An Oscillatory Neural Network Model of Object Selection and Segmentation in the Visual Scene

An oscillatory neural network model is presented that allows selection of a specified object from the image. The processing of the image is divided into two stages. The first stage implements contour extraction by applying traditional image processing algorithms. The result is raw contours accompanied by the noise and some spurious objects. During the second stage the searched object is segmented from the image, its boundaries are determined, and the noise is suppressed. The second stage is implemented by a two layer network of phase oscillators controlled by a central oscillator. The extraction of an object is made in terms of the temporal correlation hypothesis. The oscillators coding the selected object form an assembly with coherent activity which also runs in phase with the central oscillator. Other oscillators do not show synchrony with this assembly. The work of the model is illustrated on an example of the image used in robotics.

[1]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[2]  Abderrahim Labbi,et al.  Visual object segmentation using FitzHugh–Nagumo oscillators , 2001 .

[3]  A. Treisman,et al.  Conjunction search revisited. , 1990, Journal of experimental psychology. Human perception and performance.

[4]  E. Niebur,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2022 .

[5]  Mauro Ursino,et al.  A model of contextual interactions and contour detection in primary visual cortex , 2004, Neural Networks.

[6]  Angelo Cangelosi,et al.  An open-source simulator for cognitive robotics research: the prototype of the iCub humanoid robot simulator , 2008, PerMIS.

[7]  Günther Palm,et al.  Scene Segmentation through Synchronization , 2005 .

[8]  S. Strogatz,et al.  CORRIGENDUM: Collective synchronisation in lattices of non-linear oscillators with randomness , 1988 .

[9]  T. Sejnowski,et al.  Synchronous oscillatory activity in sensory systems: new vistas on mechanisms , 1997, Current Opinion in Neurobiology.

[10]  LIANG ZHAO,et al.  A Network of Coupled Chaotic Maps for Adaptive Multi-Scale Image Segmentation , 2003, Int. J. Neural Syst..

[11]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Pixel clustering by adaptive pixel moving and chaotic synchronization , 2004, IEEE Trans. Neural Networks.

[13]  W. Singer,et al.  Oscillatory Neuronal Synchronization in Primary Visual Cortex as a Correlate of Stimulus Selection , 2002, The Journal of Neuroscience.

[14]  Giulio Sandini,et al.  The ITALK project: Integration and Transfer of Action and Language Knowledge in Robots , 2008 .

[15]  Liang Zhao,et al.  A network of dynamically coupled chaotic maps for scene segmentation , 2001, IEEE Trans. Neural Networks.

[16]  Mark E. Oxley,et al.  Physiologically motivated image fusion for object detection using a pulse coupled neural network , 1999, IEEE Trans. Neural Networks.

[17]  DeLiang Wang,et al.  Weight adaptation and oscillatory correlation for image segmentation , 2000, IEEE Trans. Neural Networks Learn. Syst..

[18]  B. C. Motter.  Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.

[19]  Daido,et al.  Lower critical dimension for populations of oscillators with randomly distributed frequencies: A renormalization-group analysis. , 1988, Physical review letters.

[20]  Alessandro Sarti,et al.  Binding and segmentation of multiple objects through neural oscillators inhibited by contour information , 2003, Biological Cybernetics.

[21]  D. Wang,et al.  The time dimension for scene analysis , 2005, IEEE Transactions on Neural Networks.

[22]  DeLiang Wang,et al.  Object selection based on oscillatory correlation , 1999, Neural Networks.

[23]  DeLiang Wang,et al.  Image Segmentation Based on Oscillatory Correlation , 1997, Neural Computation.

[24]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[25]  N. Petkov,et al.  Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition , 2007, Biological Cybernetics.

[26]  B. S. Manjunath,et al.  Multi-scale edge detection and image segmentation , 2005, 2005 13th European Signal Processing Conference.

[27]  Mauro Ursino,et al.  Modeling segmentation of a visual scene via neural oscillators: fragmentation, discovery of details and attention. , 2004, Network.

[28]  Pieter R. Roelfsema,et al.  Object-based attention in the primary visual cortex of the macaque monkey , 1998, Nature.

[29]  Roman M Borisyuk,et al.  Oscillatory neural network model of attention focus formation and control. , 2003, Bio Systems.

[30]  Angelo Cangelosi,et al.  Towards language acquisition in autonomous robots , 2008, ALIFE.

[31]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[32]  Y. J. Zhang,et al.  A survey on evaluation methods for image segmentation , 1996, Pattern Recognit..

[33]  Joachim M. Buhmann,et al.  Image Segmentation by Networks of Spiking Neurons , 2005, Neural Computation.

[34]  Roman Borisyuk,et al.  Dynamics of neural networks with a central element , 1999, Neural Networks.

[35]  N. Kanwisher,et al.  Visual attention: Insights from brain imaging , 2000, Nature Reviews Neuroscience.

[36]  R. Ellis,et al.  The potentiation of grasp types during visual object categorization , 2001 .

[37]  N. Cowan.  Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. , 1988, Psychological bulletin.

[38]  Roman Borisyuk,et al.  Oscillatory model of attention-guided object selection and novelty detection , 2004, Neural Networks.

[39]  Wentao Huang,et al.  Modeling contextual modulation in the primary visual cortex , 2008, Neural Networks.

[40]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[41]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[42]  DeLiang Wang,et al.  A dynamically coupled neural oscillator network for image segmentation , 2002, Neural Networks.

[43]  Giulio Sandini,et al.  The iCub humanoid robot: an open platform for research in embodied cognition , 2008, PerMIS.

[44]  A. Baddeley.  Exploring the Central Executive , 1996 .

[45]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.