A wide range of topics in the image understanding area is covered but not limited to the following:
ICIPVS-2024 invites submissions in different areas of research but not limited to:
Image Enhancement
Image Restoration
Image Segmentation
Image Compression
Image Fusion
Super-resolution Imaging
Noise Reduction Techniques
Morphological Image Processing
Color Image Processing
Multi-scale and Multi-resolution Techniques
Object Detection and Recognition
Object Tracking
3D Vision
Feature Extraction and Matching
Scene Understanding
Visual Navigation
Video Analysis and Understanding
Face Recognition and Analysis
Gesture Recognition
Computational Photography
Medical Image Segmentation
Image Registration
Computer-Aided Diagnosis
Radiomics and Radiogenomics
Image-Based Biomarkers
Surgical Navigation
Deep Learning in Medical Imaging
Quantitative Imaging
Image-Based Treatment Planning
Satellite Image Analysis
Hyperspectral Image Processing
Synthetic Aperture Radar (SAR) Imaging
Environmental Monitoring
Land Cover Classification
Change Detection
Urban Remote Sensing
Agriculture and Forestry Applications
Oceanographic Image Analysis
Disaster Management and Response
Visual Perception for Robots
Object Manipulation and Grasping
Visual SLAM (Simultaneous Localization and Mapping)
Vision-Based Navigation
Human-Robot Interaction
Scene Understanding for Robotics
Robot Vision Systems
Vision-Based Object Tracking for Robots
Autonomous Vehicle Vision Systems
Vision-Based Robot Learning
Markerless Tracking
Augmented Reality Applications
User Interface Design for AR
AR in Healthcare and Medicine
AR in Education and Training
Indoor Navigation using AR
Mobile AR Technologies
AR Content Creation and Authoring Tools
Social and Collaborative AR
AR Visualization Techniques
Visual Psychophysics
Visual Attention and Eye Tracking
Color Perception
Depth Perception
Visual Illusions
Perceptual Organization
Visual Cognition
Visual Memory
Cognitive Neuroscience of Vision
Visual Disorders and Rehabilitation
Deep Learning for Image Analysis
Convolutional Neural Networks (CNNs)
Generative Adversarial Networks (GANs)
Transfer Learning in Vision Tasks
Reinforcement Learning for Vision
Semi-supervised and Unsupervised Learning
Interpretability and Explainability of ML Models
Meta-learning Approaches
Hybrid Models Combining ML and Traditional Vision Techniques
Ethical and Bias Considerations in ML for Vision