Del 2476 i serien Communications in Computer and Information Science

Computer Vision and Image Processing

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

Häftad, Engelska, 2025

AvJagadeesh Kakarla,R. Balasubramanian,Subrahmanyam Murala,Santosh Kumar Vipparthi,Deep Gupta

2 229 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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

Utgivningsdatum2025-07-24
Mått155 x 235 x 27 mm
Vikt744 g
FormatHäftad
SpråkEngelska
SerieCommunications in Computer and Information Science
Antal sidor470
FörlagSpringer International Publishing AG
ISBN9783031936968

Tillhör följande kategorier

.- Analysis of Chromatic Response of EEG Signals Induced for Primary Color Visualization..- ReConfNET: Confidence-Driven Hierarchical Indoor Scene Reconstruction..- On Tree Mango Fruit Yield Estimation using Graph Cuts and Depth First Search..- Leveraging GANs for Chest X-ray Image Synthesis..- Early Violence Recognition using Knowledge Distillation..- MiXceptionLeaf: A Novel Model for Cassava Leaf Disease Classification Using XceptionNet and ConvMixer Layer..- Optimizing Image Captioning using BLIP Framework with Advanced Processing of DWT LL Band-Compressed Images..- Robust Denoising in Fringe Projection Profilometry: A Poissonian-Gaussian Approachwith Lightweight Neural Networks..- A retinex driven fractional-order regularization model for despeckling and enhancing of Synthetic Aperture Radar images..- Customized Self-Configuring U-Net Framework for Anatomical Lesion Segmentation in Post-Stroke Brain MRI..- Plant Leaf Diseases classification using Knowledge Distillation Methodologies..- TDIUC-AVQA: A Visual Question Answering Dataset in Low-Resource Assamese Language..- Joint compression of multi-model systems for edge devices..- VLRASN-112: A Visual Lip Reading Dataset for Assamese Compound Numeric Sequence Recognition..- Multimodal Deep Learning Framework for Skin Lesion Classification..- Enhancing Diagnostic Precision: AI-Based Differentiation of Brain Tumors and Multiple Sclerosis..- CubeMin: Fingerprint Indexing Scheme using Pairwise Minutia Feature in Cubic Space..- Unconstrained Low-Resolution Face Recognition using Attention Network and Resolution Aware Images..- An Instance-Aware Attention Vision Transformer for Glaucoma Detection using Retinal Fundus Image Analysis..- Improved Image data augmentation using Dynamic Mode Decomposition..- Assessment of the Impact of D-Adaptation on Convolutional Image Classification..- An Efficient Hybrid Feature Selection Approach for Offline Handwritten Mathematical Symbol Recognition..- Single Image Super-Resolution: Use of Transformer with Multiple Attention modules..- MSPlantNet: Plant Disease Classification using Few Shot Learning..- PATROL’S PAL: Anomaly Face Recognition from Low-Quality CCTV Footage..- Skeleton-Based Action Recognition - Dual Stream Learning of Discriminative Representations..- Leveraging Biogeography-based Optimization for Band Selection in Hyperspectral Brain Imaging: a CNN-based Classification Framework..- RWAEFA: Random Walk-based Artificial Electric Field Optimization Algorithm - An Application towards Feature Selection for Cytology Image Classification..- Super-resolution Enhanced Tree Classification in Satellite Images Using Convolutional Neural Networks..- Prediction of Short-Term Traffic Flow Using a Hybrid Architecture..- Enhanced Semantic-Driven Anomaly Detection: A Multi-Class Strategy for Video Data..- Activity Recognition in Smart Homes with Knowledge Graph and Attention-Guided Learning.