Abstract
Several solutions allowing to compensate the lack of performance of GNSS (Global Navigation Satellites Systems) occurring when operating in constrained environments (dense urbain areas) have emerged in recent years. Characterizing the environment of reception of GNSS signals using a fisheye camera oriented to the sky is one of these relevant solutions. The idea consists in determining LOS (Line-Of-Sight) satellites and NLOS (Nonline-Of-Sight) satellites by classifying the content of acquired images into two regions (sky and not-sky). In this paper, aimed to make this approach more effective, we propose a region-based image classification technique through Bhattacharyya coefficient-based distance and local image region descriptors. The proposed procedure is composed of four major steps: (i) A simplification step that consists in simplifying the acquired image with an appropriate couple of colorimetric invariant and exponential transform. (ii) The second step consists in segmenting the simplified image in different regions of interest using Statistical Region Merging segmentation method. (iii) In the third step, the segmented regions are characterized with a number of local color image region descriptors. (iv) The fourth step introduces the supervised \(\mathcal {MSRC}\) (Maximal Similarity Based Region Classification) method by using Bhattacharyya coefficient-based distance to classify the characterized regions into sky and non sky regions. Experimental results prove the robustness and performance of the proposed procedure according to the proposed group of color local image region descriptors.
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El merabet, Y. et al. (2016). Horizon Line Detection from Fisheye Images Using Color Local Image Region Descriptors and Bhattacharyya Coefficient-Based Distance. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_6
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