A computational theory of visual receptive fields

Adelson E, Bergen J (1985) Spatiotemporal energy models for the perception of motion. J Opt Soc Am A2:284–299 Almansa A, Lindeberg T (2000) Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale-selection. IEEE Trans Image Process 9(12):2027–2042 Babaud J, Witkin AP, Baudin M, Duda RO (1986) Uniqueness of the Gaussian kernel for scale-space filtering. IEEE Trans Pattern Anal Mach Intell 8(1):3–26 Bardy C, Huang JY, Wang C, Fitzgibbon T, Dreher B (2006) ‘Simplification’ of responses of complex cells in cat striate cortex; suppressive surrounds and ’feedback’ inactivation. J Physiol 574(3):731–750 Baumberg A (2000) Reliable feature matching across widely separated views. In: Proceedings of the CVPR, Hilton Head, SC, vol I, pp 1774–1781 Bay H, Ess A, Tuytelaars T, van Gool L (2008) Speeded up robust features (SURF). Comput Vis Image Underst 110(3):346–359 Blasdel GG (1992) Orientation selectivity, preference and continuity in monkey striate cortex. J Neurosci 12(8):3139–3161 Bonhoeffer T, Grinvald A (1991) Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature 353:429–431 Bonin V, Mante V, Carandini M (2005) The suppressive field of neurons in the lateral geniculate nucleus. J Neurosci 25(47):10844–10856 Burghouts GJ, Geusebroek JM (2009) Performance evaluation of local colour invariants. Comput Vis Image Underst 113(1):48–62 Burt PJ (1981) Fast filter transforms for image processing. Comput Vis Graph Image Process 16:20–51 Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 9(4):532–540 Carandini M, Demb JB, Mante V, Tolhurst DJ, Dan Y, Olshausen BA, Gallant JL, Rust NC (2005) Do we know what the early visual system does. J Neurosci 25(46):10577–10597 Carslaw HS, Jaeger JC (1959) Conduction of heat in solids. Clarendon Press, Oxford Cavanaugh JR, Bair W, Movshon A (2001a) Nature and interaction of signals from the receptive field center and surround in Macaque V1 neurons. J Neurophysiol 88:2530–2546 Cavanaugh JR, Bair W, Movshon A (2001b) Selectivity and spatial distribution of signals from the receptive field surround in Macaque V1 neurons. J Neurophysiol 88:2547–2556 Chen Y, Anand S, Martinez-Conde S, Macknik SL, Bereshpolova Y, Swadlow HA, Alonso JM (2002) The linearity and selectivity of neuronal responses in awake visual cortex. J Vis 9(9):1–17 Chomat O, de Verdiere V, Hall D, Crowley J (2000) Local scale selection for Gaussian based description techniques. In: Proceedings of the ECCV’00, Lecture Notes in Computer Science, vol 1842. Springer, Dublin, Ireland I:117–133 Conway BR (2006) Spatial and temporal properties of cone signals in alert macaque primary visual cortex. J Neurosci 26(42):10826–10846 Crowley JL (1981) A representation for visual information. Ph.D. Thesis, Carnegie-Mellon University, Robotics Institute, Pittsburgh, Pennsylvania Crowley JL, Christensen HI (1994) Vision as process. Springer, Heidelberg Crowley JL, Parker AC (1984) A representation for shape based on peaks and ridges in the difference of low-pass transform. IEEE Trans Pattern Anal Mach Intell 6(2):156–170 Crowley JL, Sanderson AC (1987) Multiple resolution representation and probabilistic matching of 2-d gray-scale shape. IEEE Trans Pattern Anal Mach Intell 9(1):113–121 Crowley JL, Stern RM (1984) Fast computation of the difference of low pass transform. IEEE Trans Pattern Anal Mach Intell 6:212–222 DeAngelis GC, Anzai A (2004) A modern view of the classical receptive field: Linear and non-linear spatio-temporal processing by V1 neurons. In: Chalupa LM, Werner JS (eds) The visual neurosciences, vol 1. MIT Press, Cambridge, pp 704–719 DeAngelis GC, Ohzawa I, Freeman RD (1995) Receptive field dynamics in the central visual