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show on a modified MNIST dataset that when faced with scale variation, building
show on a modified MNIST dataset that when faced with scale variation, building
in scale-invariance allows ConvNets to learn more discriminative features with
in scale-invariance allows ConvNets to learn more discriminative features with
===Shadow-Free Segmentation in Still Images Using Local Density Measure===
Speaker: [http://www.umiacs.umd.edu/~aecins/ Aleksandrs Ecins] -- Date: December 11, 2014
Abstract: Over the last decades several approaches were introduced to deal with cast shadows in background subtraction applications. However, very few algorithms exist that address the same problem for still images. In this paper we propose a figure ground segmentation algorithm to segment objects in still images affected by shadows. Instead of modeling the shadow directly in the segmentation process our approach works actively by first segmenting an object and then testing the resulting boundary for the presence of shadows and resegmenting again with modified segmentation parameters. In order to get better shadow boundary detection results we introduce a novel image preprocessing technique based on the notion of the image density map. This map improves the illumination invariance of classical filterbank based texture description methods. We demonstrate that this texture feature improves shadow detection results. The resulting segmentation algorithm achieves good results on a new figure ground segmentation dataset with challenging illumination conditions.
