Conference Information
ICCPR 2023: International Conference on Computing and Pattern Recognition
Submission Date:
2023-09-14 Extended
Notification Date:
Conference Date:
Qingdao, China
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Call For Papers
International Conference on Computing and Pattern Recognition (ICCPR) series held annually to provide a common platform for the cross fertilization of ideas, and for shaping knowledge and scientific achievements by bridging Computing and Pattern Recognition these two important and complementary disciplines into an interactive and attractive forum. Keeping this objective in mind, ICCPR solicits original contributions in the following non exclusive lists of areas:

Computer Vision and Robot Vision

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 	

Document Analysis, Biometrics and Pattern Recognition 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
Forensic biometrics and applications
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

Pattern Recognition and Machine Learning

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
Deep learning architectures & techniques 	

Image, Speech, Signal and Video Processing

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

Biomedical Image Analysis and Applications

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
Last updated by Dunn Carl in 2023-09-06
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