Understanding images in biological and computer vision

Andrew Schofield, Iain Gilchrist, Marina Bloj, Ales Leonardis, Nicola Bellotto

Research output: Contribution to journalArticlepeer-review


This issue of Interface Focus is a collection of papers arising out of a Royal
Society Discussion meeting entitled ‘Understanding images in biological and
computer vision’ held at Carlton Terrace on the 19th and 20th February,
2018. There is a strong tradition of inter-disciplinarity in the study of visual perception
and visual cognition. Many of the great natural scientists including
Newton [1], Young [2] and Maxwell (see [3]) were intrigued by the relationship
between light, surfaces and perceived colour considering both physical and perceptual
processes. Brewster [4] invented both the lenticular stereoscope and the
binocular camera but also studied the perception of shape-from-shading. More
recently, Marr’s [5] description of visual perception as an information processing
problem led to great advances in our understanding of both biological
and computer vision: both the computer vision and biological vision communities
have a Marr medal. The recent successes of deep neural networks in
classifying the images that we see and the fMRI images that reveal the activity
in our brains during the act of seeing are both intriguing. The links between
machine vision systems and biology may at sometimes be weak but the
similarity of some of the operations is nonetheless striking [6].
This two-day meeting brought together researchers from the fields of biological
and computer vision, robotics, neuroscience, computer science and
psychology to discuss the most recent developments in the field. The meeting
was divided into four themes: vision for action, visual appearance, vision for
recognition and machine learning.
Original languageEnglish
Article number20180027
JournalInterface Focus
Issue number4
Early online date15 Jun 2018
Publication statusPublished - 6 Aug 2018


Dive into the research topics of 'Understanding images in biological and computer vision'. Together they form a unique fingerprint.

Cite this