Abstract
This paper presents a biologically-inspired artificial vision system. The goal of the proposed vision system is to correctly match regions among several images to obtain scenes matching. Based on works that consider that humans perceive visual objects divided in its cons-tituent parts, we assume that a particular type of regions, called curvilinear regions, can be easily detected in digital images. These features are more complex than the basic features that human vision uses in the very first steps in the visual process. We assume that the curvilinear regions can be compared in their complexity to those features analysed by the IT cortex for achieving objects recognition. The approach of our system is similar to other existing methods that also use intermediate complexity features for achieving visual matching. The novelty of our system is the curvilinear features that we use.
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References
Bandera, A., Urdiales, C., Arrebola, F., Sandoval, F.: Corner detection by means of adaptively estimated curvature function. Electronics Letters 36(2), 124–126 (2000)
Biederman, I.: Recognition-by-components: A theory of human image understanding. Psychological Review 94, 115–147 (1987)
Grauman, K., Darrell, T.: Efficient image matching with distributions of local invariant features. In: Proc. of IEEE Conf. Computer Vision and Pattern Recognition, pp. 627–634 (2005)
Haushofer, J., Baker, C., Kanwisher, N.: Greater Sensitivity to Convexities than Concavities in Human Lateral Occipital Complex. In: Society for Neuroscience Annual Meeting (2005)
Klette, G.: A comparative discussion of distance transformation and simple deformations in digital image processing. Machine Graphics & Vision 12(2), 235–256 (2003)
Leek, E.C., Reppa, I., Arguin, M.: The structure of three-dimensional object representations in human vision: evidence from whole-part matching. Journal of Experimental Psychology: Human Perception and Performance 31(4), 668–684 (2005)
Lowe, D.: Towards a computational model for object recognition in IT Cortex. In: First IEEE International Workshop on Biologically Motivated Computer Vision, Seoul, Korea, pp. 20–31 (2000)
Marfil, R., Rodriguez, J.A., Bandera, A., Sandoval, F.: Bounded irregular pyramid: a new structure for color image segmentation. Pattern Recognition 37(3), 623–626 (2004)
Marr, D., Nishihara, H.K.: Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London B: Biological Sciences 200, 269–294 (1978)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 384–393 (2002)
Tarr, M., Bulthof, H.: Image-based object recognition in man, monkey and machine. Cognition 67, 1–20 (1998)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5, 682–687 (2002)
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Pérez-Lorenzo, J.M., Bandera, A., Reche-López, P., Marfil, R., Vázquez-Martín, R. (2007). An Approach to Visual Scenes Matching with Curvilinear Regions. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_43
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DOI: https://doi.org/10.1007/978-3-540-73055-2_43
Publisher Name: Springer, Berlin, Heidelberg
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