Kakarla / Balasubramanian / Murala | Computer Vision and Image Processing | Buch | 978-3-031-93708-8 | sack.de

Buch, Englisch, 478 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 756 g

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 V
Erscheinungsjahr 2025
ISBN: 978-3-031-93708-8
Verlag: Springer

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

Buch, Englisch, 478 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 756 g

Reihe: Communications in Computer and Information Science

ISBN: 978-3-031-93708-8
Verlag: Springer


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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Zielgruppe


Research

Weitere Infos & Material


.- TranscoderGAN: Transformer-Inception based Encoder-Decoder Generative Adversarial Network for Thermal-Visible Face Transformation.

.- PCNet3D: A Pillar based Cascaded 3D Object Detection Model using LiDAR Point Cloud.

.- Leveraging English Captions for Improved Image Captioning in Low-Resource Languages.

.- Plant-AT: Customized Hybrid Attention Model for Diagnosing Plant Disease.

.- FL-STGCN: Mental Health Recognition using RGB and IR Landmark Feature Fusion.

.- An Object Translation-based Image Augmentation-enabled Cyclone Eye Detection System for Tropical Cyclones in India.

.- Automatic Monitoring of Crops from Drone Images with a Suitable Deep Learning Model.

.- TransDerain: Efficient Transformer Network for Video Deraining.

.- Deep Compressive Sensing for High-Quality Video Recovery.

.- Transforming Single Photon Camera Images to Color High Dynamic Range Images.

.- Breast Cancer Detection using Thresholded Wavelet Transformation and Transfer Learning.

.- Stage Estimation in Parkinson’s Disease from MRI and Clinical Assessment Data with a Multivariate Analysis.

.- Enhanced Image Feature Selection Techniques for Visualization and Classification of Android Malware.

.- End-to-End Prostate Cancer Segmentation for RT Planning.

.- Bangla Chitra Net: An efficient transformer-based architecture for image captioning.

.- Adaptive Quantization of Deep Neural Networks via Layer Importance Estimation.

.- Paired Attention Swin UNETR for Volumetric Segmentation of Brain Tumor.

.- Enhancing Maritime Multi Object Tracking with Meta-data Assisted Re-Identification.

.- Advanced Fingerprint Authentication System Integrating HQC and AES Encryption for Post-Quantum Security.

.- MyoCI: Computational Intelligence for Early Detection of Myocardial Infarction using Text Analysis through Clinical Data.

.- Domain Adaptation for Image Classification: A Comparative Study of Semi-Supervised versus Unsupervised Approaches.

.- WAVE-NET: Weakly-Supervised Video Anomaly Detection through Feature Enhancement and triplet loss.

.- GAN-driven Brain Tumor Segmentation with Attention Residual U-Net.

.- Efficient Masked Face Recognition in Low-Resolution Images with MobileNet and Attention Mechanism.

.- CompTSRTrans: Computationally Efficient Thermal Image Super-Resolution based on Transformer.

.- Medical Image Restoration based on Split Bregman with DIP-TV.

.- Hand Detection in the Wild Leveraging RetinaNet.

.- Learnable Gabor and Wavelet Filters Based Deep Learning for Image Forgery Detection.

.- Manifold Embedding for Pseudo-centric Domain Adaptation Approaches.

.- Fooling Face Recognition Systems through Physical Adversarial Attack.

.- Enhanced Eye Disease Diagnosis using Integrated ResNet-101 and Vision Transformer.

.- Leveraging CNN Features and Vision Transformers for Enhanced Focal Liver Lesion
Classification.

.- OSCMamba: Omni-directional Selective Scan Convolution Mamba for Medical Image
Classification.



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