ETD system

Electronic theses and dissertations repository


Tesi etd-10282017-221538

Thesis type
Tesi di dottorato di ricerca
Bioinspired Artificial Vision
Settore scientifico disciplinare
Corso di studi
tutor Prof. De Rossi, Danilo
commissario Prof. Carpi, Federico
commissario Prof. Scilingo, Enzo Pasquale
commissario Prof. Viollet, Stephane
Parole chiave
  • Optics
  • Image Formation Simulation
  • Bioinspiration
  • Computer Vision
  • Artificial Vision
  • Animal Eyes
  • Social Robots
Data inizio appello
Data di rilascio
Riassunto analitico
Bioinspired Arti cial Vision is a multi-disciplinary research eld aimed at developing technology and system implementing visual functions taking inspiration from studies on biological vision apparatus able to accomplish the same function. Such a type of arti cial systems include a visual body and a visual brain. The body is de ned as the whole of the robotic eyes optical and imaging components, and their associated optical and imaging early (low-level)processing, and the brain is the whole cognitive (high-level) visual processing. This PhD thesis targets three foundational innovations in all the phases of arti cial vision system design. First, a bioinspired technology to accomplish a low-level visual mechanism such as keep objects in focus. It consists in the design, development and characterization of a tunable lenses entirely made of soft solid matter (elastomers) whose working principle is inspired by the accommodation in bird and reptile. Second, a visual perception system which implement the high-level visual tasks (e.g. estimation of visually salient stimuli, face/gesture detection and recognition, etc.) that a humanoid robot need to extract during the social interaction with humans. Third and last innovation target a evolutionary-driven methodology in design the over-all arti cial vision system. State-of-the-art evolutionary algorithms have been used only in developing the visual brain through simulation. Here, I introduce a software tool that enable a realistic estimation of the visual body imaging response that can be used to model this part of the system in a simulated scenario