Topics of IPCML 2025 征稿主题
Original and high-quality papers are invited from the potential authors. The topics include, but are not limited to:
Image Processing Sensing, Representation, Modepng, and Registration Image Segmentation Image Denoising Image Filtering Image Restoration Image Enhancement Multi-resolution Processing Compression, Coding, and Transmission Detection, Recognition, and Classification Computational Imaging Color, Multi-spectral, and Hyper-spectral Imaging Stereoscopic, Multi-view, and 3D Processing Image and Video Quapty Models Motion Estimation Image Registration Image and Video Analysis Computer Vision Pattern Recognition Robotics and Vision Image Scanning, Display and Printing Shape and Image Retrieval Feature Extraction Medical Imaging |
Communications Cognitive Radio and AI-Enabled Networks Communication and Information System Security Communication QoS, Reliability and Modeling Communication Software and Multimedia Communication Theory Green Communication Systems and Networks IoT and Sensor Networks Mobile & Wireless Networks Next-Generation Networking and Internet Optical Networks & Systems Signal Processing for Communications Wireless Communications Aerial Communications Big Data Cloud Computing, Networking and Storage E-Health Full-Duplex Communications Machine Learning for Communications Molecular, Biological and Multi-Scale Communications Quantum Communications & Computing Satellite and Space Communications Smart Grid Communications Social Networks |
Machine Learning Natural Language Processing Machine learning methods Learning and adaptive control Learning/adaption of recognition and perception Learning for Handwriting Recognition Learning in Image Pre-Processing and Segmentation Learning in process automation Learning of appropriate behaviour Learning of action patterns Learning robots Feature extractions Support vector machines (SVM) Least-squares SVM (LS-SVM) Twin SVM (TWSVM) Extreme learning machine (ELM) Artificial neural network (ANN) Classification techniques Reinforce learning Deep learning Representation Learning and Deep Learning Scene Analysis and Understanding Neural Generative Models, Autoencoders, GANs Optimization and Learning Methods |