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

共 1670 条记录，以下是371-380 订阅

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Advancing Deep Learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI Based computer vision Applications

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sudhanshu Shekhar Sathwik T S Mayank Pritwani Mohana Ramakanth Kumar P Sreelakshmi K Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India Computer Science and Engineering RV College of Engineering® Bengaluru India Electronics and Teleommunication Engineering RV College of Engineering® Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NVIDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications.

关键词： Deep learning Performance evaluation computer vision Accuracy Computational modeling image edge detection Object detection Real-time systems Nanoscale devices Artificial intelligence

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Performance Analysis of image Caption Generation Using Deep Learning Techniques 3rd

Performance Analysis of Image Caption Generation Using Deep ...

引用

3rd international conference on Microelectronic Devices, Circuits and Systems, ICMDCS 2022

作者： Patil, Shweta S. Varma, B. Srinivasa Devadasu, G. Basha, C. H. Hussaian Inamdar, M.J.R. Salman, S.S. Department of E&TC Nanasaheb Mahadik College of Engineering Walwa India Hyderabad India Bengaluru India

ISBN: (纸本)9783031239724

In recent years, Artificial Intelligent (AI) is rapidly developed. The image caption is attracting the attention of many scientists. It is very interesting work. Automatically generating image caption into the natural language description according to the objects observed in the image. This is the part of scene understanding. Since, understanding is the combination of computer vision and natural language processing. The text is processed by using Recurrent Neural Network (RNN) with clubbed single dimensional feature obtained from Convolutional Neural Network (CNN) model. Given the data is given to trained model and last output is predicted. For the experiment of image captioning Flickr8k dataset is used. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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A Robust Watermarking Scheme for Copyright Protection 3rd

A Robust Watermarking Scheme for Copyright Protection

引用

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

作者： Singh, Satendra Pal Bhatnagar, Gaurav Indian Inst Technol Karwar Dept Math Karwar India

ISBN: (纸本)9789813292918;9789813292901

In this paper, a robust image watermarking scheme based on all phase sine biorthogonal transform (APSBT), singular value decomposition (SVD) and dynamic stochastic resonance (DSR) is presented. Firstly, the cover image is transformed by APSBT and then a gray-scale logo is embedded through the singular value decomposition. After the authentication process which essentially resolves the false-positive extraction of SVD in watermarking, a phenomenon based on dynamic stochastic resonance is deployed for the logo extraction from the watermarked image. The simulation results demonstrate that the proposed scheme has better performance in the aspect of robustness and invisibility.

关键词： image watermarking Singular value decomposition Dynamic stochastic resonance (DSR) All phase sine biorthogonal transform (APSBT)

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computer vision assisted Deep Learning based Sitting Posture Suggestion System

Computer Vision assisted Deep Learning based Sitting Posture...

引用

Innovation in Technology (INOCON), IEEE international conference for

作者： Ghanashyam Vagale GK Prajwal Rohit Kumar P. Padma Priya Dharishini Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user’s posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user’s daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm’s efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals.

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Detection of Down Syndrome Using Deep Facial Recognition 3rd

Detection of Down Syndrome Using Deep Facial Recognition

引用

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

作者： Mittal, Ankush Gaur, Hardik Mishra, Manish Graph Era Deemed Univ Dehra Dun Uttarakhand India

ISBN: (纸本)9789813290884;9789813290877

Down syndrome is a genetic disorder that affects 1 in every 1000 babies born worldwide. The cases of Down syndrome have increased in the past decade. It has been observed that humans with Down syndrome generally tend to have distinct facial features. This paper proposes a model to identify people suffering from Down syndrome based on their facial features. Deep representation from different parts of the face is extracted and combined with the aid of Deep Convolutional Neural Networks. The combined representations are then classified using a Random Forest-based pipeline. The model was tested on a dataset of over 800 individuals suffering from Down syndrome and was able to achieve a recognition rate of 98.47%.

关键词： KNN CNN HOG Machine learning computer vision Random forest Healthcare Down syndrome

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MSDB-based CNN architecture for image dehazing in driverless cars

MSDB-based CNN architecture for image dehazing in driverless...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Qihang Wu Junyin Liu Mengchao Feng Heriot-Watt University Edinburgh Scotland

With the booming development of image processing technology and computer vision technology, scene detection and image processing in special weather has become an important research direction in this field. Among them, images taken in foggy days are easily affected by fog or haze, resulting in blurred details and low contrast to the loss of important image information, and to solve such problems image defogging algorithms are born. To address these challenges, a lightweight convolutional neural network based on multi-scale dense connectivity, called MSDL, is proposed in this paper for reconstructing blurred images. DehazeNet is the End-to-End defogging system that takes the fogged image as input, its transmission map as output, and then uses an atmospheric scattering model for image reduction. The proposed MSDL uses the transformed atmospheric scattering model to jointly estimate the transmission map and atmospheric light. In addition, a novel feature extraction module MSDB is proposed. Finally, extensive experiments are carried out using synthetic and natural hazy images. The experimental results show superiority over both non-deep learning and deep learning methods in both qualitative and quantitative evaluation. The PSNR, SSIM and MSE metrics were measured on different datasets, and the advantages were obtained on NYU2 dataset.

