Abstract
Brightness perception in real-life situations and subsequent image segmentation based on it is a complex phenomenon. The human visual system (HVS), apparently, does this effortlessly in the case of natural images. For specialized segmentation tasks, as in medical imaging, this is performed by the trained visual system of the specialist (radiologist, for instance, in medical imaging). In the present work, we shall concentrate on one such specialized task, viz. analysis of Photomicrograph of rock thin sections using petrological microscope, in the light of the HVS. For this, a new neural network model for the extended classical receptive field (ECRF) of Parvo (P) and Magno (M) cells in mid-level vision is elaborated at the outset. The model is based upon various well-known findings in neurophysiology, anatomy and psychophysics in HVS, especially related to the role parallel channels (P and M) in the central visual pathway. These two channels are represented by two different spatial filters that validate the reports of several psychophysical experiments on the direction of brightness induction. The mechanism of selecting the preferred channel for each of the stimuli consists of an algorithm that depends upon the output from an initial M channel filtering as captured in the visual cortex. We assume that the visual system of the geologist is training itself in the same way through such filtering processes in mid-level vision and identifying the important information in various situations in optical mineralogy and petrography. In the present work, the proposed model is applied in a simplified form on one such situation dealing with clast–matrix segregation from photomicrograph of sedimentary rocks, and is found to yield a promising result.
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References
Helson, H.: Studies of anomalous contrast and assimilation. J. Opt. Soc. Am. 53, 179–184 (1963)
Jameson, D., Hurvich, L.M.: Essay concerning color constancy. Ann. Rev. Psych. 40, 1–22 (1989)
Blakeslee, B., McCourt, M.E.: A unified theory of brightness contrast and assimilation incorporating oriented multiscale spatial filtering and contrast normalization. Vis. Res. 44, 2483–2503 (2004)
Ghosh, K.: A possible role and basis of visual pathway selection in brightness induction. Seeing and Perceiving 25(2), 179–212 (2012)
Ghosh, K.: A neural network based model of M and P LGN cells. In: IEEE Proceedings, Bioinformatics and Systems Biology (BSB), International Conference on, pp. 1–5 (2016)
Bullier, J.: Integrated model of visual processing. Brain Res. Rev. 36, 96–107 (2001)
Wei, H., Wang, X., Lai, L.L.: Compacts image representation model based on both nCRF and reverse control mechanisms. IEEE Trans. Neural Netw. Learn. Syst. 23 150–162 (2012)
Wei, H.: A bio-inspired integration method for object semantic representation. J. Artif. Intell. Soft Comput. 6, 137–154 (2016)
Merigan, W.H., Maunsell, J.R.H.: How parallel are the primate visual path ways? Ann. Rev. Neurosci. 16, 369–402 (1993)
Bowker, D.O.: Suprathreshold spatiotemporal response characteristics of the human visual system. J. Opt. Soc. Am. 73, 436–440 (1983)
De Valois, R.L., De Valois, K.K.: Spatial Vision. Oxford University Press, New York (1988)
Maunsell, J.H., Nealey, T.A., DePriest, D.D.: Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. J. Neurosci. 10, 3323–3334 (1990)
Hotchstein, S., Ahissar, M.: View from the top: hierarchies and reverse hierarchies in the visual system. Neuron 36, 791–804 (2002)
Fueten, F.: A computer controlled rotating polarizer stage for the petrographic microscope. Comput. Geosci. 23(2), 203–208 (1997)
Goodchild, J.S., Fueten, F.: Edge detection in petrographic images using the rotating polarizer stage. Comput. Geosci. 24, 745–751 (1998)
Lumbreras, F., Serrat, J.: Segmentation of petrological images of marbles. Comput. Geosci. 22(5), 547–558 (1996)
Thompson, S., Fueten, F., Bockus, D.: Mineral identification using artificial neural networks and the rotating polarizer stage. Comput. Geosci. 27, 1081–1089 (2001)
Izadi, H., Sadri, J., Mehran, N. A.: A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering. Comput. Geosci. 81, 38–52 (2015)
Jungmann, M., Pape, H., Wißkirchen, P., Clauser, C., Berlage, T.: Segmentation of thin section images for grain size analysis using region competition and edge-weighted region merging. Comput. Geosci. 72, 33–48 (2014)
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Das, R., Shankar, B.U., Chakraborty, T., Ghosh, K. (2020). Addressing Grain-Matrix Differentiation in Sedimentary Rock Photomicrographs in the Light of Brightness Perception Modelling. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_18
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