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, image analysis and image processing problems.
  
  • Evolutionary computation paradigms include
    
    • genetic algorithms,
      
    • genetic programming,
      
    • evolutionary strategies,
      
    • evolutionary programming,
      
    • particle swarm optimisation,
      
    • ant colony optimisation,
      
    • differential evolution,
      
    • evolutionary multi-objective algorithms,
      
    • evolutionary transfer learning,
      
    • and many others (e.g., neuroevolution, surrogate-assisted evolutionary algorithms).
      
  • Potential applications in computer vision and image processing include
    
    • image segmentation,
      
    • image registration,
      
    • visual scene analysis,
      
    • image feature analysis,
      
    • object detection,
      
    • image classification,
      
    • handwritten digit recognition,
      
    • object tracking,
      
    • face detection and recognition,
      
    • texture image analysis,
      
    • medical image analysis,
      
    • gesture recognition,
      
    • and robot vision, 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 is especially encouraged. This will include research in
  
  • transfer learning and domain adaptation,
    
  • meta-learning,
    
  • few-shot learning,
    
  • neural architecture search,
    
  • automated machine learning,
    
  • and any hybridisation of evolutionary computation with
    
    • multi-layer perceptrons,
      
    • autoencoders,
      
    • adversarial networks,
      
    • convolutional neural networks, and
      
    • transformers,
      
    • and recurrent neural networks, among many other neural models.
      
• Hybridisations of evolutionary computation methods and other computational intelligence and machine learning techniques (e.g. fuzzy systems, reinforcement learning, and ensemble methods), applied to computer vision and image processing tasks are also encouraged.
  

• Paper Submission Deadline: 31 Jan 2022
  
• Notification of Acceptance: 26 Apr 2022
  
• Final Paper Submission Deadline: 23 May 2022
  

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 a Marie Curie ITN. Later, 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. In 2018, he joined the University of Granada (Spain) as a Marie Curie Experienced Researcher, and he currently is Associate Professor at the same institution. He is chair of the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing, and co-founding partner and chief AI officer of Panacea Cooperative Research, an 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 from Bagh-dad University, Baghdad, Iraq, in 2005, the master’s and Ph.D. degrees from the Victoria University of Wellington (VUW), Wellington, New Zealand, in 2010 and2017, respectively, all in computer science. In October 2016, he 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, 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.

Ying Bi received the B.Sc.degree in 2013 from Wuhan Polytechnic University, Hubei, China, the M.Sc. degree in 2016 from Shenzhen University, Shenzhen, China, and the PhD degree in 2020 from Victoria University of Wellington (VUW), New Zealand. She is currently a postdoctoral research fellow and project coordinator with the School of Engineering and Computer Science at VUW. Her research focuses mainly on computer vision, image analysis, machine learning, evolutionary computation, classification, feature learning, and transfer learning. She has published an authored book and over 40 papers in fully refereed journals and conferences in computer vision and evolutionary computation. She has been serving as an organising committee member of IEEE CEC 2019 and Australasian AI 2018, a guest editor of special issue on evolutionary and memetic algorithms for computer vision and image processing in the journal of Memetic Computing, an organiser of a workshop in IEEE ICDM 2021, a special session in IEEE SSCI 2021 and a special session in IDEAL 2021, and a program committee member of over ten international conferences including IJCAI, GECCO, IEEE WCCI/CEC, IEEE SSCI, and Australian AI. She was co-chair poster session in IEEE CEC 2019. She is serving as a reviewer of over ten international journals including all major journals related to EC. She is a member of IEEE, IEEE CIS, ACM SIGEVO, and the IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction.

• 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)