关键词： computer vision Atmospheric measurements Atmospheric modeling Scattering Transportation computer architecture Autonomous automobiles

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Research On an image Dehazing Algorithm Based On MATLAB

Research On an Image Dehazing Algorithm Based On MATLAB

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Jingxuan Zhang Renkang Li School of Physics and Electronic Information Yunnan Normal University Kunming China

With the rapid development of technology, computer technology is gradually maturing. In the computer vision field, image processing technology in specific weather has become an important research field. For those photos taken in foggy weather, the mist will affect natural light leading to a certain impact on the visual effect making pictures seem unclear. Additionally, the useful information in the pictures will decrease. To solve the image blurring problem caused by natural weather such as haze, this paper adopts the histogram equalization method. By averaging the gray histogram of the image, the blurred areas are magnified and dispersed, and a clear state is visually presented. The retinex algorithm is used to process the histogram equalization result image to enhance the useful image information and further process the result details to defog the image and obtain the final defogged image.

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ABID: Attention-Based Bengali image Description 3rd

ABID: Attention-Based Bengali Image Description

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3rd international conference on Communication, Devices and Computing, ICCDC 2021

作者： Das, Bidyut Sekh, Arif Ahmed Majumder, Mukta Phadikar, Santanu Department of Information Technology Haldia Institute of Technology Haldia India School of Computer Science and Engineering XIM University Bhubaneswar India Department of Computer Science and Application University of North Bengal Darjeeling India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal India

ISBN: (纸本)9789811691539

image caption or description generation is a fundamental problem of artificial intelligence. It requires both knowledge, natural language processing, and computer vision together. It automatically produces description(s) from an image. The Bengali language is the fifth language spoken widely in the world. It is considered in many areas of research and development. Various established datasets are available in the literature for image caption generation in English since no such standard dataset exists for Bengali. This paper proposes a model for automatic image captioning in the Bengali language. This study uses a Bengali dataset to train the encoder–decoder neural network model. The proposed model trained with two descriptions per image and achieved a 0.67 BLEU-1 score. We expect that a far better result is obtained with a larger and more diverse dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Research on Text Detection Algorithm for Work Ticket images Based on Feature Enhancement and Multi Feature Fusion 3

Research on Text Detection Algorithm for Work Ticket Images ...

引用

3rd international conference on image processing, computer vision and Machine Learning, ICICML 2024

作者： Xu, Qing Zhang, Haiduo Jian, Tian Chen, Yujian Gu, Hui State Grid Jilin Electric Power Company Limited Industry Department Changchun China State Grid Jilin Power Supply Company Changchun China State Grid Jilin Electric Power Company Limited Safety Supervision Department Changchun China

ISBN: (纸本)9798350355413

The images of construction work receipts are filled with various noises, such as stains, scratches, handwritten handwriting, and seal images, which result in poor performance of traditional text detection methods. This article proposes a text detection method based on efficient feature enhancement and multi-scale feature fusion to address this issue. By integrating an efficient segmentation module and a learnable post-processing method, the performance of the detection method is enhanced by using the visual language pre training model oCLIP. The segmentation module adopts image segmentation method for text detection of construction work ticket images, including an efficient feature enhancement module and a multi-scale feature fusion, which together constitute the key components of the segmentation module. Experimental results have shown that the model achieves good text detection performance in low resolution and high noise work order images. © 2024 IEEE.

关键词： image segmentation

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image Dehazing using Dark Channel Prior 3

Image Dehazing using Dark Channel Prior

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Chowdari, Ch. Pratyusha Kashyap, V Dheeraj Kumar, K Kiran Charan, M Raghu Akhil, B. Griet Dept.ECE Hyderabad India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Windstorms, foggy winters, and sand storms generally degrade image quality and have an impact on computer vision applications, which could be a safety concern for drivers because of light dispersing and retention by dust remnant or air molecules, making the view hazy or foggy. Our method relies on the Dark Channel Prior (DCP) methodology, which improves visual contrast too. Upon quantifying the ambient light, the value is utilized for calculating transmission estimate. The transmission map is fine-tuned afterwards, and even the image is recovered. By processing locally, we simplify the refining stage and localize the refinement zone by working with the coarse dark channel. This strategy will reduce blurriness while restoring the true brilliance of distant objects. To compare we have chosen different existing methods like All-in-One Dehazing Network (AOD-net), Convolutional Neural Network (CNN), High Resolution de-hazer(HR de-hazer) and make the comparison using metrics like Peak Signal to Noise Ratio(PSNR) and Structural Similarity Index (SSIM). Not only in terms of quality parameters but we have also checked over the latency period where we found DCP is giving a valuable output. Hence, the Peak Signal to Noise Ratio value of our proposed method is 20% greater than that of existing methods. © 2022 IEEE.

关键词： Signal to noise ratio

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