Vision is the complex process of deriving meaning from what is seen. The fields of computer vision and image processing have tried to automate tasks that the human visual system can do, with the aim of gaining a high-level understanding of images and videos. Computer vision algorithms have been successfully applied to a high number of real-world problems ranging from remote sensing to medical image analysis, video surveillance, human-robot interaction, and computer-aided design. In turn, evolutionary computation methods have been shown to be more efficient than classical optimization approaches for discontinuous, non-differentiable, multimodal and noisy problems. They have also demonstrated their ability as robust approaches to cope with the fundamental steps of the computer vision and image processing pipeline (e.g. restoration, segmentation, registration, or tracking). As a result of the convergence of the computer vision and evolutionary computation research fields, a large number of research activities have arisen in the last two decades including a myriad of scientific papers, international projects, book chapters, special sessions in conferences, and special issues in journals.
Taken into account the aforementioned context, the ECVIP task force (TF) was born in 2010 as a common track to relate all events bringing together evolutionary computation and computer vision. The main idea was to convert this TF in an outstanding tool able to create a coordinated body to interconnect all kinds of activities that, prior to the existence and consolidation of the TF, were generally announced in isolation. Individual conferences and other kinds of specific activities only provide limited means to open a fruitful exchange of ideas, transfer of tools, and generation of new research lines, while our objectives require a supervised and integrated series of actions with the continuous participation of recognized researchers. The ECVIP TF provides not only excellent opportunities to organize activities, especially but not exclusively in the framework of IEEE journals and IEEE supported conferences, but also to define the research agenda for the future in advanced evolutionary algorithms in computer vision and image processing.
The main goal of this TF is to promote research on evolutionary computer vision and image processing, facilitating the knowledge transfer and collaboration between researchers from different disciplines (e.g. evolutionary computation, computer vision, deep learning, biomedical imaging, signal processing, pattern recognition).
The main goal of this TF can be divided in the following sub-objectives:
This task force is focused on all aspects (theory, practice and applications) belonging to the intersection between evolutionary computation/computational intelligence and disciplines like computer vision, image processing, medical imaging, deep learning, signal processing and pattern recognition.
Topics of interest include but are not limited to: