CS 548: Computer Vision REVIEW: Digital Image Basics. Spring 2016 Dr. Michael J. Reale

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1 CS 548: Computer Vision REVIEW: Digital Image Basics Spring 2016 Dr. Michael J. Reale

2 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)

3 Image Perception and Formation: Eye vs. Camera Camera Lens moves but has fixed shape Eye Lens changes shape but doesn t move

4 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

5 Vision Properties: Brightness and Spatial Discrimination Brightness discrimination Discriminate between different intensity levels Spatial discrimination Discriminate between different physical points on object

6 Image Acquisition: EM Spectrum Electromagnetic spectrum Varies in wavelength Visible light fraction of spectrum Higher frequency higher energy

7 Image Acquisition: Sensor Mechanics Illumination energy sensor voltage waveform

8 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

9 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

10 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

11 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)

12 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

13 Image Acquisition: Aliasing Aliasing Jagged or staircase effect Caused by sampling/displaying lower than Nyquist rate (2*F max )

14 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

15 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.

16 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

17 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)

18 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

19 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 )

20 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