Neural architectures for unifying brightness perception and image processing [Elektronische Ressource] / Matthias Sven Keil

Sujets

• Informatik
• Physik
• Biologie

Informations

• Publié par: universitat_ulm
• Publié le: 01 janvier 2003
• Nombre de lectures: 20
• Langue: English
• Poids de l'ouvrage: 8 Mo

Extrait

Neural Architectures for Unifying
Brightness Perception and Image
Processing
Instituto de Optica (CSIC)
Image and Vision Department
Serrano 121, E-28006 Madrid (Spain)
and
Abteilung Neuroinformatik
Fakult at fur Informatik
Universit at Ulm
Albert-Einstein-Allee, D-89069 Ulm (Germany)
Dissertation zur Erlangung des Doktorgrades Dr.rer.nat.
der Fakultat fur Informatik der Universitat Ulm
Matthias Sven Keil aus Hof an der Saale
(erschienen 2002)Amtierender Dekan: Prof. Dr. F. W. von Henke
Gutachter 1: Prof. Dr. Heiko Neumann
Gutachter 2: Prof. Dr. Gunther Palm
Gutachter 3: Dr. Gabriel Crist obal
Tag der Promotion: 16. Juni 2003Contents
1 Biophysical Principles 6
1.1 Biological neurons and the equivalent circuit . . . . . . . . . . . . . . 6
1.2 The membrane equation of a passive neuron . . . . . . . . . . . . . . 7
1.2.1 Synaptic input . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Realistic vs. abstract modeling of biological neurons . . . . . . . . . 13
1.3.1 Spike rate vs. mean ring rate . . . . . . . . . . . . . . . . . 13
1.3.2 Driving Potential vs. Potential-Independent Synaptic Input . 14
1.4 Dendrites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 An Introduction to Brightness Perception 20
2.1 Luminance, brightness, and the visual pathway . . . . . . . . . . . . 21
2.2 Retinal Ganglion cells constitute the retinal output . . . . . . . . . . 22
2.2.1 Retinal ganglion cells respond to luminance contrasts . . . . 22
2.2.2 The di erence-of-Gaussian (DOG) model . . . . . . . . . . . 22
2.2.3 Nonlinearly summing ganglion cells . . . . . . . . . . . . . . . 23
2.2.4 Ganglion cells in the primate retina . . . . . . . . . . . . . . 23
2.3 Beyond the retina - cortical representations of surfaces . . . . . . . . 26
2.3.1 Viewing brightness perception as a coding problem . . . . . . 26
2.3.2 Cortical surface representations . . . . . . . . . . . . . . . . . 26
2.3.3 Creating - the lling-in hypothesis . . 27
2.3.4 Neurophysiological correlate for lling-in . . . . . . . . . . . . 29
2.3.5 Filling-in models of brightness perception . . . . . . . . . . . 30
2.3.6 Formal description of standard lling-in . . . . . . . . . . . . 32
2.3.7 Filling-in and inverse problems . . . . . . . . . . . . . . . . . 32
2.3.8 Standard lling-in is a special case of con dence-based lling-in 34
2.4 Models for brightness perception and the anchoring problem . . . . . 34
2.4.1 An extra luminance-driven or low-pass channel . . . . . . . . 35
2.4.2 Superimposing band-pass lters . . . . . . . . . . . . . . . . . 37
2.4.3 Directional lling-in (1-D) . . . . . . . . . . . . . . . . . . . . 39
2.4.4 The multiplexed retinal code - a novel approach . . . . . . . 39
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
23 Novel Retinal Models 41
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2 The luminance correlation coe cien t (LCC) . . . . . . . . . . . . . . 42
3.3 The standard model of retinal ganglion cells . . . . . . . . . . . . . . 43
3.3.1 Di eren tial equations for the membrane potential . . . . . . . 43
3.3.2 Steady-state solutions . . . . . . . . . . . . . . . . . . . . . . 44
3.3.3 Choice of receptive eld parameter . . . . . . . . . . . . . . . 45
3.3.4 Positions of retinal responses relative to luminance dis-
continuities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.5 Luminance correlation coe cien t with the standard model . . 48
3.4 How luminance information may be passed into the cortex . . . . . . 49
3.4.1 Neurophysiological evidence - the extensive disinhibitory sur-
round (DIR) . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4.2 Incorporating the three-Gaussian model into the standard
retinal model . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.5 Novel retinal models - multiplexing contrast and luminance in parallel
channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5.1 A novel model for retinal ganglion cells . . . . . . . . . . . . 53
3.5.2 Model I - divisive gain control . . . . . . . . . . . . . . . . . . 56
3.5.3 Model II - multiplicative gain control . . . . . . . . . . . . . . 59
3.5.4 Improving the modulation depth of the multiplicative gain
control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.5.5 Model III - saturating multiplicative gain control . . . . . . . 62