pathways. Trends Neurosci 18(10):451–457 Doi E, Lewicki MS (2005) Relations between the statistical regularities of natural images and the response properties of the early visual system. In: Japanese cognitive science society: Sig P & P. Kyoto University, pp 1–8 Duits R, Florack L, de Graaf J (2004) On the axioms of scale space theory. J Math Imaging Vis 22:267–298 Einhäuser W, König P (2010) Getting real—sensory processing of natural stimuli. Curr Opinn Neurobiol 20(3):389–395 Fagerström D (2005) Temporal scale-spaces. Int J Comput Vis 2–3:97–106 Fagerström D (2007) Spatio-temporal scale-spaces. In: Gallari F, Murli A, Paragios N (eds) Proceedings of the 1st international conference on scale-space theories and variational methods in computer vision, Lecture Notes in Computer Science, vol. 4485. Springer, pp 326–337 Faugeras O, Toubol J, Cessac B (2009) A constructive mean-field analysis of multi-population neural networks with random synaptic weights and stochastic inputs. Frontiers in Computational Neuroscience 3(1). doi:10.3389/neuro.10.001.2009 Felsberg M, Sommer G (2004) The monogenic scale-space: a unifying approach to phase-based image processing in scale-space. J Math Imaging Vis 21:5–26 Felsen G, Touryan J, Han F, Dan Y (2005) Cortical sensitivity to visual features in natural scenes. PLoS Biol 3(10):e342 Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4:2379–2394 Fleet DJ, Langley K (1995) Recursive filters for optical flow. IEEE Trans Pattern Anal Mach Intell 17(1):61–67 Florack L, Niessen W, Nielsen M (1998) The intrinsic structure of optic flow incorporating measurement duality. Int J Comput Vis 27(3):263–286 Florack LMJ (1997) Image structure. Series in Mathematical Imaging and Vision. Springer, Berlin Florack LMJ, ter Haar Romeny BM, Koenderink JJ, Viergever MA (1992) Images: regular tempered distributions. In: Ying Y, Toet A, Heijmanns H (eds) Proceedings NATO workshop ’Shape in Picture, NATO ASI Series F. Springer, New York, Driebergen, Netherlands, pp 651–659 Florack LMJ, ter Haar Romeny BM (1992) Scale and the differential structure of images. Image Vis Comput 10(6):376–388 Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906 Geisler WS (2008) Visual perception and the statistical properties of natural scenes. Annu Rev Psychol 59:10.1–10.26 Geusebroek JM, van den Boomgaard R, Smeulders AWM, Geerts H (2001) Color invariance. IEEE Trans Pattern Anal Mach Intell 23(12):1338–1350 Hall D, de Verdiere V, Crowley J (2000) Object recognition using coloured receptive fields. In: Proceedings of the ECCV’00, Lecture Notes in Computer Science, vol 1842. Springer, Dublin, Ireland I:164–177 Hartline HK (1938) The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. Am J Physiol 121:400–415 Heeger DJ (1992) Normalization of cell responses in cat striate cortex. Vis Neurosci 9:181–197 Hille E, Phillips RS (1957) Functional analysis and semi-groups, vol XXXI. American Mathematical Society Colloquium Publications, USA Hirschmann II, Widder DV (1955) The convolution transform. Princeton University Press, Princeton Horn BKP (1986) Robot vision. MIT Press, Cambridge Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat’s striate cortex. J Physiol 147:226–238 Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160:106–154 Hubel DH, Wiesel TN (2005) Brain and visual perception: the story of a 25-year collaboration. Oxford University Press, Oxford Hyvärinen A, Hurri J, Hoyer PO (2009) Natural image statistics: a probabilistic approach to early computational vision. Computational imaging and vision. Springer, Berlin Ibbitson MR, Price NSC, Crowder NA (2005) On the division of cortical cells into simple and complex types: a comparative viewpoint. J