Computer Vision and Image Processing

9th International Conference, CVIP 2024, Chennai, India, December 19–21, 2024, Revised Selected Papers, Part II

AvJagadeesh Kakarla,R. Balasubramanian

Häftad, Engelska, 2025

Del 2474 i serien Communications in Computer and Information Science

1 791 kr

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Beskrivning

The Six-volume proceedings set CCIS 2473 and 2478 constitutes the refereed proceedings of the 9th International Conference on Computer Vision and Image Processing, CVIP 2024, held in Chennai, India, during December 19–21, 2024.The 178 full papers presented were carefully reviewed and selected from 647 submissions.The papers focus on various important and emerging topics in image processing, computer vision applications, deep learning, and machine learning techniques in the domain.

Produktinformation

Utgivningsdatum:2025-07-21
Mått:155 x 235 x 30 mm
Vikt:718 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:462
Förlag:Springer International Publishing AG
ISBN:9783031936906

Utforska kategorier

Innehållsförteckning

.- Multimodal Vision-based Hand Rehabilitation and Performance Visualization..- Pose-Invariant 2D Face Verification by combining MICA and 2D Features..- Robust Pedestrian Detection Via Enriched Dataset..-AutoTagGen: A Semantic Approach for Image Tagging utilising Large Language Models and Community Verified Integrative Knowledge..- Localizing Open World Objects Using Self-Learned Latent Keypoints..- A Horizontal based Federated Learning approach for non-IID Data distributionin Surgical Instrument Segmentation from MIS..- Classification of Wireless Capsule Endoscopy Abnormalities with Explainable AI..- Effect of network sparsity in classifier performance in ASD patients: A multicenter study based on centrality measures of rsfMRI..- Printed OCR for Extremely Low-resource Indic Languages..- SkyGuard: Semi-Supervised Drone Technology for Real-time Traffic Rule Enforcement..- Disp R-CNN for Personalized Care: A Deep Learning Approach to Fall Detection and Activity Recognition in AAL Systems..-A Least Squares Generative Adversarial Neatwork with Multi-Scale Residual Block for Single Image Super-Resolution..-Psy-H-Phy: ConvGNN-based Psychological Health Monitoring using PhysiologicalCues..- Advancing Diabetic Retinopathy Diagnosis: The Synergy of Deep Learning Models in DR-PanopticNet System..- Towards Safer Roads: Utilizing Synthetic Data and Neural Networks to Classify Safe Distances in Driving Scenarios..- NightSight: Low-Light Enhancement and De-blurring..- HyP-ECA: An Attention for Aerial Tree Crown Delineation and Species Classification..- Leveraging Internal Representations of Model for Magnetic Image Classification..- Enhancing Bread Spoilage Detection Through Ensemble Machine Learning Approaches..- Economy and Explainability with Non-Separable Wavelet-based CNN and Generative Modelling..- Comparative analysis for SAM, FastSAM, EfficientSAM, Detectron2 for Semantic Segmentation in Self driving cars..- A Deep Learning Approach to Pneumonia Prediction through X-Ray Image Analysis..- Efficient Deep Learning Framework for Glaucoma Detection in Color Fundus Image..- Speckle Noise Suppression in OCT Images via Real Image Denoising Network..- Assessing YOLOv8 and YOLOv9 for advancements in underwater object detection in the pond environment..- Enhanced Traffic Sign Detection and Classification Using YOLOv8x & SEResNet-101x Model: A Deep Learning Approach..- Grasp Pattern Recognition Using Convolutional Vision Transformer..- Attire in Attention: Enhancing CCTV Surveillance with cloth-based image retrieval..- AI Powered Video Content Moderation governed by Intensity Based Custom Rules with Remedial Pipelines..- Unveiling Alzheimer's Risk: Leveraging Deep Learning and Attention Mechanisms for Early Detection..- Enhancing Stone Inscription Clarity: A Hybrid Approach for Denoising in Digital StonePaleography..- EfficientNet-Driven Framework for Precise Detection and Grading of Liver and Colon Cancer using H&E Stained Histopathological Images..- Brain Tumor Segmentation with Federated Learning: A BraTS2020 Dataset.

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