Download presentation

Presentation is loading. Please wait.

1
**Land’s Retinex algorithm**

9.35 Edward Adelson

2
**Land’s “Retinex” theory of lightness constancy**

Edwin Land founded Polaroid. Cameras need correct exposure (both color and Luminance) , i.e. need to “divide out” the effects of illumination. How do humans do it? (Note “Retinex” means “retina plus cortex.”)

3
**The formal problem Luminance (observed image intensity) =**

Illumination (incident light) x Reflectance (percent reflected) L(x,y) = I(x,y) x R(x,y) At every pixel you have one number, and you want to estimate two. You can’t unmultiply. It’s impossible (ill-posed). But humans seem to do it. How?

4
**Take advantage of scene statistics.**

Scene statistics means: some interpretations are more likely than others. For instance, it is common for illumination to vary gradually over space, but for reflectance to vary abruptly (e.g., to be piecewise constant). Land and McCann used an idealized “toy world” using “Mondrians”. (Image removed due to copyright considerations.)

5
**Using logs, make it additive**

Start by taking the log on both sides, making it into a simpler additive problem. Thus use log (illumination) instead of illumination, etc. We’ll still call it illumination for simplicity. (Image removed due to copyright considerations.)

6
**Consider it in 1-D Image formation is the forward process**

(Image removed due to copyright considerations.) Image formation is the forward process Vision is the inverse process.

7
**Land’s proposal: take spatial derivatives and classify them.**

(Image removed due to copyright considerations.) If they are big (strongly positive or negative), classify as reflectance. If small, classify as illumination. Integrate big ones to retrieve reflectance. Remainder is illuminance. Note: integration is a form of filling in.

8
**How to implement in neural hardware**

How to implement in neural hardware? One idea: use “edge detectors” in V1, which take an approximate spatial derivative. (Image removed due to copyright considerations.) Input Response

9
**Craik-O’Brien-Cornsweet effect**

Indicates the we give strong weight to edges, and not to slow gradients, in computing lightness. (Image removed due to copyright considerations.)

10
**Cornsweet square wave grating**

The bar centers are the same shades of gray. (Image removed due to copyright considerations.) (Image removed due to copyright considerations.) The emphasis on edges will make this grating resemble a normal square wave grating.

11
**Levels of analysis (articulated by Marr)**

• Computational: what is the problem to be solved? • Algorithmic: what approach to solve it? • Implementation: how to actually put it in hardware. Example 1: Calculate x/y (do floating point division). Example 2: Compute pi. Example 3: Do lightness constancy.

12
**Ideas illustrated with Retinex**

Levels of analysis. Scene statistics to help ill-posed problems. Toy world to help think through a problem. Convergence of ideas from computation, psychophysics, and physiology.

Similar presentations

OK

Tricolor Attenuation Model for Shadow Detection. INTRODUCTION Shadows may cause some undesirable problems in many computer vision and image analysis tasks,

Tricolor Attenuation Model for Shadow Detection. INTRODUCTION Shadows may cause some undesirable problems in many computer vision and image analysis tasks,

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Ppt on construction industry in india Ppt on sweat equity shares Ppt on index of industrial production Ppt on 555 timer schematic Read ppt on ipad 2 Ppt on google glass project Ppt on spiritual leadership quotes Ppt on reflection in c# How to use ppt on ipad Ppt on diode as rectifier definition