Neurophysiol 93:3699–3702 Iijima T (1962) Observation theory of two-dimensional visual patterns. Technical report. Papers of technical group on automata and automatic control, IECE, Japan Jaynes ET (1968) Prior probabilities. Trans Syst Sci Cybern 4(3):227–241 Jones J, Palmer L (1987) An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58:1233–1258 Jones J, Palmer L (1987) The two-dimensional spatial structure of simple receptive fields in cat striate cortex. J Neurophysiol 58:1187–1211 Kagan I, Gur M, Snodderly DM (2002) Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings. J Neurophysiol 88:2557–2574 Koch C (1999) Biophysics of computation: information processing in single neurons. Oxford University Press, Oxford Koenderink JJ (1984) The structure of images. Biol Cybern 50:363–370 Koenderink JJ (1988) Scale-time. Biol Cybern 58:159–162 Koenderink JJ, Kaeppers A, van Doorn AJ (1992) Local operations: the embodiment of geometry. In: Orban G, Nagel HH (eds) Artificial and biological vision systems, pp 1–23 Koenderink JJ, van Doorn AJ (1978) Visual detection of spatial contrast; influence of location in the visual field, target extent and illuminance level. Biol Cybern 30:157–167 Koenderink JJ, van Doorn AJ (1987) Representation of local geometry in the visual system. Biol Cybern 55:367–375 Koenderink JJ, van Doorn AJ (1990) Receptive field families. Biol Cybern 63:291–298 Koenderink JJ, van Doorn AJ (1992) Generic neighborhood operators. IEEE Trans Pattern Anal Mach Intell 14(6):597–605 Kokkinos I, Yuille A (2008) Scale invariance without scale selection. In: Proceedings of the CVPR, pp 1–8 Kuffler SW (1953) Discharge patterns and functional organization of mammalian retina. J Neurophysiol 16(1):37–68 Land EH (1974) The retinex theory of colour vision. Proc R Inst Great Britain 57:23–58 Land EH (1986) Recent advances in retinex theory. Vis Res 26(1):7–21 Laptev I, Caputo B, Schuldt C, Lindeberg T (2007) Local velocity-adapted motion events for spatio-temporal recognition. Comput Vis Image Underst 108:207–229 Laptev I, Lindeberg T (2003) Space–time interest points. In: Proceedings of the 9th international conference on computer vision, Nice, France, pp 432–439 Laptev I, Lindeberg, T (2004a) Local descriptors for spatio-temporal recognition. In: Proceedings of the ECCV’04 workshop on spatial coherence for visual motion analysis, Lecture Notes in Computer Science, vol 3667. Springer, Prague, Czech Republic, pp 91–103 Laptev I, Lindeberg T (2004) Velocity-adapted spatio-temporal receptive fields for direct recognition of activities. Image Vis Comput 22(2):105–116 Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 27(8):1265–1278 Lifshitz L, Pizer S (1990) A multiresolution hierarchical approach to image segmentation based on intensity extrema. IEEE Trans Pattern Anal Mach Intell 12:529–541 Linde O, Lindeberg T (2004) Object recognition using composed receptive field histograms of higher dimensionality. In: International conference on pattern recognition, vol. 2, Cambridge, pp 1–6 Linde O, Lindeberg T (2012) Composed complex-cue histograms: an investigation of the information content in receptive field based image descriptors for object recognition. Comput Vis Image Underst 116:538–560 Lindeberg T (1990) Scale-space for discrete signals. IEEE Trans Pattern Anal Mach Intell 12(3):234–254 Lindeberg T (1994a) Scale-space theory: a basic tool for analysing structures at different scales. J Appl Stat 21(2):225–270. Also available from http://www.csc.kth.se/tony/abstracts/Lin94-SI-abstract.html Lindeberg T (1994) Scale-Space Theory in Computer Vision. Springer, The Springer International Series in Engineering and Computer Science Lindeberg T (1996) On the axiomatic