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Biologically Motivated Computer Vision Digital Image Processing Sumitha Balasuriya Department of Computing Science, University of Glasgow.

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Presentation on theme: "Biologically Motivated Computer Vision Digital Image Processing Sumitha Balasuriya Department of Computing Science, University of Glasgow."— Presentation transcript:

1 Biologically Motivated Computer Vision Digital Image Processing Sumitha Balasuriya Department of Computing Science, University of Glasgow

2 General Vision Problem Machine vision has been very successful in finding solutions to specific, well constrained problems such as optical character recognition or fingerprint recognition. In fact machine vision has surpassed human vision in many such closed domain tasks. However it is only in biology where we find systems that can handle unconstrained, diverse vision problems. How can a biological or machine system which just captures two dimensional visual information from a view of a cluttered field even attempt to reason with and function in the environment? An accurate detailed spatial model of the environment is difficult to compute and the whole problem of scene analysis is ill-posed. A problem is well posed if (1) a solution exists, (2) the solution is unique, (3) the solution depends continuously on the initial data (stability property).

3 Ill-posed problem ? Several possible solutions exist

4 The general vision problem isnt really solved in biology … For example I can't build an accurate spatial world model of the scene I look at... Biological systems have evolved to process visual data to extract just enough information to perform the reasoning for everyday tasks that are part of survival. Visual information is combined with higher level knowledge and other sensory modalities that constrain the reasoning in the solution space and finally makes vision possible.

5 Visual cortex and a bit more … Lower visual cortex Direct feedback projections to V1 originate from: V2 (complex features) V3 (orientation, motion, depth) V4 (colour, attention) MT (motion) MST (motion) FEF (saccades, spatial memory) LIP (saccade planning) IT (recognition) Feedback from higher cortical areas Frontal cortex V2, V4, FEF, IT V1 Face features V1

6 Newborn kittens Placed in a carousel One active, other passively towed along Both receive same stimulation The actively moving kitten receives visual stimulation which results from its own movements Only the active kitten develops sensory-motor coordination. Held and Hein, 1963

7 Conventional Computer Vision Architecture Feature Extraction Input Action Classification, Recognition, Disparity Output

8 The Future - Biologically Motivated Computer Vision Architecture Hierarchical processing Input More abstract features / symbols Square triangle st Feedback processing Lateral processing Is there a square, triangle or circle? Feedforward processing Optical illusions Other modalities

9 Biologically Motivated Computer Vision Architectures in action Simple colour cues. Foveated sensors. Also: Learnt arm control, Learn how to act on objects

10 Biologically Inspired features Machine vision and biological vision systems process similar information (visual scenes) and perform similar tasks (recognition, targeting) Not surprisingly the optimal features that are extracted by many machine vision system look surprising like those found in biology But first ….

11 11 Why bother with feature extraction? Why not use the actual image/video itself for reasoning/analysis? INVARIANCE! The information we extract (i.e. the features) from the entity must be insensitive to changes. The extracted features might be invariant to rotation and scaling of objects in images, lighting conditions, partial occlusions

12 What features should we extract? Depends…. Modality (video/image/audio …) Task (eg: topic categorisation/face recognition/ audio compression) Dimensionality reduction / sparsification Invariance vs descriptiveness If the features are too descriptive they cant generalise to new examples If they generalise to much – everything looks just about the same As the feature we extract becomes more complex/descriptive it will also become less invariant to even minor changes in the entity that we are measuring.

13 Human visual pathway Inspiration for feature extraction methodology Circularly symmetric retinal ganglion receptive fields Receptive field: area in the FOV in which stimulation leads to a response in the neuron Orientated simple cell cortical receptive fields (similar to Gabor filter)

14 Gabor filter A function f(t) can be decomposed into cosine (even) and sine (odd) functions. Good for defining periodic structures. Not localised. There is an uncertainty relation between a signals specificity in time and frequency. Dennis Gabor defined a family of signals that optimised this trade-off Enables us to extract local features Daugman(1995) defined a 2D filter based on the above which was called a Gabor filter These filters resemble cortical simple cells

15 Gabor filter Localise the sine and cosine functions using a Gabor envelope. Gaussian envelope Modulating cosineModulating sine Even symmetric cosine Gabor wavelet Odd symmetric sine Gabor wavelet Gaussian envelope Assuming symmetric Gaussian envelope In the Fourier domain the Gabor is a Gaussian centred about the central frequency (U,V). The orientation of the Gabor in the spatial domain is σ U,V u v

16 Spatial Frequency Bandwidth Bandwidth at half power point Bandwidth depends on symmetric Gaussian envelopes sigma. Large sigma results in narrow bandwidth at the Gabor filter exactly filters at its central frequency. Also due to the uncertainty relation a narrow frequency bandwidth will result in reduced spatial localisation by the filter. SpatialSpectral (Fourier) frequency Wide bandwidthNarrow bandwidth Odd symmetric sine Gabor wavelet Even symmetric cosine Gabor wavelet Spatial filter profile

17 Gabor filter with asymmetric Gaussian However the Gabors Gaussian envelope need not be circular symmetric! An elliptical spatial Gaussian envelope lets us control orientation bandwidth. Better formulation for asymmetric Gaussian envelope Spatial domain along direction of wave propagation Spectral domain along direction of wave propagation f o = central frequency θ = angle γ = sigma in direction of propagation η = sigma perpendicular to direction of propagation Fourier domain

18 Bandwidth of Gabor with asymmetric Gaussian Half power points Along direction of wave propagation, Perpendicular to direction of wave propagation, Spatial bandwidth perpendicular to wave propagation Spatial bandwidth in direction of wave propagation

19 Orientation Bandwidth Orientation bandwidth is related to the number of orientations we want to extract. The half power points of the filters should coincide in the spectral domain. u v Orientation bandwidth Spatial frequency bandwidth Half power ωoωo Δθ If the filter bank consists of k orientated filters, and redundancy in orientation sampling l=rθ small θ

20 Orientation Bandwidth u v Orientation bandwidth Spatial frequency bandwidth Half power ωoωo Δθ Spatial domain Frequency domain Filter bank

21 Hypercolumn Experiments by Hubel and Weisel (1962,1968) A set of orientation selective units over a common patch of the FOV. Organised as a vertical column in the visual cortex In computational system use information in hypercolumn for higher level reasoning Feature vector Only using the even symmetric component in the filter bank

22 Properties of the hypercolumn feature vector Invariance to rotation in image plane Even symmetric detector Hypercolumn responses stimulation

23 Cycle to canonical orientation Invariance to rotation in image plane Cycle responses in feature vector stimulation

24 Properties of the hypercolumn feature vector Invariance to scaling (i.e. spatial frequency) central frequency stimulation

25 Scale Invariance Feature Transform Pandemonium model (Selfridge, 1959!) Build ever more complex / abstract features along the hierarchy Aggregate hypercolumn feature vectors to complex feature

26 SIFT features Hypercolumn features Complex feature vector Rotate hypercolumn features to canonical of large support region Rotate descriptor canonical of large support region

27 Recognition Extract SIFT features at corner locations (Harris corner detector), and scale space peaks Training Recognition

28 Recap Biologically motivated computer vision architecture Feedforward, feedback, lateral processing in architecture Hierarchical processing Feature extraction provides information about entities which are (somewhat!) invariant to changes Gabor filter Hypercolumn feature vector. SIFT features

29 The End

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