction factor is introduced to estimate the incident light, and the surround function in the algorithm is optimized. The results show that compared with the original image, the image processed by this algorithm has improved the information entropy, average gradient, and standard deviation of the image. The hardware circuit was designed and the video real-time dehazing was successfully demonstrated on the field programmable gate array (FPGA), which improved the quality of the video image. A digital analysis of board-level resources and function consumption was conducted, proving that the hardware system in this article belongs to the low-power category.

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Visual Target Measurement Method in Unknown Environment Based on Stereo SLAM 3rd

Visual Target Measurement Method in Unknown Environment Base...

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3rd international conference on Artificial Intelligence, 2022

作者： Ma, Lin Zheng, Jiayi Guo, Shizeng Zhang, Zhongwang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China China National Aeronautical Radio Electronics Research Institute Shanghai China

ISBN: (纸本)9789811694226

With the development of UAV technology and deep learning, the demand for 3D information acquisition of unknown environment is increasingly strong. The 3D information of unknown environment can be widely used in unmanned driving, augmented reality, disasters rescue and other scenes. Using computer vision to estimate the 3D information of the target in the scene has become the mainstream method. Because the single image lacks depth information, it is usually necessary to know the size of a reference object in the scene, or to shoot continuously by using SLAM algorithm to obtain the 3D information. The stereo vision can solve the shortcomings of monocular vision. It uses two cameras to shoot and uses the spatial geometric relationship to recover the 3D information. This paper uses stereo SLAM to track and map, and extracts feature points to calculate its 3D coordinates of the feature points. After a series of image processing operations, the feature points are used to estimate the size of target object. Finally, the accurate measurement of road width and tree height is realized. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stereo vision

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A Comparative Study on Single image De-Raining Using Convolutional Neural Network 3rd

A Comparative Study on Single Image De-Raining Using Convolu...

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3rd international conference on Data, Engineering, and Applications, IDEA 2021

作者： Shrivastava, Poornima Gupta, Roopam Moghe, Asmita A. Department of Electronics and Communication UIT RGPV Bhopal India Department of Information Technology UIT RGPV Bhopal India

ISBN: (纸本)9789811946868

Most of the algorithms for computer vision require a clear image for its processing. For finding the proper solution to the visual degradation of the image due to streaks caused by rain, an effective de-raining algorithm needs to be developed. De-raining is the task of removing streaks of rain and their effect from the distorted image that contains rain. The huge success of methods based on learning and that too CNN-based methods in different domains have prompted its use in de-raining. For effectively obtaining the automatic feature information, deep CNN methods are used. The efficiency of various methods of de-raining is applied and tested on datasets such as Rain1200, Rain100H, Rain100L, Rain1400, and many more that are discussed and analyzed here. The quantitative metrics for the comparison are PSNR and SSIM. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Home Security System Using RaspberryPi 3rd

Home Security System Using RaspberryPi

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3rd international conference on Energy Systems, Drives and Automations, ESDA 2020

作者： Banerjee, Pratik Datta, Parabi Pal, Suhankar Chakraborty, Sangbit Roy, Ankur Poddar, Satwik Dhali, Soumya Ghosh, Arijit Department of AEIE RCC Institute of Information Technology Canal South Road Kolkata700015 India

ISBN: (纸本)9789811672736

With the development of advanced technologies in the field of robotics and computer vision, real time image processing has become a very popular tool. The paper aims at using the RaspberryPi camera along with suitable machine learning algorithms to perform face detection and face recognition for security purpose. This paper portrays machine-learning approach for face recognition with high identification rates using Intel’s open source framework called OpenCV (Open Source computer vision) library in python programming language. RaspberryPi-cam is used to capture picture of the faces that are to be stored in the database in the form of grayscale and colored image. This is a three-stage process namely, face detection, data gathering, and face recognition, which will match the live faces with facial pictures stored in the database and give us identification details of the person under consideration. If the matching index is 45% or more then only the face will be recognized properly. Here, Haar-Cascade algorithm is used and this whole setup is successfully demonstrated using a RaspberryPi model 3B hardware setup. Based on successful face recognition, a miniature door will open and the security of the system will be ensured. Whereas, if the face matching index is below the threshold, then the face is not to be identified and the door will remain closed. This is a real-time implementation of smart security application using RaspberryPi. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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