Understanding images in biological and computer vision

Schofield, Andrew and Gilchrist, Iain and Bloj, Marina and Leonardis, Ales and Bellotto, Nicola (2018) Understanding images in biological and computer vision. Interface Focus, 8 (4). pp. 1-3. ISSN 2042-8898

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Abstract

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.

Keywords:Computer vision, biological vision
Subjects:C Biological Sciences > C100 Biology
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:32403
Deposited On:02 Jul 2018 12:42

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