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

共 1803 条记录，以下是491-500 订阅

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vision-Based Hand Gesture Recognition System for Assistive Communication Using Neural Networks and GSM Integration

Vision-Based Hand Gesture Recognition System for Assistive C...

引用

Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pooja Singhal Sonia Verma Ritu Gupta Rishi Kumar Rajneesh Kumar Arya Department of Computer Science ABES Engineering College Ghaziabad Uttar Pradesh India Department of Computer Science Bhagwan Parshuram of Institute and Technology GGSIPU Delhi India Department of Computer Science and Enggineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This paper explores the application of vision-based hand gesture recognition in addressing communication challenges faced by deaf and dumb individuals within the realm of human-computer interaction (HCI). Highlighting the barriers posed by conventional communication methods and sign language interpretation, the paper introduces the Dumb Aid Phone system as an innovative solution. This system, functioning as an interface, enables seamless interaction for non-speaking individuals with their surroundings. Utilizing image processing and neural networking, the system captures and interprets hand gestures, matching them with a database to generate corresponding speech sounds. Enhanced by a web camera and a GSM module, the Dumb Aid Phone system not only facilitates effective communication but also holds the potential to revolutionize accessibility, contributing to a more inclusive society for the deaf and dumb community.

关键词： Hands GSM Human computer interaction Sign language image recognition Databases Neural networks Feature extraction Speech processing Computational intelligence

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Masked Face Inpainting Using Denoising Diffusion Probabilistic Models

Masked Face Inpainting Using Denoising Diffusion Probabilist...

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Signal processing and Intelligent Computing (SPIC), international conference on

作者： Bin Zhao Fang Li Fengxiang Li Shenyang Ligong University Shenyang China Liaoning Guidaojiaotong Polytechnic Institute Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

image inpainting in computer vision is a challenging task, as most existing approaches are trained for certain mask distributions, which limiting their generalization capability when confronted with unknown mask types. However, rather than producing semantically significant texture, training with pixel-level and perceptual losses frequently results in the extrapolation of basic texture in absent areas. In this paper, we propose an image inpainting approach based on Denoising Diffusion Probabilistic Model (DDPM). We utilize a pretrained unconditional DDPM as a generative prior. We alter the generative process of the model, enabling accelerated resampling for unknown regions based on known information from the given image. Since our approach does not involve any adjustments or modifications to the DDPM network itself, it can handle any form of mask and generate high-quality and diverse outcomes. The CelebA-HQ dataset experimental results show that our approach produces state-of-the-art performance.

关键词： Training Extrapolation computer vision Limiting image synthesis Noise reduction Signal processing algorithms Diffusion models Probabilistic logic Faces

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Low Texture 3D Reconstruction Based on Deep Learning Hierarchical Networks

Low Texture 3D Reconstruction Based on Deep Learning Hierarc...

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Yaohui Hou Zhuo Yang Yang Hu Lijun Wang Electronic Computer Science and Technology Program North University of Technology Beijing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

In scenes with sparse image features, conventional neural radiation fields perform poorly due to inadequate fitting of high-frequency functions. We enhance the depth-supervised constraint model and introduce a novel sampling strategy that utilizes depth uncertainty to guide sampling by sampling regions with more deterministic depth information. In addition, we propose a sensing framework that focuses on regions with uncertain depth values by layering sensing through a KAN network. Quantitative analysis shows that our method improves about 2.5%, 3.6% and 8.0% in PSNR, SSIM and LPIPS metrics, respectively, compared to existing methods.

关键词： Measurement Solid modeling Correlation Uncertainty Three-dimensional displays Statistical analysis Neural radiance field Sensors image reconstruction Standards

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Plastic image detection from UAV images using YOLOV8

Plastic image detection from UAV images using YOLOV8

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2nd international conference on computer vision and Internet of Things (ICCVIoT 2024)

作者： M. Nivaashini S. R. Dhayal C. Gokul V. T. Sanjay M. Sharan Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Tamil Nadu India

ISBN: (数字)9781837242535

Plastic pollution is a critical environmental issue that demands innovative monitoring solutions. This study utilizes the YOLOv8 deep learning algorithm in combination with Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras to automate plastic detection in aerial imagery. A custom drone dataset, with 85% of the images containing plastic, was developed to evaluate detection performance. The results demonstrate that YOLOv8 can effectively identify plastic in complex environments, offering a scalable and efficient alternative to traditional ground surveys. However, challenges such as varied plastic types, sizes, and visual complexity remain. This UAV-based approach shows promise for large-scale applications, including tracking cleanup initiatives, mapping plastic distribution, and assessing environmental impacts. Future research could improve this methodology by incorporating advanced image processing techniques to address plastic variability and enhance detection accuracy. Overall, the findings highlight UAVs as a valuable tool for comprehensive plastic pollution monitoring and contribute to advancing environmental conservation efforts.

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Conditional Diffusion Model for image Steganography

Conditional Diffusion Model for Image Steganography

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Hongjun Fan Ziping Zhao Wei Xia School of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

To ensure robust, high-capacity, and secure communication, we propose a conditional diffusion model for coverless image steganography, called CDIS, which not only generates realistic stego images but also successfully extracts valid secret images in the case of distorted stego images. CDIS utilizes the DDIM inversion technique to achieve the transformation between images, ensuring the reversibility of steganography. Additionally, an image encoder is also used to learn the high-level semantics of the secret image and the stego image, thus speeding up the diffusion and generation process in a controlled manner. The data sampled by the latent semantics module follows the same probability distribution as the high-level semantics of real images, serving as a condition for generating the stego image, thus realizing steganography without embedding. We conduct comprehensive experiments to demonstrate the advantages of our proposed CDIS framework in terms of robustness and security.

