Abstract
In this paper we introduce a neural architecture for multiple scale color image segmentation on a Graphics Processing Unit (GPU): the BioSPCIS (Bio-Inspired Stream Processing Color Image Segmentation) architecture. BioSPCIS has been designed according to the physiological organization of the cells on the mammalian visual system and psychophysical studies about the interaction of these cells for image segmentation. Quality of the segmentation was measured against hand-labelled segmentations from the Berkeley Segmentation Dataset. Using a stream processing model and hardware suitable for its execution, we are able to compute the activity of several neurons in the visual path system simultaneously. All the 100 test images in the Berkeley database can be processed in 5 minutes using this architecture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Antón-Rodríguez, M., Díaz-Pernas, F.J., Díez-Higuera, J.F., Martínez-Zarzuela, M., González-Ortega, D., Boto-Giralda, D.: Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion. Neurocomputing 72, 3713–3725 (2009)
BSDS: The berkeley segmentation dataset and benchmark, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/ (last visit July 2010)
Carey, J.: Brain Facts: A primer on the brain and nervous system. The Society For Neuroscience (2006)
Díaz-Pernas, F.J., Antón-Rodríguez, M., Díez-Higuera, J.F., Martínez-Zarzuela, M., González-Ortega, D., Boto-Giralda, D.: Texture classification of the entire brodatz database through an orientational-invariant neural architecture. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5602, pp. 294–303. Springer, Heidelberg (2009)
Díaz-Pernas, F., Antón-Rodríguez, M., Martínez-Zarzuela, M., Díez-Higuera, J.F., González-Ortega, D., Boto-Giralda, D.: Multiple scale neural architecture for enhancing regions in the colour image segmentation process. Expert Systems 28, 70–96 (in press)
Gobron, S., Devillard, F., Heit, B.: Retina simulation using cellular automata and gpu programming. Mach. Vision Appl. 18(6), 331–342 (2007)
Hérault, J., Durette, B.: Modeling visual perception for image processing. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 662–675. Springer, Heidelberg (2007)
Hodgkin, A.: The Conduction of the Nerve Impulse. Springfield (1964)
Hubel, D., Wiesel, T.: Receptive fields and functional architecture of monkey striate cortex. J. Physiology 195(1), 215–243 (1968)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (July 2001)
Martínez-Zarzuela, M., Díaz-Pernas, F., Antón-Rodríguez, M., Díez-Higuera, J., González-Ortega, D., Boto-Giralda, D., López-González, F., De La Torre, I.: Multi-scale neural texture classification using the gpu as a stream processing engine. Mach. Vision Appl. (January 2010), http://dx.doi.org/10.1007/s00138-010-0254-3
Martínez-Zarzuela, M., Pernas, F.D., Higuera, J.F.D., Antón-Rodríguez, M.: Fuzzy ART neural network parallel computing on the GPU. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 463–470. Springer, Heidelberg (2007)
Martínez-Zarzuela, M., Pernas, F.D., de Pablos, A.T., Antón-Rodríguez, M., Higuera, J.F.D., Boto-Giralda, D., Ortega, D.G.: Adaptative resonance theory fuzzy networks parallel computation using CUDA. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 149–156. Springer, Heidelberg (2009)
Mingolla, E., Ross, W., Grossberg, S.: A neural network for enhancing boundaries and surfaces in synthetic aperture radar images. Neural Networks 12, 499–511 (1999)
Obermayer, K., Blasdel, G.G.: Geometry of orientation and ocular dominance columns in monkey striate cortex. Journal of Neuroscience 13, 4114–4129 (1993)
Owens, J., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A., Purcell, T.: A survey of general-purpose computation on graphics hardware. Computer Graphics Forum. 26(1), 80–113 (2007), http://www.blackwell-synergy.com/doi/pdf/10.1111/j.1467-8659.2007.01012x
Rumpf, M., Strzodka, R.: Level set segmentation in graphics hardware. In: Proceedings of the IEEE International Conference on Image Processing (ICIP 2001), vol. 3, pp. 1103–1106 (2001)
Schwartz, S.: Visual Perception: a clinical orientation, 3rd edn. McGraw-Hill, New York (2004)
Viola, I., Kanitsar, A., Gruller, M.E.: Hardware-based nonlinear filtering and segmentation using high-level shading languages. IEEE Visualization, 309–316 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Zarzuela, M., Díaz-Pernas, F.J., Antón-Rodríguez, M., Perozo-Rondón, F., González-Ortega, D. (2011). Bio-inspired Color Image Segmentation on the GPU (BioSPCIS). In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-21326-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21325-0
Online ISBN: 978-3-642-21326-7
eBook Packages: Computer ScienceComputer Science (R0)