Physiology of Vision: a swift overview 16-721: Learning-Based Methods in Vision A. Efros, CMU, Spring 2007 Some figures from Steve Palmer

Class Introductions Name: Research area / project / advisor What you want to learn in this class? When I am not working, I ______________ Favorite fruit:

Image Formation Digital Camera Film The Eye

Monocular Visual Field: 160 deg (w) X 135 deg (h) Binocular Visual Field: 200 deg (w) X 135 deg (h)

What do we see? 3D world 2D image Figures © Stephen E. Palmer, 2002

What do we see? 3D world 2D image Painted backdrop

The Plenoptic Function Figure by Leonard McMillan Q: What is the set of all things that we can ever see? A: The Plenoptic Function (Adelson & Bergen) Let’s start with a stationary person and try to parameterize everything that he can see…

Grayscale snapshot P(q,f) is intensity of light Seen from a single view point At a single time Averaged over the wavelengths of the visible spectrum (can also do P(x,y), but spherical coordinate are nicer)

Color snapshot P(q,f,l) is intensity of light Seen from a single view point At a single time As a function of wavelength

Spherical Panorama All light rays through a point form a ponorama See also: 2003 New Years Eve http://www.panoramas.dk/fullscreen3/f1.html All light rays through a point form a ponorama Totally captured in a 2D array -- P(q,f) Where is the geometry???

A movie P(q,f,l,t) is intensity of light Seen from a single view point Over time As a function of wavelength

Space-time images t y x

Holographic movie P(q,f,l,t,VX,VY,VZ) is intensity of light Seen from ANY viewpoint Over time As a function of wavelength

The Plenoptic Function P(q,f,l,t,VX,VY,VZ) Can reconstruct every possible view, at every moment, from every position, at every wavelength Contains every photograph, every movie, everything that anyone has ever seen! it completely captures our visual reality! Not bad for a function…

The Eye is a camera The human eye is a camera! Iris - colored annulus with radial muscles Pupil - the hole (aperture) whose size is controlled by the iris What’s the “film”? photoreceptor cells (rods and cones) in the retina

The Retina

Retina up-close Light

Two types of light-sensitive receptors Cones cone-shaped less sensitive operate in high light color vision Rods rod-shaped highly sensitive operate at night gray-scale vision © Stephen E. Palmer, 2002

Rod / Cone sensitivity The famous sock-matching problem…

Distribution of Rods and Cones Night Sky: why are there more stars off-center? © Stephen E. Palmer, 2002

Electromagnetic Spectrum At least 3 spectral bands required (e.g. R,G,B) Human Luminance Sensitivity Function http://www.yorku.ca/eye/photopik.htm

…because that’s where the Visible Light Why do we see light of these wavelengths? …because that’s where the Sun radiates EM energy © Stephen E. Palmer, 2002

The Physics of Light Any patch of light can be completely described physically by its spectrum: the number of photons (per time unit) at each wavelength 400 - 700 nm. © Stephen E. Palmer, 2002

The Physics of Light Some examples of the spectra of light sources © Stephen E. Palmer, 2002

The Physics of Light Some examples of the reflectance spectra of surfaces Red 400 700 Yellow 400 700 Blue 400 700 Purple 400 700 % Photons Reflected Wavelength (nm) © Stephen E. Palmer, 2002

The Psychophysical Correspondence There is no simple functional description for the perceived color of all lights under all viewing conditions, but …... A helpful constraint: Consider only physical spectra with normal distributions mean area variance © Stephen E. Palmer, 2002

The Psychophysical Correspondence Mean Hue # Photons Wavelength © Stephen E. Palmer, 2002

The Psychophysical Correspondence Variance Saturation Wavelength # Photons © Stephen E. Palmer, 2002

The Psychophysical Correspondence Area Brightness # Photons Wavelength © Stephen E. Palmer, 2002

Physiology of Color Vision Three kinds of cones: Why are M and L cones so close? © Stephen E. Palmer, 2002

Retinal Processing © Stephen E. Palmer, 2002

Single Cell Recording Microelectrode Electrical response Amplifier Electrical response (action potentials) mV © Stephen E. Palmer, 2002

Single Cell Recording © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in ganglion cells: On-center Off-surround Stimulus condition Electrical response © Stephen E. Palmer, 2002

Retinal Receptive Fields RF of On-center Off-surround cells © Stephen E. Palmer, 2002

Retinal Receptive Fields RF of Off-center On-surround cells Surround Center © Stephen E. Palmer, 2002

Retinal Receptive Fields

Retinal Receptive Fields Receptive field structure in bipolar cells Light © Stephen E. Palmer, 2002

Retinal Receptive Fields Receptive field structure in bipolar cells © Stephen E. Palmer, 2002

Visual Cortex Cortical Area V1 aka: Primary visual cortex Striate cortex Brodman’s area 17 © Stephen E. Palmer, 2002

Cortical Receptive Fields Single-cell recording from visual cortex David Hubel & Thorston Wiesel © Stephen E. Palmer, 2002

Cortical Receptive Fields Single-cell recording from visual cortex © Stephen E. Palmer, 2002

Cortical Receptive Fields Three classes of cells in V1 Simple cells Complex cells Hypercomplex cells © Stephen E. Palmer, 2002

Cortical Receptive Fields Simple Cells: “Line Detectors” © Stephen E. Palmer, 2002

Cortical Receptive Fields Simple Cells: “Edge Detectors” © Stephen E. Palmer, 2002

Cortical Receptive Fields Constructing a line detector © Stephen E. Palmer, 2002

Cortical Receptive Fields Complex Cells 0o © Stephen E. Palmer, 2002

Cortical Receptive Fields Complex Cells 60o © Stephen E. Palmer, 2002

Cortical Receptive Fields Complex Cells 90o © Stephen E. Palmer, 2002

Cortical Receptive Fields Complex Cells 120o © Stephen E. Palmer, 2002

Cortical Receptive Fields Constructing a Complex Cell © Stephen E. Palmer, 2002

Cortical Receptive Fields Hypercomplex Cells © Stephen E. Palmer, 2002

Cortical Receptive Fields Hypercomplex Cells © Stephen E. Palmer, 2002

Cortical Receptive Fields Hypercomplex Cells © Stephen E. Palmer, 2002

Cortical Receptive Fields Hypercomplex Cells “End-stopped” Cells © Stephen E. Palmer, 2002

Cortical Receptive Fields “End-stopped” Simple Cells © Stephen E. Palmer, 2002

Cortical Receptive Fields Constructing a Hypercomplex Cell © Stephen E. Palmer, 2002

Mapping from Retina to V1

Why edges? So, why “edge-like” structures in the Plenoptic Function?

Because our world is structured!

Problem: Dynamic Range The real world is High dynamic range 1 1500 25,000 400,000 2,000,000,000

Is Camera a photometer? Image pixel (312, 284) = 42 42 photos? 2

Long Exposure 10-6 High dynamic range 106 10-6 106 0 to 255 Real world Picture 0 to 255

Short Exposure 10-6 High dynamic range 106 10-6 106 0 to 255 Real world 10-6 106 Picture 0 to 255

Varying Exposure

What does the eye sees? The eye has a huge dynamic range Do we see a true radiance map?

Eye is not a photometer! "Every light is a shade, compared to the higher lights, till you come to the sun; and every shade is a light, compared to the deeper shades, till you come to the night." — John Ruskin, 1879

Cornsweet Illusion

Campbell-Robson contrast sensitivity curve Sine wave Campbell-Robson contrast sensitivity curve

Metamers Eye is sensitive to changes (more on this later…)