Call for Contributions

AIPR conference is intended to foster the dissemination of state-of-the-art research in the area of Artificial Intelligence & Pattern Recognition and the fundamental interaction between them. Authors are invited to submit regular research papers on all related areas. Papers exploring new directions or areas will receive a thorough and encouraging review. Areas of interest include, but not limited to :

 

Pattern Recognition and Machine Learning Computer Vision and Robot Vision Image, Speech, Signal and Video Processing
· Statistical, syntactic and structural pattern recognition
· Machine learning and data mining
· Artificial neural networks
· Dimensionality reduction and manifold learning
· Classification and clustering
· Graphical Models for Pattern Recognition
· Representation and analysis in pixel/voxel images
· Support vector machines and kernel methods
· Symbolic learning
· Active and ensemble learning
· Deep learning
· Pattern recognition for big data
· Transfer learning
· Semi-supervised learning and spectral methods
· Model selection
· Reinforcement learning and temporal models
· Performance Evaluation		 · Vision sensors
· Early/low-level vision
· Biologically motivated vision
· Illumination and reflectance modeling
· Image based modeling
· Physics-based vision
· Perceptual organization
· Shape modeling and encoding
· Computational photography
· 3D shape recovery
· Motion, tracking and video analysis
· 3D sensors: depth sensors, ToF, Kinect
· 2D/3D object detection and recognition
· Activity and event analysis
· Scene understanding
· Occlusion and shadow detection
· Stereo and multiple view geometry
· Reconstruction and camera motion estimation
· Vision for graphics
· Deep learning
· Vision for robotics
· Cognitive and embodied visión
· Humanoid vision		 · Image, Speech, Signal and Video Processing
· Image and video analysis and understanding
· Sensor array & multichannel signal processing
· Segmentation, features and descriptors
· Texture and color analysis
· Enhancement, restoration and filtering
· Coding, compression and super-resolution
· Facial expression recognition
· Affective computing
· Human computer interaction
· Human body motion and gesture based interaction
· Audio and acoustic processing and analysis
· Automatic speech and speaker recognition
· Spoken language processing
· Speech and natural language based interaction
· Group interaction: analysis of verbal and non-verbal communication
· Multimedia analysis, indexing and retrieval
· Depth & range sensor data processing and analysis
Document Analysis, Biometrics and Pattern Recognition Applications Biomedical Image Analysis and Applications  
· Character and Text Recognition
· Handwriting Recognition
· Graphics Recognition
· Document Understanding
· Gesture and Behavior Analysis
· Mixed and Augmented Reality
· Face, fingerprint and iris recognition
· Other biometrics (gait, soft, speaker, periocular, etc.)
· Novel biometrics
· Biometric systems and applications
· Multi-biometrics
· Forensic biometrics and applications
· Bioinformatics
· Surveillance and Security
· Search, Retrieval and Visualization
· Art, Cultural Heritage and Entertainment
· Industrial image analysis
· Human computer interaction
· Analysis of humans
· Pattern and digital evidence
· Performance analysis and enhancement
· Applications of pattern recognition to big data		 · Medical image and signal analysis
· Biological image and signal analysis
· Modeling, simulation and visualization
· Computer-aided detection and diagnosis
· Image guidance and robot guidance of interventions
· Content based image retrieval and data mining
· Medical and biological imaging
· Segmentation of biomedical images
· Molecular and cellular image analysis
· Volumetric image analysis
· Deformable object tracking and registration
· Computational anatomy and digital human
· VR/AR in medical education, diagnosis and surgery
· Medical robotics
· Imaging and hardware for health care
· Brain-computer interfaces
· Data mining for biological databases
· Algorithms for molecular biology
· Deep learning for biomedical image analysis