foundations of linear scale-space. In: Sporring J, Nielsen M, Florack L, Johansen P (eds) Gaussian scale-space theory: proceedings of the PhD School on scale-space theory. Springer, Copenhagen, Denmark Lindeberg T (1997) Linear spatio-temporal scale-space. In: ter Haar Romeny BM, Florack LMJ, Koenderink JJ, Viergever MA (eds) Scale-space theory in computer vision: proceedings of the first international conference Scale-Space’97, Lecture Notes in Computer Science, vol 1252. Springer, Utrecht, The Netherlands, pp 113–127. Extended version available as technical report ISRN KTH NA/P-01/22-SE from KTH. Lindeberg T (1997) On automatic selection of temporal scales in time-casual scale-space. In: Sommer G, Koenderink JJ (eds) Proceedings of the AFPAC’97: algebraic frames for the perception-action cycle, Lecture Notes in Computer Science vol 1315. Springer, Kiel, Germany, pp 94–113 Lindeberg T (1998) Edge detection and ridge detection with automatic scale selection. Int J Comput Vis 30(2):117–154 Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):77–116 Lindeberg T (1999) Principles for automatic scale selection. In: Handbook on computer vision and applications. Academic Press, Boston, USA, pp 239–274. Also available from http://www.csc.kth.se/cvap/abstracts/cvap222.html Lindeberg T (2001) Linear spatio-temporal scale-space. report, ISRN KTH/NA/P-01/22-SE, Department of Numerical Analysis and Computing Science, KTH Lindeberg T (2002) Time-recursive velocity-adapted spatio-temporal scale-space filters. In: Johansen P (ed) Proceedings of the ECCV’02, Lecture Notes in Computer Science, vol 2350. Springer, Copenhagen, Denmark, pp 52–67 Lindeberg T (2008) Scale-space. In: Wah B (ed) Encyclopedia of computer science and engineering. Wiley, Hoboken, pp 2495–2504 Lindeberg T (2011) Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space. J Math Imaging Vis 40(1):36–81 Lindeberg T (2013) Scale selection. In: Encyclopedia of computer vision. Springer (in press) Lindeberg T, Akbarzadeh A, Laptev I (2004) Galilean-corrected spatio-temporal interest operators. In: International conference on pattern recognition, Cambridge, I:57–62 Lindeberg T, Fagerström D (1996) Scale-space with causal time direction. In: Proceedings of the ECCV’96, vol 1064. Springer, Cambridge, UK, pp 229–240 Lindeberg T, Florack L (1992) On the decrease of resolution as a function of eccentricity for a foveal vision system. report, ISRN KTH/NA/P-92/29-SE, Department of Numerical Analysis and Computing Science, KTH Lindeberg T, Florack L (1994) Foveal scale-space and linear increase of receptive field size as a function of eccentricity. report, ISRN KTH/NA/P-94/27-SE, Department of Numerical Analysis and Computing Science, KTH. Available from http://www.csc.kth.se/tony/abstracts/CVAP166.html Lindeberg T, Gårding J (1997) Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D structure. Image Vis Comput 15:415–434 Lörincz A, Palotal Z, Szirtes G (2012) Efficient sparse coding in early sensory processing: lessons from signal recovery. PLoS Comput Biol 8(3)(e1002372) doi:10.1371/journal.pcbi.1002372 Lowe D (1999) Object recognition from local scale-invariant features. In: Proceedings of the 7th international conference on computer vision, Corfu, Greece, pp 1150–1157 Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110 Marcelja S (1980) Mathematical description of the responses of simple cortical cells. J Opt Soc Am 70(11):1297–1300 Martin PR, Grünert U (2004) Ganglion cells in mammalian retinae. In: Chalupa LM, Werner JS (eds) The visual neurosciences, vol 1. MIT Press, Cambridge, pp 410–421 Martinez LM, Alonso JM (2003) Complex receptive fields in primary visual cortex. Neuroscientist 9(5):317–331 