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Nightvision Based on a Biological Model

Oskarsson, Magnus LU ; Malm, Henrik and Warrant, Eric LU (2015) In Biologically Inspired Computer Vision: Fundamentals and Applications p.377-404
Abstract
The colors and contrasts of the nocturnal world are just as rich as those found in the diurnal world. This chapter describes a recent biomimetic advance inspired by the visual systems of nocturnal insects. Since the underlying principles of both animal and camera vision are similar, it is natural to try to mimic the neural processes of nocturnal animals in order to construct efficient computer vision algorithms. The chapter explains both the underlying biological principles and the computer vision approach in detail. It discusses the specific characteristics of different types of noise that are present in digital images and relate them to their biological counterparts. The “dark noise” in photoreceptors is described. This thermal effect is... (More)
The colors and contrasts of the nocturnal world are just as rich as those found in the diurnal world. This chapter describes a recent biomimetic advance inspired by the visual systems of nocturnal insects. Since the underlying principles of both animal and camera vision are similar, it is natural to try to mimic the neural processes of nocturnal animals in order to construct efficient computer vision algorithms. The chapter explains both the underlying biological principles and the computer vision approach in detail. It discusses the specific characteristics of different types of noise that are present in digital images and relate them to their biological counterparts. The “dark noise” in photoreceptors is described. This thermal effect is also present in digital sensors and is called dark current noise. In order to produce a digital image, the electrical signal is quantized into a digital signal with a fixed number of bits. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Computer vision, night vision, biologically inpired methods
in
Biologically Inspired Computer Vision: Fundamentals and Applications
editor
Cristobal, Gabriel; Perrinet, Laurent; Keil, Matthias; ; and
pages
377 - 404
publisher
John Wiley & Sons
external identifiers
  • scopus:84950107362
ISBN
9783527412648
9783527680863
DOI
10.1002/9783527680863.ch16
language
English
LU publication?
yes
id
1bc46c59-a1d6-4aee-9948-186d574e0ca4 (old id 8519267)
date added to LUP
2016-01-22 11:34:41
date last changed
2017-01-01 08:03:20
@inbook{1bc46c59-a1d6-4aee-9948-186d574e0ca4,
  abstract     = {The colors and contrasts of the nocturnal world are just as rich as those found in the diurnal world. This chapter describes a recent biomimetic advance inspired by the visual systems of nocturnal insects. Since the underlying principles of both animal and camera vision are similar, it is natural to try to mimic the neural processes of nocturnal animals in order to construct efficient computer vision algorithms. The chapter explains both the underlying biological principles and the computer vision approach in detail. It discusses the specific characteristics of different types of noise that are present in digital images and relate them to their biological counterparts. The “dark noise” in photoreceptors is described. This thermal effect is also present in digital sensors and is called dark current noise. In order to produce a digital image, the electrical signal is quantized into a digital signal with a fixed number of bits.},
  author       = {Oskarsson, Magnus and Malm, Henrik and Warrant, Eric},
  editor       = {Cristobal, Gabriel and Perrinet, Laurent and Keil, Matthias},
  isbn         = {9783527412648},
  keyword      = {Computer vision,night vision,biologically inpired methods},
  language     = {eng},
  pages        = {377--404},
  publisher    = {John Wiley & Sons},
  series       = {Biologically Inspired Computer Vision: Fundamentals and Applications},
  title        = {Nightvision Based on a Biological Model},
  url          = {http://dx.doi.org/10.1002/9783527680863.ch16},
  year         = {2015},
}