How Machines See the World: Five Essays on Biological and Artificial Vision

機器如何看世界: 關於生物視覺與人工視覺的五篇論文

Student thesis: Doctoral Thesis

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Award date14 Dec 2020


This thesis is a journey into the domain of biological and artificial vision. The aim of this thesis is to investigate how vision emerges in biological and artificial ‘entities’, and furthermore to outline the foundation of a wholly new understanding of vision. The thesis begins by questioning why and how the world appears to be articulated into objects that are distinct from each other and that have precise characteristics. Moreover, given the inability of biological sensory apparatus to retrieve the physical property of the world (as several psychophysical studies have demonstrated), this thesis reflects on the strategies that visually gifted creatures have implemented to circumvent this obstacle.

With these preoccupations in mind, this thesis highlights the difficulties, complications, and erroneous beliefs concerning vision that have been unveiled since the first tentative steps of creating visually ‘intelligent’ machines in the 1950s. Furthermore, it discusses the role played by these machines to better comprehend how vision works in humans and, more generally, biological creatures. From there, using the driving principle of neural network technologies - i.e. trial and error -, it proceeds to formulate an empirical and unified approach to vision that is able to connect biological and artificial vision systems. Additionally, this thesis investigates the psychological implications of visually ‘intelligent’ machines in redefining the visible landscape, and moreover, their participation in creating the desire of a perfect visual world. Finally, it tries to foresee future scenarios of the co-evolution of human and machine vision. The hitherto unexplored scenarios that the arrival of 'intelligent' artificial vision have opened up now oblige us to reconsider vision as a diffuse practice across visually gifted entities, and call us to re-examine the visual modalities in which we see the world and our position in it.

    Research areas

  • Vision, Machine vision, Biological vision, Artificial vision, Computer vision, Visual perception, Visual culture, visual – art