Mata ML, Ringach DL (2005) Spatial overlap of ON and OFF subregions and its relation to response modulation ratio in Macaque primary visual cortex. J Neurophysiol 93:919–928 Mattia M, Guidice PD (2002) Population dynamics of interacting spiking neurons. Phys Rev E 65(5):051917 Mechler F, Ringach DL (2002) On the classification of simple and complex cells. Vis Res 22:1017–1033 Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86 Nagel H, Gehrke A (1998) Spatiotemporal adaptive filtering for estimation and segmentation of optical flow fields. In: Proceedings of the ECCV’98. Springer, Freiburg, Germany, pp 86–102 Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. J Optl Soc Am 381:607–609 Olshausen BA, Field DJ (2004) What is the other 85 % of V1 doing. In: Sejnowski TJ, van Hemmen L (eds) Problems in systems neuroscience. Oxford University Press, Oxford Omurtag A, Knight BW, Sirovich L (2000) On the simulation of large populations of neurons. J Comput Neurosci 8:51–63 Palmer SE (1999) Vision science: photons to phenomenology first edition. MIT Press, Cambridge Pauwels EJ, Fiddelaers P, Moons T, van Gool LJ (1995) An extended class of scale-invariant and recursive scale-space filters. IEEE Trans Pattern Anal Mach Intell 17(7):691–701 Pazy A (1983) Semi-groups of linear operators and applications to partial differential equations. Applied Mathematical Sciences. Springer, Berlin Perona P (1992) Steerable-scalable kernels for edge detection and junction analysis. Image Vis Comput 10:663–672 Priebe NJ, Mechler F, Carandini M, Ferster D (2004) The contribution of spike threshold to the dichotomy of cortical simple and complex cells. Nat Neurosci 7(10):1113–1122 Rao RPN, Ballard DH (1998) Development of localized oriented receptive fields by learning a translation-invariant code for natural images. Comput Neural Syst 9(2):219–234 Reichardt WE (1961) Autocorrelation: a principle for the evaluation of sensory information by the central nervous system. In: Rosenblith WA (ed) Sensory communication. MIT Press, Cambridge, pp 303–317 Reichardt WE, Schögl RW (1988) A two dimensional field theory for motion computation. Biol Cybern 60:23–35 Ringach DL (2002) Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. J Neurophysiol 88:455–463 Ringach DL (2004) Mapping receptive fields in primary visual cortex. J Physiol 558(3):717–728 Ringach DL, Bredfeldt CE, Shapley RM, Hawken MJ (2002) Suppression of neural responses to nonoptimal stimuli correlates with tuning selectivity in Macaque V1. J Neurophysiol 87: 1018–1027 Ringach DL, Hawken MJ, Shapley R (2002) Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. J Vis 2(1):12–24 Rodieck RW (1965) Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vis Res 5(11):583–601 Rothganger F, Lazebnik S, Schmid C, Ponce J (2006) 3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. Int J Comput Vis 66(3):231–259 Rust NC, Schwartz O, Movshon JA, Simoncelli EP (2005) Spatiotemporal elements of V1 receptive fields. Neuron 46(6):945–956 Schaffalitzky F, Zisserman A (2001) Viewpoint invariant texture matching and wide baseline stereo. In: Proceedings of the 8th international conference on computer vision, Vancouver, Canada, II:636–643 Schiele B, Crowley J (1996) Object recognition using multidimensional receptive field histograms. In: Proceedings of the ECCV’96, Lecture Notes in Computer Science, vol 1064. Springer, Cambridge, UK, pp 610–619 Schiele B, Crowley J (2000) Recognition without correspondence using multidimensional receptive field histograms. Int J Comput Vis 36(1):31–50 Schwartz O, Chichilnsky EJ, Simoncelli EP (2002) Characterizing neural gain control using spike-triggered covariance. