Image Perception ‘Let there be light! ‘

“Let there be light”

Image perception “Let there be Newton, then comes the light light”

ELECTROMAGNETIC SPECTRUM AND VISIBLE BAND Can perceive 7% Cycle per second

Human Visual System Eye-Brain Channel brain eye Low level Acquisition Sampling Quantization Enhancement Restoration Compression High Level Enhancement + Restoration+ Compression+ Feature Extraction Labeling Understanding

EYE: Diameter:20mm Pupil 2mm Fovea: Sensors,rodes and cones A Feedback Control System: Adjusts the diameter of Eye ball Pupil Lenz

RODES Million for Shape perception

CONES: 6-7 Million For color perception CONES: 6-7 Million For color perception

Cones:6-7 Milion

Sampling and quantization around the Fovea Cones: 6-7 million, located mostly on fovea, sensitive to color, each one is connected to a nerve cell Rodes: million, distributed over retina, sensitive to shape info, several of them is connected to the same nerve cell

In the Brain (60-70% is for vision)

In the Brain Gestalt Theory (Koffka 1935) LGN: Feature extraction, classification, texture analysis, recognition. Visual Cortex: –Scene representation –Interpretation –Knowledge manipulation –Hierarchical library of indexed feature –Task related to hierarchical vision

Lateral Geniculate Nucleus Primary Visual Cortex

ART (Grosberg, 1978) There are two state –Resonance –Reset

CHARATERSITICS OF HVS RESOLUTION: Ability to separate 1.Two adjacent pixels: Spatial Resolution 2.Two colors: Radiometric resolution 3.Two frames: Time resolution Response of HVS to resolution depends on the 1.Distance from the eye 2.Illumination

Low frequency High frequency f=2 f= 4 x direction 1.Spatial resolution

Field of viev Spatial Resolution is restricted by physical size of rodes and cones sampling rate of image

2. Radiometric resolution: Response to intensity and color Pupil changes size according to the brightnes level What you see is different then what it is dark Glare limit quantization

Brightness adaptation is logarithmic function of intensity

Weber’s law: weber ratio =  I/I I: intensity,  I: increment of discriminable illumination Weber’s law: weber ratio =  I/I I: intensity,  I: increment of discriminable illumination rodes cones

MACH BAND EFFECT (Ernst Mach)

Simultaneous Contrast: backround effect the color of the object

3. Temporal resolution response of HVS as a function of time Flicker frequency: ability to observe a flicker Depends on the brightness: 50 hertz

Visual Perception- Illusions

Simultaneous Contrast

HVS: Visual Illusion From Prof. E. H. Adelson

What is this? HVS: Visual Illusion

Which lines are straight? HVS: Visual Illusion

İmage acquisition devices Three types –Single sensor (scanners) –Sensor stripes (xerox, tomography), circular sensor stripes –Sensor arrays (cameras)

İmage acquisition: current technology

Chapter 2: Digital Image Fundamentals

Emerging technology: Single pixel camera Compressive sensing

Image Representation Continuous image: f(x,y), d= (x,y), r: Brightness values Digital image: both d and r is discrete Discrete image d is discrete, r is continuos Motion: f(x,y,t) Binary image: r  (0,1) Gray scale image r  (0.1, L-1) Color image f i (x,y), i=r,g,b Multispectral image: f i (x,y)

IMAGE SAMPLING: Digitize the domain of f(x,y) Generate an NxM matrix. How: Study sampling theorem IMAGE SAMPLING: Digitize the domain of f(x,y) Generate an NxM matrix. How: Study sampling theorem

IMAGE QUANTIZATION: Digitize the range of f(x,y)

Sampled and quantized image

f(x,y): storage space: NxMxkxT T: number of frames Number of gray values: L=2 k: f(x,y): storage space: NxMxkxT T: number of frames Number of gray values: L=2 k:

Storage Spaces: S= NxMxkxi i= # of bands L=2 k Storage Spaces: S= NxMxkxi i= # of bands L=2 k

SAMPLING

QUANTIZATION

Chapter 2: Digital Image Fundamentals

HOW TO SELECT SAMPLING AND QUANTIZATION RATES? HOW TO SELECT SAMPLING AND QUANTIZATION RATES?

Chapter 2: Digital Image Fundamentals

Storage Spaces: S= NxMxkxi i= # of bands L=2 k Storage Spaces: S= NxMxkxi i= # of bands L=2 k

Chapter 2: Digital Image Fundamentals