International Conference on Image Processing and Artificial Intelligence (IPAI 2025)

Topics

• Pattern Recognition and Machine Learning
  • Machine learning and data mining
  • Statistical and structural pattern recognition
  • Dimensionality reduction and manifold learning
  • Artificial neural networks
  • Symbolic learning
  • Classification and clustering
  • Deep learning
  • Active and ensemble learning
  • Support vector machine
  • Machine learning
  • Bayesian learning
  • Neural networks
  • Reinforcement learning and temporal models
  • Transfer learning
  • Semi-supervised learning and spectral methods
• Image, Speech, Signal and Video Processing
  • Image and video analysis and understanding
  • Signal, image and video processing
  • Segmentation, features and descriptors
  • Audio and acoustic processing and analysis
  • Automatic speech and speaker recognition
  • Texture and color analysis
  • Vision sensors
  • Multimedia analysis, indexing and retrieval
  • Computer-aided detection and diagnosis
  • Medical image and signal analysis
  • Face recognition
  • Image guidance and robot guidance of interventions
  • Graph theory in image processing
  • Image compression, coding, and encryption
  • Natural language processing
• Data Science and Artificial
  • Knowledge Engineering
  • Intelligence Application
  • Intelligent agent
  • Computational intelligence
  • Knowledge management
  • Intelligent control
  • Big data Data mining
  • Artificial Immune System
  • Human Computer Interaction
  • Distributed and parallel learning
  • Intelligent Systems
  • Fuzzy Systems and Control
  • Graphics algorithms
  • Soft Computing
• Computer Vision
  • Biologically motivated vision
  • Vision sensors
  • Image based modeling
  • Illumination and reflectance modeling
  • Perceptual organization
  • Physics-based vision
  • 3D shape recovery
  • Computational photography
  • Cognitive and embodied vision
  • 2D/3D object detection and recognition
  • Vision for graphics
  • Occlusion and shadow detection
  • Scene understanding