Abstract
Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision.
Highlights
Biological vision systems are remarkable at extracting and analysing the essential information for vital functional needs such as navigating through complex environments, finding food or escaping from a danger
Computational models of biological vision aim at identifying and understanding the strategies used by visual systems to solve problems which are often the same as the one encountered in computer vision
These models would shed light into functioning of biological vision and provide innovative solutions to engineering problems tackled by computer vision
Summary
Biological vision systems are remarkable at extracting and analysing the essential information for vital functional needs such as navigating through complex environments, finding food or escaping from a danger. Even simple biological systems can efficiently and quickly solve most of the difficult computational problems that are still challenging for artificial systems such as scene segmentation, local and global optical flow computation, 3D perception or extracting the meaning of complex objects or movements All these aspects have been intensively investigated in human psychophysics and the neuronal underpinnings of visual performance have been scrutinised over a wide range of temporal and spatial scales, from single cell to large cortical networks so that visual systems are certainly the best-known of all neural systems (see [59] for an encyclopaedic review). The two research fields have largely drifted apart, partly because of several technical obstacles that obstructed this interdisciplinary agenda for decades, such as the limited capacity of the experimental tools used to probe visual information processing or the limited computational power available for simulations
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