Physiology of Vision: a swift overview : Advanced Machine Perception A. Efros, CMU, Spring 2006 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: Class mailing list…

Image Formation Digital Camera The Eye Film

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

Figures © Stephen E. Palmer, 2002 What do we see? 3D world2D image

What do we see? 3D world2D image Painted backdrop

The Plenoptic Function 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… Figure by Leonard McMillan

Grayscale snapshot 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) P(  )

Color snapshot is intensity of light Seen from a single view point At a single time As a function of wavelength P(  )

Spherical Panorama All light rays through a point form a ponorama Totally captured in a 2D array -- P(  ) Where is the geometry??? See also: 2003 New Years Eve

A movie is intensity of light Seen from a single view point Over time As a function of wavelength P( ,t)

Space-time images x y t

Holographic movie is intensity of light Seen from ANY viewpoint Over time As a function of wavelength P( ,t,V X,V Y,V Z )

The Plenoptic Function 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… P( ,t,V X,V Y,V Z )

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

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

Rod / Cone sensitivity The famous sock-matching problem…

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

Electromagnetic Spectrum Human Luminance Sensitivity Function

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

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 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 Wavelength (nm) % Photons Reflected Red Yellow Blue Purple © 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 area mean variance © Stephen E. Palmer, 2002

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

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

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

Three kinds of cones: Physiology of Color Vision Why are M and L cones so close?

Retinal Processing © Stephen E. Palmer, 2002

Single Cell Recording Microelectrode 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

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

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

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

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

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

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

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

Retinal Receptive Fields

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

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

Visual Cortex aka: Primary visual cortex Striate cortex Brodman’s area 17 Cortical Area V1

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 0o0o © Stephen E. Palmer, 2002

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

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

Cortical Receptive Fields Complex Cells 120 o © 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 , ,000 2,000,000,000 The real world is High dynamic range

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

Long Exposure Real world Picture 0 to 255 High dynamic range

Short Exposure Real world Picture 0 to 255 High dynamic range

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

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