关键词： Steganography Machine vision Semantics Process control Diffusion processes Diffusion models Information retrieval Robustness Probability distribution Security

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Optimizing Semantic Segmentation for Enhanced Football Analytics: A Pixel-level Approach 2

Optimizing Semantic Segmentation for Enhanced Football Analy...

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2nd international conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Malakreddy, Bharathi Venkataraman, Sadanand Khan, Mohammed Sinan Nidhi Padmanabhuni, Srinivas Natarajan, Santhi Research & Consultancy Cell BMS Institute of Technology & Management Bengaluru India Department of Computer Science and Engineering Research Shiv Nadar University Chennai India Department of Artificial Intelligence & Machine Learning BMS Institute of Technology & Management Bengaluru India *** Bengaluru India

Semantic Segmentation, a pivotal technique in image analysis, is adeptly leveraged in this research to bolster sports analytics, with a concentrated focus on football. A comprehensive pipeline is unveiled for an in-depth analysis of a select portion of the IAUFD 100k dataset, encompassing 2030 manually annotated football images. The methodology entails a thorough evaluation and comparison of diverse semantic segmentation models, supplemented by the integration of advanced pre-processing strategies and optimal training techniques. Such a holistic approach culminates in a marked enhancement in model performance, as evidenced by a significant uptick in the mean Intersection over Union (mIoU). This research offers granular, object-oriented insights that substantially augment player tracking, action recognition, and event detection in football. The conclusive remarks of the study highlight prospective avenues for further research, emphasizing the potential incorporation of Explainable AI and advanced Metamorphic and Security Testing to fortify sports analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sports

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VisioLingua: IVIS (Intelligent vision Interpretation System) 2

VisioLingua: IVIS (Intelligent Vision Interpretation System)

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Avinash, N.J. Anushree Gaurav, K. Jagathraj, A.B. Mangalore Institute of Technology and Engineering Dept. of Electronics& Communication Engineering Moodbidri India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Visually impaired individuals face significant challenges in accessing textual and Braille-based information, limiting their independence and engagement with the world. To address these issues, this work introduces an innovative assistive system that combines machine learning, computer vision, and text-to-speech technologies. This paper discusses the design and implementation of a system that enables real-time recognition and processing of printed text and Braille characters, converting them into audible speech for immediate use. Convolutional neural networks (CNNs) powered by TensorFlow and Keras ensure high accuracy in Braille character recognition. At the same time, OpenCV handles crucial image preprocessing steps such as grayscale conversion, thresholding, and contour detection. Text recognition is facilitated through Tesseract OCR, and the pyttsx3 library provides efficient multilingual text-to-speech conversion. Compact and user-friendly, the system has the potential to significantly improve the quality of life for visually impaired individuals by fostering independence and inclusivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on attention-based multiscale information fusion with the real-time instance segmentation method 2

Research on attention-based multiscale information fusion wi...

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2nd international conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： He, Yifan Huang, Hua Li, Qishen Zhang, Guiwen School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network. © 2022 IEEE.

关键词： computer vision

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An Improved Faster and Novel Methodology for Diabetes Ulcer Classification Based on Customized CNN 2

An Improved Faster and Novel Methodology for Diabetes Ulcer ...

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2nd IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Rajalakshmi, D. Tharunya, R. Sri Sai Ram Inst Technol Comp Sci & Engn Chennai Tamil Nadu India Sri Sai Ram Inst Technol ME Big Data Analyt Chennai Tamil Nadu India

ISBN: (纸本)9781665411202

Diabetics Foot Ulcer is a common difficulty of the diabetics which is shaped due to skin tissue breaking down. Given the complicated domain shift relationship in images, the accurate prediction is the challenging task for generalization. Most of the past studies are not efficient enough to handle the domain shift issue. The singular goal of this research to create an accurate and robust model to predict the Foot Ulcer from the medical images which is a crucial step in improving the Diabetic patient's care. This research will discover the Diabetes Foot Ulcer from the trained model. Using the Deep learning algorithm, such as Convolution Neural Network to automate the foot ulcer detection process and improving the prediction accuracy.

关键词： Digital image processing Deep Learning Diabetic Foot Ulcer Prediction computer vision Machine Learning

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Corrosion Recognition of Transmission Line Armour Clamp Based on computer vision

Corrosion Recognition of Transmission Line Armour Clamp Base...

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Intelligent Perception and computer vision (CIPCV), international conference on

作者： Xu Bo Wei Yijun Maintenance Branch of State Grid Jiangxi Electric Power Company Nanchang China

ISBN: (数字)9798350360097

ISBN: (纸本)9798350360103

Due to the complex environment and special corrosion reasons, the metal parts such as insulation fasteners and fittings of transmission lines have significant differences in the location and shape of corrosion, making it very difficult to detect. With the gradual application of autonomous inspection drones in line detection, it is possible to use machine vision to identify the corrosion situation of Peking Opera. In response to the difficulty of image recognition detection in auxiliary and food detection, this paper proposes an algorithm that combines object detection with collar features to improve the applicability and efficiency of digital image processing in metal external characteristic detection. The main methods include deep learning and corrosion detection. The experimental results show that the comprehensive algorithm can achieve good recognition effect and high recognition accuracy for common metal corrosion features in the text. The combination of deep learning and color features has greatly improved the detection of complex metal corrosion points and the recognition of corrosion degree, providing technical ideas for the daily management and maintenance of related metals in the electrical industry

关键词： Deep learning Training Power transmission lines Text recognition Target recognition image color analysis Corrosion

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