S02 - Machine Learning in Computer Vision and Robotics (MLCVR)

Chairs

  • José García-Rodríguez, University of Alicante.
  • Enrique Dominguez, University of Malaga.
  • Ramón Moreno, Grupo Antolín.

Abstract

Special session webpage http://www.dtic.ua.es/~jgarcia/IWINAC2022/

Over the last decades there has been an increasing interest in using machine learning and in the last few years, deep learning methods, combined with other vision techniques to create autonomous systems that solve vision problems in different fields. This special session is designed to serve researchers and developers to publish original, innovative and state-of-the art algorithms and architectures for real time applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, neural networks, intelligent interfaces and biomimetic object-vision recognition.

This special session provides a platform for academics, developers, and industry-related researchers belonging to the vast communities of *Neural Networks*, *Computational Intelligence*, *Machine Learning*, *Deep Learning*, *Biometrics*, *Vision systems*, and *Robotics*, to discuss, share experience and explore traditional and new areas of the computer vision, machine and deep learning combined to solve a range of problems. The objective of the workshop is to integrate the growing international community of researchers working on the application of Machine Learning and Deep Learning Methods in Vision and Robotics to a fruitful discussion on the evolution and the benefits of this technology to the society.

The methods and tools applied to vision and robotics include, but are not limited to, the following:

  • Computational Intelligence methods
  • Machine Learning methods
  • Self-adaptation, self-organisation and self-supervised learning
  • Robust computer vision algorithms (operation under variable conditions, object tracking, behaviour analysis and learning, scene segmentation, ...)
  • Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
  • Registration Methods
  • Convolutional Neural Networks CNN
  • Recurrent Neural Networks RNN
  • Deep Reinforcement Learning DRL
  • Generative Adversial Networks
  • Predictive Learning
  • Active-Incremental-Online Learning
  • Hardware implementation and algorithms acceleration (GPUs, FPGA,s,.)

The fields of application can be identified, but are not limited to, the following:

  • Video and Image Processing
  • Video tracking
  • 6D Object detection
  • Objects Grasping/Manipulation
  • 3D Scene reconstruction
  • 3D Tracking in Virtual Reality Environments
  • 3D Volume visualization
  • Intelligent Interfaces (User-friendly Man Machine Interface)
  • Multi-camera and RGB-D camera systems
  • Multi-modal Human Pose Recovery and Behavior Analysis
  • Human body reconstruction
  • Gesture and posture analysis and recognition
  • Biometric Identification and Recognition
  • Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
  • Surveillance systems
  • Robotic vision
  • Synthetic data generation
  • Sim2Real
  • Autonomous and Social Robots
  • Industry 4.0
  • IoT and Cyber-physical Systems