Computational Photography: Color perception, light spectra, contrast Connelly Barnes.

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Presentation transcript:

Computational Photography: Color perception, light spectra, contrast Connelly Barnes

Color Perception, etc ●Previously: ○Camera obscura / pinhole camera ○Cameras with lenses ○Modeling camera projections

Color Perception, etc ●Today: ○Human / electronic eyes ○Electromagnetic spectrum ○Color spaces Various slides by Alexei Efros, Fredo Durand, James Hays

Image Formation Digital Camera The Eye Film Slide by Efros

Sensor Array CMOS sensor Slide by Efros CCD sensor

Sampling and Quantization

Interlace vs. progressive scan by Steve Seitz

Progressive scan by Steve Seitz

Interlace by Steve Seitz

Rolling Shutter SLR cameras at high shutter speed, most CMOS cameras

The Eye 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 Slide by Steve Seitz

The Retina

© 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

Some Goals of Human Eye Recognize food Recognize friends, mates Detect predators Navigation -- identify 3D structure Limited memory, computation budget Highest resolution in fovea - (2 degrees, 50% of visual cortex) Absolute luminance discarded Edges, corners retained Store only a tiny fraction of what is observed

Visual Clutter - Bandwidth Overload

Eye Movements Motion Magnification -- Eye Movements

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

Ordinary Human Vision (Trichromatism)

Perceptual Sensitivity ITU Recommendation for HDTV: Y = 0.21 R G B Evolved to detect vegetation, berries?

Tetrachromatism Most birds, and many other animals, have cones for ultraviolet light. Some humans, mostly female, seem to have slight tetrachromatism. Bird cone responses

Color Spectra metamers

Slide by Fredo Durand

Color Image R G B

Images in Python/MATLAB R G B row column Image as array: h x w x channels I(y,x,channel) Red channel, upper left corner: MATLAB: I(1,1,1), Python: I[0,0,0]

Color spaces: RGB 0,1,0 0,0,1 1,0,0 Image from: Some drawbacks Strongly correlated channels Non-perceptual Default color space R (G=0,B=0) G (R=0,B=0) B (R=0,G=0)

Color spaces: HSV Intuitive color space H (S=1,V=1) S (H=1,V=1) V (H=1,S=0)

Color spaces: L*a*b* “Perceptually uniform” color space L (a=0,b=0) a (L=65,b=0) b (L=65,a=0)

White Balance Slide by Alexei Efros

Problem: Dynamic Range , ,000 2,000,000,000 The real world is High dynamic range Slide by Alexei Efros

pixel (312, 284) = 42 Image 42 photons? Is Camera a photometer? Slide by Alexei Efros

Long Exposure Real world Picture 0 to 255 High dynamic range Slide by Alexei Efros

Short Exposure Real world Picture 0 to 255 High dynamic range Slide by Alexei Efros

sceneradiance (W/sr/m ) sceneradiance  sensorirradiancesensorirradiancesensorexposuresensorexposure LensLensShutterShutter 22 tttt analog voltages analog voltages digital values digital values pixel values pixel values CCD ADC Remapping Image Acquisition Pipeline Camera is NOT a photometer!

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 Photo-meter