CS 548: Computer Vision REVIEW: Digital Image Basics Spring 2016 Dr. Michael J. Reale
Human Vision System: Cones and Rods Two types of receptors in eye: Cones Brightness and color Photopic vision = bright-light vision Most in fovea true center of vision Rods Low-level light (no color) Scotopic vision = dim-light vision (# of rods) > (# of cones)
Image Perception and Formation: Eye vs. Camera Camera Lens moves but has fixed shape Eye Lens changes shape but doesn t move
Vision Properties: Brightness / Contrast Adaptation Humans dynamically adjust perceived brightness and contrast based on average local intensity Wide range of intensity levels human eye can adapt to Cannot adapt to entire range simultaneously
Vision Properties: Brightness and Spatial Discrimination Brightness discrimination Discriminate between different intensity levels Spatial discrimination Discriminate between different physical points on object
Image Acquisition: EM Spectrum Electromagnetic spectrum Varies in wavelength Visible light fraction of spectrum Higher frequency higher energy
Image Acquisition: Sensor Mechanics Illumination energy sensor voltage waveform
Image Acquisition: Image Digitizing Sampling = digitizing the coordinate values Ideally, sampling at Nyquist Rate: 2*F max F max = maximum frequency Quantization = digitizing the amplitude values Both may be uniform or non-uniform
Image Sensing: Types of Sensors Single sensor Must move sensor/object in at least two dimensions OR use mirrors Infinite possible resolution Line sensors Must move sensor/object in at least one dimension Sensor strips (scanners) and CT/MRI Finite resolution in one dimension; infinite in others Array of sensors 2D array CCD Finite resolution in all dimensions
Image Acquisition: How much space to represent a digital image? Given a digital image with: M rows (y coordinate) N columns (x coordinate) k bits per pixel Number of pixels total = M*N Number of possible gray levels per pixel = 2 k Total size = M*N*k
Image Acquisition: Coordinates and Values In image v = f(x,y): (x,y) IMAGE coordinates (NOT world coordinates) v INDEX to illumination value (NOT true illumination value)
Image Acquisition: Spatial Resolution Spatial resolution measured by: # of pixels per physical image size E.g. DPI = dots per inch # of line pairs per physical vertical image distance E.g., lp/mm = line pairs per millimeter
Image Acquisition: Aliasing Aliasing Jagged or staircase effect Caused by sampling/displaying lower than Nyquist rate (2*F max )
Image Transformations Size change: Zoom in low to high resolution Need to fill gaps: Pixel replication nearest neighbor per pixel Pixel interpolation weighted average of pixel and neighbors Super-resolution Map from new, high-res back to old, low-res Zoom out high to low resolution Pick pixels using above methods in reverse Map from new, low-res back to old, high-res Shape Change Also known as geometric transformation
Image Quality Assessment Subjective Human-verified Used often for image enhancement, visualization, and effects Objective Ground truth comparison Used often for encoding/decoding, classification, etc.
Connected Components: Neighbors N 4 (P) strong neighbors North, South, East, West 4-neighbors N D (P) weak neighbors Diagonals N 8 (P) = N 4 (P) + N D (P) 8-neighbors
Connected Components: Adjacency 4-adjacency q is in set N 4 (p) 8-adjacency q is in set N 8 (p) m-adjacency (mixed-adjacency) 1) q is in set N 4 (p) OR 2) q is in set N D (p) AND no 1-valued pixels in N 4 (p) N 4 (q)
Connected Components: Definitions Path = p and q connected m path: path based on m-connected pixels closed path: starting p and ending q are connected Connected component Set of pixels which are connected connected set Region Connected set with closed-path boundary Edge Gray-level discontinuity at a point I.e., perpendicular to edge, intensity changes sharply Linked edge points edge segment
Connected Components: Distance Distance Types: Euclidean (D e ) circular, disk shape De(p,q) = sqrt[(xp xq)^(2) + (yp yq)^(2)] City-block or Manhattan (D 4 ) diamond shape D 4 (p,q) = (xp xq) + (yp yq) Chessboard (D 8 ) square shape D 8 (p,q) = max( (xp xq), (yp yq) ) Shortest m-path (D m )
Pixel Operations Pixel operations Point-wise operations Can operate on one or two images Treat image as an M x N matrix Can be linear or non-linear