In conjunction with ICIAP2017

Location: Room 9, Monastery of San Nicolò l’Arena, Catania, Sicily

Date: 11 September 2017

We are pleased to announce the First International Workshop on Brain-Inspired Computer Vision (WBICV2017), which will be held as part of the conference ICIAP2017, 11 September, Catania, Sicily (Italy).


The visual perception of a human is a complex process performed by various elements of the visual system of the brain. This remarkable unit of the brain has been used as a source of inspiration for developing algorithms that can be used in computer vision tasks such as finding objects, analysing motion, identifying or detecting instances, reconstructing scenes or restoring images. One of the most challenging goals in computer vision is, therefore, to design and develop algorithms that can process visual information as humans do.

The main aim of WBICV2017 is to bring together researchers from the diverse fields of computer science (pattern recognition, machine learning, artificial intelligence, high performance computing and visualisation) along with the fields of visual perception and visual psychophysics who aim to model different phenomena of the visual system of the brain. We look forward to discussing the current and next generation of brain system modelling for a wide range of vision related applications. This workshop aims to comprise powerful, innovative and modern image analysis algorithms and tools inspired by the function and biology of the visual system of the brain.

The researchers will present their latest progress and discuss novel ideas in the field. Besides the technologies used, emphasis will be given to the precise problem definition, the available benchmark databases, the need of evaluation protocols and procedures in the context of brain-inspired computer vision methods and applications.

Papers are solicited in, but not limited to, the following TOPICS:

  • Mathematical models of visual perception
  • Brain-inspired algorithms
  • Learning: Deep learning, recurrent networks, differentiable neural computers, sparse coding.
  • The appearance of neuronal properties: sparsity and selectivity
  • Circuitry: hierarchical representations and connections between layers.
  • Selecting where to look: saliency, attention and active vision.
  • Hierarchy of visual cortex areas
  • Feedforward, feedback and inhibitory mechanisms
  • Applications: object recognition, object tracking, medical image analysis, contour detection and segmentation