We propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The representation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the impact of image resolution upon the accuracy of extracting such a geometry, showing that accurate centerline and wall-floor boundaries can be estimated even in texture-poor environments with images as small as 16 × 12. In a database of 7 unique corridor sequences for orientation measurements, less than 2% additional error was observed as the resolution of the image decreased by 99%. One of the advantages of working at such resolutions is that the algorithm operates at hundreds of frames per second, or equivalently requires only a small percentage of the CPU. © Springer-Verlag Berlin Heidelberg 2012.
CITATION STYLE
Li, Y., Murali, V. N., & Birchfield, S. T. (2012). Extracting minimalistic corridor geometry from low-resolution images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7506 LNAI, pp. 207–216). https://doi.org/10.1007/978-3-642-33509-9_20
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