Biologically Inspired Intelligent Systems. Lecture 07 Dr. Roger S. Gaborski. Feature Maps. Intensity contrast (HW#3) Orientation (HW#4) 0, 45, 90 and 135 degrees Color Information (HW#5) Red-Green opponent color Blue-Yellow opponent color. Processing of Color Data.
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Feature Maps • Intensity contrast (HW#3) • Orientation (HW#4) • 0, 45, 90 and 135 degrees • Color Information (HW#5) • Red-Green opponent color • Blue-Yellow opponent color
Processing of Color Data • How is color coded in primates? • Three classes of cone photoreceptors • Arrangement of cone types seems to be random • Color opponent cells (cone signals brought together in opposition) • Red-green • Blue-yellow
Red-Green RF White overlap Red in center Green in surround | | | | | | | | | | | | | | | | | | | | |
Red-Green RF Green in center Red in center Green in center Green in surround Red in surround | | | | | | ||||||||||||||||| NO RESPONSE
Color Channels (approximation) • Create broadly tuned color channels: R = r-(g+b)/2 G = g- (r+b)/2 B = b- (r+g)/2 Y = r+g – 2(|r-g| + b) (negative values set to zero) • Maximum response to pure hue the channel is tuned to (if a pixel contains both r and g it will have a smaller R response than if it only had r) • Zero response to white or black inputs
Color Difference Map • Create red-green, blue-yellow color channels • For each color plane, apply receptive fields at different scales, 7x7, 15x15, 31x31, 63x63… • Combine color receptive fields output to form color difference Map
Potential Red-Green RF Model Create Dog64 Red_Kernel: Green_Kernel R_ctr=filter red data with Red_Kernel G_sur=filter green data with Green_Kernel Form difference: R_ctr- G_sur RED GREEN GREEN
Color opponent filters Red center Green surround Yellow center Blue surround Green center Red surround Blue center Yellow surround Gabor orientation filters Difference of Gaussian filters (Intensity contrast) On CenterOff Center 0 degree 45 degree 90 degree 135 degree Still Attention Processing Image
Still Attention Module • Combine • Normalized Intensity Contrast Map • Normalized Orientation Maps • Normalized Color difference Map to form Still Attention Module
Partial model WHERE n = 0, 45, 90 and 135 degrees n degrees 7x7 Gabor n degrees 15x15 Gabor n degrees 31x31 Gabor n degrees 7x7 Gabor n degrees 15x15 Gabor n degrees 31x31 Gabor n degrees 7x7 Gabor n degrees 15x15 Gabor n degrees 15x15 Gabor R-G B-Y Contrast Images imCon8 imCon16 imCon32 Retina Model 8x8, 16x16 and 32x32 circular receptive fields Color Opponent Image
Motion Detection • Types of motion • Object: Visual area V5 or MT (middle temporal), • Optical flow (MSTd –medial superior temporal/dorsal)
A Sense of time • We need temporal information to detect motion • Computer vision techniques: • Frame differencing • Pixel modeling (Gaussian) • ‘system’ level biological models
(x,t) Plot – Ignore y axis x Slope of blue bar represents the velocity that the bar is moving Can you relate this plot to a stationary bar with the same slope?? t Since object is moving vertical bar, nothing is lost with this representation
FASTER Moving bar detection (x,t) x Faster plot of a moving bar (moves more x distance in same amount of time t Spatial temporal (space and time) receptive field We need a separate spatiotemporal for each velocity
“Gaussian Derivative Model for spatial-temporal vision” (R.A. Young) • GENERAL EQUATION • Gn,o,p(x', y', t') = gn(x')go(y')gp(t') for n = 0,1 2,... • G - 3 D GD spatial-temporal filterx', y' and t' are the coordinate axes of Gg are one dimensional GD functionsn, o, and p are derivative numbers along x', y' and t' • Additional parameters allow placement of model, spatial orientation, optimal speed (see paper for details)
