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8th International conference on Computing in Engineering and Technology, ICCET 2023

作者： Reddy, K. Sivanagi Jyothika, B. Shravya, R. AathmajaSai, G. Department Electronics and Communication Engineering India Sridevi Women's Engineering College Telangana India

ISBN: (纸本)9781839539237

Currency fraud now possess a serious risk to both people's livelihoods and the nation's economy. Due to improvements, in printing and scanning technology, currency duplication is still a growing worry for governments. Although banks have many note-detecting systems;but are expensive and frequently wrong. This problem has the potential to be considerably solved by the disciplines of machine vision, neural networks, and image processing. Although there are fake cash detectors, they are only used in banks and corporate offices, leaving the general public and small companies at risk. In order To detect and invalidate fraudulent indian currency, we will develop a software-based system in this project using cutting-edge image processing and computer vision techniques. First, we will analyze the many security characteristics of indian currency. This currency authentication system was created entirely in the Jupyter Notebook environment using the Python programming language. © The Institution of Engineering & Technology 2023.

关键词： computer vision

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8th International conference on computer vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on computer vision and image processing, CVIP 2023

ISBN: (纸本)9783031581809

The proceedings contain 140 papers. The special focus in this conference is on computer vision and image processing. The topics include: A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity;MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement;feature Fusion and Multi-head Attention Based Hindi Captioner;Payload Length and Location Identification Using a Novel CNN and Re-embedding Strategy in Stego images Created by Content Adaptive Steganographic Algorithms;ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions;driver Drowsiness Detection Using vision Transformer;automated Cricket Commentary Generation for Videos;image Captioning with Visual Positional Embedding and Bi-linear Pooling;Cartilage Segmentation from MRI images Towards Prediction of Osteoarthritis;Reinforcement Algorithm-Guided ROI Extraction of Fingerprint Biometric Data;improved Metric Space for Shape Correspondence;an Integrated Approach: Combining GrabCut and Contour-Matching for Hand Gesture Segmentation in indian Sign Language;identity Preserved Expressive Talking Faces with Synchrony;isolated Sign Language Recognition Using Deep Learning;a Gradient-Based Approach to Interpreting Visual Encoding Models;adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation;Efficient Seizure Prediction from images of EEG Signals Using Convolutional Neural Network;coping with Increased Levels of Label Noise in Facial Expression Recognition;experimental Evaluation of Needle Tip Prediction Using Kalman Filtering Approach;a Deep Face Antispoofing System with Hardware Implementation for Real-Time Applications;Bit Plane Segmentation and LBP-Based Coverless Video Steganography for Secure Data Transmission;a Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear images Using

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8th International conference on computer vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on computer vision and image processing, CVIP 2023

ISBN: (纸本)9783031581731

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8th International conference on computer vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on computer vision and image processing, CVIP 2023

ISBN: (纸本)9783031585340

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Automated Denoising of Diabetic Retinopathy images for Enhanced Medical Diagnosis 3

Automated Denoising of Diabetic Retinopathy Images for Enhan...

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3rd International conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Suresh, G.V. Sri, Gokavarapu Pavani Naga Pranathi Angidi, Krishna Sai Dheeraj, Tumma Sai Snehitha, Goda Naga Venkata Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350375190

Globally, diabetic retinopathy (DR) continues to be a major cause of vision impairment. Early diagnosis and detection of DR are essential to preventing blindness and vision loss. But the traditional manual method of screening retinal pictures takes a lot of time and is prone to human mistake. Using image processing to apply automated denoising techniques can increase image quality, make DR lesions more visible, and help with correct diagnosis. In order to improve the quality of images of diabetic retinopathy, this study investigates the ability of four image denoising filters: guided image filtering, Gaussian-bilateral filtering, Gabor filtering, and Haar Wavelet filtering. AGAR300 (IEEE DataPort), Diabetic Retinopathy Detection, Stare, indian Diabetic Retinopathy image Dataset, and APTOS 2019 Blindness Detection are the five datasets used to evaluate the efficacy of these filters. The findings demonstrate that noise in DR images may be efficiently reduced by all four filters. The one that shows the biggest overall improvement in lesion visibility and image quality among them is guided image filtering. © 2024 IEEE.

关键词： Gabor filters

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Single View Homography Estimation for an Inclined Textured Planar Surface: Overcoming the Inverse and Ill-Posed Challenge! 14

Single View Homography Estimation for an Inclined Textured P...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Kumari, Pooja Das, Sukhendu Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9798400716256

Homography estimation is a crucial step in many computer vision problems involving the planar transformation of an image from one view to another. Generally, from two image views of a scene with a planar patch, one can compute the homography matrix (H) using Direct Linear Transformation (DLT) or its variants. In this study, we work on the inverse scenario to solve an ill-posed problem, where given only a single image (IτP) of an inclined textured planar surface, we attempt to estimate both H and the orthogonal view (IτO) of the planar surface. We propose to solve this problem of homography Estimation using an optimization framework. To the best of our knowledge, there has barely been any work done on this inverse scenario of homography estimation from only a single image view. The research presented in this paper successfully achieves image rectification and homography computation by assuming a perspective transformation on an upright planar patch when viewed by a camera placed exactly in front of it. A cost function with suitable constraints is utilized to refine homography estimation. Experimental results reveal the efficiency of the proposed approach in the case of images of checkerboard and other textured planar surfaces (real-world images with lines and other prominent textures). © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Textures

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DePondFi'23 Challenge on Real-Time Pond Environment: Methods and Results

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IEEE ACCESS 2024年 12卷 157975-157987页

