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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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## Biologically Inspired Intelligent Systems

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**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**• 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**Blue-Yellow RF**Same operation as Red- Green**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**Note Colorbar**Center Surround**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 Processing**Orientation Salience map Color Salience map Intensity Contrast Salience map**Still Attention Module**• Combine • Normalized Intensity Contrast Map • Normalized Orientation Maps • Normalized Color difference Map to form Still Attention Module**Orange trail marker no longer**stands out as it does in color image**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 • Optical flow**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**Motion- consider a bar moving to the right**y x At time t0 bar is moving to the right**(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**Stationary bar detection (x,y)**x Plot of a stationary bar y MOTION AS ORIENTATION**Stationary bar detection (x,y)**x Plot of a stationary bar - + - + - + y Simple cell response**Moving bar detection (x,t)**x Plot of a moving bar - + - + - + t TIME Spatial temporal receptive field**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)**M1n with no theta or phi component should detect a**stationary edge image at 0 degrees Red is negative, Blue is positive**M1ntheta should detect a stationary edge at theta degrees**Theta = 45 degrees Red is negative, blue is positive**M1ntheta_phi should detect a moving edge at theta degrees**moving at a rate phi Theta = 45 degrees Phi = 45 degrees Red is negative, Blue is positive**M2n with no theta or phi component should detect a**stationary line image at 0 degrees**M2ntheta_phi should detect a moving line at theta degrees**moving at a rate phi**Experimental ResultsFilter detects stationary edge at 0**degrees Note magnitude values for M1n For plane1, Max = .1 Min = -.1 For plane 21, Max = 1 Min = -1 Data block constant for all planes**Convolve each plane of data with corresponding filter plane**Result of filter would be summation of all the data plane responses

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