Special Session on Evolutionary Computer Vision and Image Processing (ECVIP)

The proposed special session aims to bring together theories and applications of evolutionary computation techniques to computer vision and image processing problems. In this sense, this special session aims to be a meeting place for researchers in the fields of computer vision and/or evolutionary computation, with the aim of enriching both disciplines by means of the hybridisation of state-of-the-art approaches from those domains. Topics of interest include, but are not limited to:

The scope of this special session covers, but not limited to, the following topics:

• New theories and methods in the application of evolutionary computation paradigms to computer vision and image processing problems.
  
  • Evolutionary computation paradigms include
    
    • genetic algorithms,
      
    • genetic programming,
      
    • evolutionary strategies,
      
    • evolutionary programming,
      
    • particle swarm optimization,
      
    • ant colony optimisation, and
      
    • differential evolution, among many others (including single- and multi-objective optimisation algorithms).
      
  • Potential applications in computer vision and image processing include
    
    • image segmentation
      
    • image registration
      
    • image restoration
      
    • image feature extraction
      
    • visual scene analysis
      
    • object detection and classification
      
    • handwritten digit recognition
      
    • object tracking
      
    • face detection and identification
      
    • texture image analysis
      
    • human activity recognition
      
    • robot vision, and
      
    • 3D scene reconstruction, among many others.
      
• Given the huge impact of deep learning in the computer vision community, especially from 2012, and the astonishing performance provided by deep learning algorithms in computer vision tasks, cross-fertilisation of evolutionary computation and deep or shallow neural networks applied to vision tasks is especially encouraged. This includes research in transfer learning and domain adaptation, as well as new training strategies based on evolutionary computation techniques, and any hybridisation of evolutionary computation with
  
  • multi-layer perceptrons,
    
  • autoencoders,
    
  • adversarial networks,
    
  • Boltzmann machines,
    
  • Hopfield networks,
    
  • deep belief networks,
    
  • neural Turing machines,
    
  • convolutional neural networks, and
    
  • recurrent neural networks, among many other neural models.
    
• Hybridisation of evolutionary computation methods and other computational intelligence and machine learning techniques (e.g. fuzzy systems, reinforcement learning, artificial immune systems, and learning classifier systems), applied to computer vision and image processing tasks are also encouraged.
  

• Paper Submission Deadline: 31 January 2021 21 February 2021
  
• Notification of Acceptance: 22 March 2021 6 April 2021
  
• Final Paper Submission Deadline: 7 April 2021 23 April, 2021
  

Biography of the Organisers

Pablo Mesejo received the M.Sc. and Ph.D. degrees in computer science respectively from University of Coruña (Spain) and University of Parma (Italy), where he was an Early Stage Researcher within the Marie Curie ITN MIBISOC ("Medical Imaging using Bio-inspired and Soft Computing"). He was a post-doctoral researcher at the ALCoV team of University of Auvergne (France) and the Mistis team of Inria Grenoble Rhône-Alpes (France), before joining the Perception team with an Inria Starting Researcher Position. He currently is a Marie Curie Experienced Researcher at the University of Granada (Spain). He is chair of the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing, and co-founder and scientific advisor of Panacea Cooperative Research S.Coop., a recently created SME focused on finding intelligent solutions aimed at solving unmet biomedical needs. His research interests include computer vision, machine learning and computational intelligence techniques mainly applied to biomedical image analysis problems.

Harith Al-Sahaf received the B.Sc. degree in computer science from Baghdad University (Iraq), in 2005. He joined the Victoria University of Wellington (VUW), (New Zealand) in July 2007 where he received his MCompSc and PhD degrees in Computer Science in 2010 and 2017, respectively. In October 2016, he has joined the School of Engineering and Computer Science, VUW as a Post-doctoral Research Fellow and as a full-time lecturer since September 2018. His current research interests include evolutionary computation, particularly genetic programming, computer vision, pattern recognition, evolutionary cybersecurity, machine learning, feature manipulation including feature detection, selection, extraction and construction, transfer learning, domain adaptation, one-shot learning, and image understanding. He is a member of the IEEE CIS ETTC Task Force on Evolutionary Computer Vision and Image Processing, the IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction, the IEEE CIS ISATC Task Force on Evolutionary Deep Learning and Applications, and the IEEE CIS ISATC Intelligent Systems for Cybersecurity.

• Stefano Cagnoni (University of Parma, Italy)
  
• Óscar Cordón (University of Granada, Spain)
  
• Sergio Damas (University of Granada, Spain)
  
• Eli David (Bar-Ilan University, Israel)
  
• Ivanoe De Falco (ICAR-CNR, Italy)
  
• Antonio Della Cioppa (University of Salerno, Italy)
  
• Francesco Fontanella (University of Cassino and Southern Lazio, taly)
  
• Óscar Ibáñez (Panacea Cooperative Research, Spain)
  
• Mario Köppen (Kyushu Institute of Technology, Japan)
  
• Krzysztof Krawiec (Poznan University of Technology, Poland)
  
• Evelyne Lutton (INRA, France)
  
• Clara Pizzuti (ICAR-CNR, Italy)
  
• Kai Qin (Swinburne University of Technology, Australia)
  
• Alessandra Scotto di Freca (University of Cassino and Southern Lazio, Italy)
  
• Stephen L. Smith (University of York, UK)
  
• Andy Song (RMIT University, Australia)
  
• Yanan Sun (Sichuan University, China)
  
• Bing Xue (Victoria University of Wellington, New Zealand)
  
• Mengjie Zhang (Victoria University of Wellington, New Zealand)