作者： Sasithradevi, A. Suganya, R. Prakash, P. Mohamed Mansoor Roomi, S. Vijayalakshmi, M. Nathan, Sabari Kasthuri, P. Persiya, J. Brighty Ebenezer, L. Jain, Sparsh Verma, Sshubam Balasubramanian, S. Sai Subramaniam, M. Sai Sriram, T. Pranav Phanindra Sai, M. Raj, Chandan Yadav, Amandeep Payak, Ritik Paul Choudhury, Suman Singh, Rohit Vellore Inst Technol Ctr Adv Data Sci Chennai 600127 India Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India Indian Inst Technol Madras Dept Elect Engn Chennai 600044 India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai 625015 India Vellore Inst Technol Sch Elect Engn Chennai 600127 India Couger Inc Tokyo Japan Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi 110086 India Sri Sathya Sai Inst Higher Learning Dept Math & Comp Sci Anantapur 515134 Andhra Pradesh India IIT Roorkee Dept Elect Engn Roorkee 247667 India SAHAJ AI Bangalore 560034 India

This article summarizes the Detection of Pond Fish Challenge (DePondfi'23 Challenge), held during the National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG 2023). The main goal of the challenge was to find the most effective methods for detecting pond fish in underwater images, overcoming obstacles such as poor visibility, variations in turbidity, and environmental shifts. Sixty participants registered, with 15 teams submitting results for phase 1. The challenge concluded with four teams earning top honors based on mAP (mean Average Precision) score and time complexity. The mAP scores achieved by toppers are as follows: DETECTRON - 38.93%, DMACS SAI - 36.65%, Pondvision - 31.63%, and Sahajeevis - 29.06%. This article describes the toppers method and discusses the detection results. Our challenge event is in line with Sustainable Development Goal 14, which focuses on the conservation and sustainable utilization of ponds and marine resources for sustainable development.

关键词： Aquaculture underwater challenges detection models mAP computational time.

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ViLP: Knowledge Exploration using vision, Language, and Pose Embeddings for Video Action Recognition 14

ViLP: Knowledge Exploration using Vision, Language, and Pose...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Chaudhuri, Soumyabrata Bhattacharya, Saumik Indian Institute of Technology Bhubaneswar Odisha Bhubaneswar India Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

Video Action Recognition (VAR) is a challenging task due to its inherent complexities. Though different approaches have been explored in the literature, designing a unified framework to recognize a large number of human actions is still a challenging problem. Recently, Multi-Modal Learning (MML) has demonstrated promising results in this domain. In literature, 2D skeleton or pose modality has often been used for this task, either independently or in conjunction with the visual information (RGB modality) present in videos. However, the combination of pose, visual information, and text attributes has not been explored yet, though text and pose attributes independently have been proven to be effective in numerous computer vision tasks. In this paper, we present the first pose augmented vision-language model (VLM) for VAR. Notably, our scheme achieves an accuracy of 92.81% and 73.02% on two popular human video action recognition benchmark datasets, UCF-101 and HMDB-51, respectively, even without any video data pre-training, and an accuracy of 96.11% and 75.75% after kinetics pre-training. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Value engineering

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QAAD: Quality Aware Adaptive Denoising 11

QAAD: Quality Aware Adaptive Denoising

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11th IEEE International conference on Signal processing and Integrated Networks, SPIN 2024

作者： Parse, Tejas Ajay Awasthi, Tanishq Yadav, Dushyant Joshi, Piyush Indian Institute of Information Technology Department of Computer Science Sri City Chittoor India

ISBN: (纸本)9798350308433

image denoising is a fundamental task in computer vision and image processing, crucial for improving the visual quality and interpretability of images captured in noisy environments. In this research, we propose a quality-Aware adaptive denoising (QAAD) approach that leverages the Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) as a key factor in determining the optimal denoising algorithm for an image. Our methodology involves an initial assessment of image quality using BRISQUE scores for a diverse set of images. Subsequently, we employ various image quality enhancement algorithms to enhance the image quality. Through extensive experimentation, we assess the performance of these enhancement algorithms with respect to specific ranges of BRISQUE scores. This evaluation enables us to determine which denoising algorithm performs optimally for images falling within particular BRISQUE score ranges. The innovation in our approach lies in its adaptability. When an image proceeds for denoising, its BRISQUE score is calculated and, based on this score, the most suitable denoising algorithm for that specific image is selected. This adaptive strategy ensures that the chosen denoising algorithm is tailored to the inherent noise characteristics of the input image, leading to superior denoising results. Our experimental results demonstrate the effectiveness of our approach in enhancing image quality and achieving state-of-The-Art denoising results. This research contributes to the field of image processing by providing a data-driven and adaptive solution for image denoising, ultimately enhancing the quality of images in diverse real-world applications. © 2024 IEEE.

关键词： image denoising

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MCDNet: Multi Context Dense Network for multi-frame super resolution of satellite images 14

MCDNet: Multi Context Dense Network for multi-frame super re...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Chouhan, Avinash Motwani, Harsh Sur, Arijit Chutia, Dibyajyoti Aggarwal, Shiv Prasad North Eastern Space Applications Centre Meghalaya Umiam India Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9798400716256

Satellite image super resolution is an important task that generates high resolution satellite images from low resolution inputs. Multi-frame super resolution utilizes multiple low-resolution images to generate a single high-resolution image. Multi-frame super resolution methods face difficulty in handling spatial and temporal dependencies of pixels. In this work, we proposed a novel architecture named Multi-context Dense Network (MCDNet) to handle spatial and temporal pixel dependencies using multiple approaches of global average pooling, multiple size kernels, and self-attention. The proposed approach improved the PSNR values by 0.29 % and 0.001 % for super resolution of NIR and RED bands on the benchmark PROBA-V dataset. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Pixels

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