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Kakarla / Balasubramanian / Murala | Computer Vision and Image Processing | E-Book | sack.de
E-Book

E-Book, Englisch, 470 Seiten

Reihe: Communications in Computer and Information Science

Kakarla / Balasubramanian / Murala Computer Vision and Image Processing

9th International Conference, CVIP 2024, Chennai, India, December 19–21, 2024, Revised Selected Papers, Part III
Erscheinungsjahr 2025
ISBN: 978-3-031-93694-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

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

E-Book, Englisch, 470 Seiten

Reihe: Communications in Computer and Information Science

ISBN: 978-3-031-93694-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark



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.

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Research

Weitere Infos & Material


.- ConvSFNet: A Novel Architecture for Disaster Image Analysis.
.- Impact of Color Channel Perturbation Attack on Vision Transformers.
.- Boundary Refinement in Abdomen Segmentation using 3D Inverseform Loss.
.- Enhancing Hyperspectral Remote Sensing Image Classification by the Fusion of Transfer Learning Models for Crop Classification.
.- Advanced Lung Nodule Segmentation for Early Detection of Lung Cancer Using SAM and Transfer Learning.
.- On OpenAI Vision’s Capability to Detect Common Vision Disorders.
.- Squeezing Global Local Attention Within Siamese Network for Robust Palm Vein Recognition.
.- Balancing Plasticity with Enhanced Stability: A Task-Incremental Learning Approach 
for Image Classification.
.- Predicting ADHD: Combining Data-Driven ROI Extraction and Composite Graph Spectral Analysis.
.- HYPC-Net: A Hybrid Yoga Pose Classification Network.
.- Fuzzyfake: Copyright protection of deep fake images using FIS optimization.
.- Explainable AI and Deep Learning for Brain Tumor Classification: A Comprehensive Approach Using Grad-CAM Visualization.
.- Coding Performance Analysis of Deep Rain Streaks Removal Convolutional Neural Network Based In-loop Filtering for High Efficiency Video Coding.
.- Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Model.
.- Data Conditioning to Improve Quality of Segmentation of Brain MRI Using Deep CNN Models.
.- Designing a Lightweight Network for Object Detection in Drone-Based Surveillance.
.- Advancing Question Answering on Handwritten Documents.
.- Object Detection through Finger Count using Deep Learning Techniques for HRI.
.- Fish92: A Novel Dataset for Indigenous Fish Classification.
.- Attention-based Detection of Night-time Traffic Accidents.
.- LDCrowdNet: A Lightweight Network for Dense Crowd Counting.
.- SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network.
.- Beyond the General Pose: An Optimized Yoga Posture Similarity Assessment Method With PCA-Driven Feature Selection.
.- UBI-DET: Towards a Unified Framework for Open-Set Fine-Grained Object Detection.
.- Face Recognition in Selfie Images using Vision Transformer Variants.
.- Converting Gujarati text in custom-embedded subsetted non-Unicode fonts to searchable formats: A case study using Jain religious texts.
.- Towards Digitizing Filled Indic Handwritten Forms.
.- Benchmarking YOLO object detectors for component detection in power line infrastructure: Dataset and Results.
.- Visual Document Understanding: A Comparative Review of Modern Methods.
.- On Application of Prufer Codes for Grid-free Lock Pattern Understanding.
.- Swin Transformer for Robust Differentiation of Real and Synthetic Images: Intra- and Inter-Dataset Analysis.
.- Quantum-Circuit Inspired Hybrid QCNN for UAV Based Crop Classification.



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