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems, vol 14. MIT Press, Cambridge, pp 269–276 Schwartz O, Simoncelli EP (2001) Natural signal statistics and sensory gain control. Nat Neurosci 4:819–825 Sherrington CS (1906) The integrative action of the nervous system. C Scribner and Sons, New York Simoncelli EP, Freeman WT, Adelson EH, Heeger DJ (1992) Shiftable multi-scale transforms. IEEE Trans Inf Theory 38(2) Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representations. Annu Rev Neurosci 24:1193–1216 Somers DC, Nelson SB, Sur M (1995) An emergent model of orientation selectivity in cat visual cortical simple cells. J Neurosci 15(8):5448–5465 Sompolinsky H, Shapley R (1997) New perspectives on the mechanisms for orientation selectivity. Curr Opin Neurobiol 7:514–522 Sporring J, Nielsen M, Florack L, Johansen P (eds) (1996) Gaussian Scale-Space Theory: Proc. PhD School on Scale-Space Theory. Series in Mathematical Imaging and Vision. Springer, Copenhagen, Denmark Stork DG, Wilson HR (1990) Do Gabor functions provide appropriate descriptions of visual cortical receptive fields. J Opt Soc Am 7(8):1362–1373 ter Haar Romeny B, Florack L, Nielsen, M (2001) Scale-time kernels and models. In: Scale-space and morphology: proceedings of the scale-space’01, Lecture Notes in Computer Science. Springer, Vancouver, Canada ter Haar Romeny B (2003) Front-end vision and multi-scale image analysis. Springer, Berlin Touryan J, Lau B, Dan Y (2002) Isolation of relevant visual features from random stimuli for cortical complex cells. J Neurosci 22(24):10811–10818 Tsotsos J (1995) Modeling visual attention via selective tuning. Artif Intell 78(1–2):507–545 Tuytelaars T, van Gool L (2004) Matching widely separated views based on affine invariant regions. Int J Comput Vis 59(1):61–85 Valois RLD, Cottaris NP, Mahon LE, Elfer SD, Wilson JA (2000) Spatial and temporal receptive fields of geniculate and cortical cells and directional selectivity. Vis Res 40(2):3685–3702 van der Schaaf, van Hateren JH (1996) Modelling the power spectra of natural images: statistics and information. Vis Res 36(17):2759–2770 van de Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596 Wässle H (2004) Parallel processing in the mammalian retina. Nat Rev Neurosci 5:747–757 Watanabe M, Rodieck RW (1989) Parasol and midget ganglion cells in the primate retina. J Comput Neurol 289:434–454 Weickert J (1998) Anisotropic diffusion in image processing. Teubner-Verlag, Stuttgart Weickert J, Ishikawa S, Imiya A (1999) Linear scale-space has first been proposed in Japan. J Math Imaging and Vis 10(3):237–252 Willems G, Tuytelaars T, van Gool L (2008) An efficient dense and scale-invariant spatio-temporal interest point detector. In: Proceedings of the ECCV’08, Lecture Notes in Computer Science, vol 5303. Springer, Marseille, France, pp 650–663 Williams PE, Shapley RM (2007) A dynamic nonlinearity and spatial phase specificity in macaque V1 neurons. J Neurosci 27:5706–5718 Witkin AP (1983) Scale-space filtering. In: Proceedings of the 8th international joint conference on artificial intelligence, Karlsruhe, Germany, pp 1019–1022 Young RA (1987) The Gaussian derivative model for spatial vision: I. Retinal mechanisms. Spatial Vis 2:273–293 Young RA, Lesperance RM (2001) The Gaussian derivative model for spatio-temporal vision: II. Cortical data. Spatial Vis 14(3,4):321–389 Young RA, Lesperance RM, Meyer WW (2001) The Gaussian derivative model for spatio-temporal vision: I. Cortical model. Spatial Vis 14(3,4):261–319 Yuille AL, Poggio TA (1986) Scaling theorems for zero-crossings. IEEE Trans Pattern Anal Mach Intell 8:15–25 Zelnik-Manor L, Irani M (2001) Event-based analysis of video. In: Proceedings of the CVPR, Kauai Marriott, Hawaii, II:123–130