Biomimetic Motion Detection

We present the results of a biomimetic model of motion detection in the insect visual system based on an elaborated correlational elementary motion detector. This model incorporates a number of elements known, or predicted, to be in the insect motion processing pathway. The results show that this model greatly diminishes velocity ambiguity across images from different environments. Due to the nature of the algorithms underlying the model it lends itself to implementation in either digital or analogue hardware including neuromorphic analogue VLSI. The successful application of this algorithm has applications in the development of miniature autonomous systems in defence and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors.

[1]  Alexander Borst,et al.  Mechanisms of dendritic integration underlying gain control in fly motion-sensitive interneurons , 1995, Journal of Computational Neuroscience.

[2]  Zhang,et al.  Honeybee navigation en route to the goal: visual flight control and odometry , 1996, The Journal of experimental biology.

[3]  D. G. Stavenga,et al.  Spectral sensitivity of blowfly photoreceptors: Dependence on waveguide effects and pigment concentration , 1986, Vision Research.

[4]  Robert A. Harris,et al.  Adaptation and the temporal delay filter of fly motion detectors , 1999, Vision Research.

[5]  C. W. G Clifford,et al.  Fundamental mechanisms of visual motion detection: models, cells and functions , 2002, Progress in Neurobiology.

[6]  D. Tolhurst,et al.  Amplitude spectra of natural images , 1992 .

[7]  A. Straw,et al.  A `bright zone' in male hoverfly (Eristalis tenax) eyes and associated faster motion detection and increased contrast sensitivity , 2006, Journal of Experimental Biology.

[8]  Simon B. Laughlin,et al.  The Role of Natural Image Statistics in Biological Motion Estimation , 2000, Biologically Motivated Computer Vision.

[9]  Derek Abbott,et al.  Implementation of saturation for modelling pattern noise using naturalistic stimuli , 2006, SPIE Micro + Nano Materials, Devices, and Applications.

[10]  A. Borst,et al.  A look into the cockpit of the fly: visual orientation, algorithms, and identified neurons , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  B. Hassenstein,et al.  Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus , 1956 .

[12]  R. Hengstenberg,et al.  Binocular contributions to optic flow processing in the fly visual system. , 2001, Journal of neurophysiology.

[13]  Martin Egelhaaf,et al.  Neural Mechanisms of Visual Course Control in Insects , 1989 .

[14]  M. Ibbotson,et al.  An adaptive Reichardt detector model of motion adaptation in insects and mammals , 1997, Visual Neuroscience.

[15]  David C. O'Carroll,et al.  A neuromorphic model for a robust, adaptive photoreceptor reduces variability in correlation based motion detectors , 2006 .

[16]  D. Stavenga Angular and spectral sensitivity of fly photoreceptors. I. Integrated facet lens and rhabdomere optics , 2002, Journal of Comparative Physiology A.

[17]  J. H. van Hateren,et al.  A theory of maximizing sensory information , 2004, Biological Cybernetics.

[18]  Klaus Hausen,et al.  Motion sensitive interneurons in the optomotor system of the fly , 1982, Biological Cybernetics.

[19]  W. Reichardt,et al.  Autocorrelation, a principle for the evaluation of sensory information by the central nervous system , 1961 .

[20]  Patrick A. Shoemaker,et al.  Bio-inspired optical rotation sensor , 2006, SPIE Micro + Nano Materials, Devices, and Applications.

[21]  K Hausen,et al.  Decoding of retinal image flow in insects. , 1993, Reviews of oculomotor research.

[22]  Derek Abbott,et al.  Effect of spatial sampling on pattern noise in insect-based motion detection , 2005, SPIE Micro + Nano Materials, Devices, and Applications.

[23]  S. Laughlin,et al.  Adaptation of the motion-sensitive neuron H1 is generated locally and governed by contrast frequency , 1985, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[24]  Eng-Leng Mah,et al.  Bio-inspired analog circuitry model of insect photoreceptor cells , 2006, SPIE Micro + Nano Materials, Devices, and Applications.

[25]  Robert A. Harris,et al.  Contrast Gain Reduction in Fly Motion Adaptation , 2000, Neuron.

[26]  K. Hausen The Lobula-Complex of the Fly: Structure, Function and Significance in Visual Behaviour , 1984 .

[27]  K. Hausen Motion sensitive interneurons in the optomotor system of the fly , 1982, Biological Cybernetics.

[28]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[29]  Said Al-Sarawi,et al.  Review of bioinspired real-time motion analysis systems , 2005, SPIE Micro + Nano Materials, Devices, and Applications.

[30]  Patrick A. Shoemaker,et al.  Velocity constancy and models for wide-field visual motion detection in insects , 2005, Biological Cybernetics.

[31]  R. Pierantoni,et al.  A look into the cock-pit of the fly , 1976, Cell and Tissue Research.

[32]  David C. O'Carroll,et al.  Bio-inspired pixel-wise adaptive imaging , 2006, SPIE Micro + Nano Materials, Devices, and Applications.

[33]  Sreeja Rajesh,et al.  Modeling pattern noise in responses of fly motion detectors to naturalistic scenes , 2005, SPIE Micro + Nano Materials, Devices, and Applications.

[34]  M. Egelhaaf,et al.  Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths , 2005, Journal of Comparative Physiology A.

[35]  R O Dror,et al.  Accuracy of velocity estimation by Reichardt correlators. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[36]  J. P. Lindemann,et al.  Function of a Fly Motion-Sensitive Neuron Matches Eye Movements during Free Flight , 2005, PLoS biology.

[37]  M. Egelhaaf,et al.  Vision in flying insects , 2002, Current Opinion in Neurobiology.

[38]  H. P. Snippe,et al.  Phototransduction in primate cones and blowfly photoreceptors: different mechanisms, different algorithms, similar response , 2005, Journal of Comparative Physiology A.

[39]  J. H. Hateren,et al.  Information theoretical evaluation of parametric models of gain control in blowfly photoreceptor cells , 2001, Vision Research.