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4 A New Role for Even Simple Cells 65
4.1 A brief overview of the proposed architecture . . . . . . . . . . . . . 65
4.2 Formal description of the texture system . . . . . . . . . . . . . . . . 67
4.2.1 Orientation selectivity as quasi one dimensional framework . 67
4.2.2 Detecting even symmetric features (\texture") . . . . . . . . 70
4.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5 A Novel Nonlinear Filling-in Framework 79
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.1.1 Limitations of Filling-in . . . . . . . . . . . . . . . . . . . . . 80
5.2 Detecting odd symmetric features . . . . . . . . . . . . . . . . . . . . 84
5.2.1 Excitatory input into an odd symmetric simple cell . . . . . . 85
5.2.2 Inhibitory input into an odd simple cell . . . . . . 85
5.2.3 Odd symmetric cell . . . . . . . . . . . . . . . . . . . . . . . . 86
5.3 Gating of multiplexed activity by odd-cell activity . . . . . . . . . . 86
5.3.1 Combination of orientation channels . . . . . . . . . . . . . . 88
5.4 Generalized di usion operators . . . . . . . . . . . . . . . . . . . . . 89
5.5 BEATS lling-in di usion layer . . . . . . . . . . . . . . . . . . . . . 90
5.6 Exploring the parameter space for surface syncytia . . . . . . . . . . 934
5.7 Simulations of brightness illusions . . . . . . . . . . . . . . . . . . . . 97
5.7.1 Craik-O’Brien-Cornsweet e ect (COCE) . . . . . . . . . . . . 97
5.7.2 Grating induction . . . . . . . . . . . . . . . . . . . . . . . . 99
5.7.3 Chevreul’s illusion . . . . . . . . . . . . . . . . . . . . . . . . 106
5.7.4 A modi ed Chevreul illusion . . . . . . . . . . . . . . . . . . 108
5.7.5 Simultaneous brightness contrast . . . . . . . . . . . . . . . . 109
5.7.6 White’s e ect (Munker-White e ect) . . . . . . . . . . . . . . 111
5.7.7 Benary cross . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.7.8 The Hermann/Hering grid . . . . . . . . . . . . . . . . . . . . 116
5.7.9 The scintillating grid illusion . . . . . . . . . . . . . . . . . . 118
5.8 Surface representations with real-world images . . . . . . . . . . . . 118
5.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6 Recovering luminance gradients 132
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.2 Formal description of the gradient system . . . . . . . . . . . . . . . 133
6.2.1 Detecting linear and nonlinear luminance gradients . . . . . . 133
6.2.2 Recovering luminance gradients . . . . . . . . . . . . . . . . . 135
6.3 Simulations of brightness illusions . . . . . . . . . . . . . . . . . . . . 139
6.3.1 Mach bands . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
6.3.2 Sine wave gratings and Gabor patches . . . . . . . . . . . . . 147
6.4 Simulations with real-world images . . . . . . . . . . . . . . . . . . . 147
6.4.1 Varying the feature inhibition weight . . . . . . . . . . . . . . 149
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
7 Binding of surfaces, texture, and gradients 161
7.1 Combining maps computationally . . . . . . . . . . . . . . . . . . . . 161
7.2 Combining maps in the brain . . . . . . . . . . . . . . . . . . . . . . 162
8 Zusammenfassung (in German) 164
A Nonlinear Di usion 166
A.0.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
A.0.2 Formal Description . . . . . . . . . . . . . . . . . . . . . . . . 167
A.0.3 Global normalization by local interactions (dynamic normal-
ization) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
A.0.4 Nonlinear contrast extraction . . . . . . . . . . . . . . . . . . 176
A.0.5 Could dynamic normalization account for brightness phenom-
ena? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
A.0.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189An overview
The architecture for brightness processing proposed with the present work aims
to unify two seemingly diverging goals, that is image processing and brightness
perception. A successful uni cation has not been achieved so far, since models
which predict brightness phenomena only rarely produce meaningful results when
processing real-world images (although some results have been demonstrated, e.g.
[Sepp & Neumann, 1999]). On the other hand, models for image processing tasks
(typically coding or denoising), which often claim to provide some account to early
vision, fail to predict phenomena associated with brightness perception. Usually,
both model classes compute their output by superimposing processed lter outputs
over various scales and orientations, whereby lter outputs are processed in order