Publication
Semantic segmentation priors for object discovery
Conference Article
Conference
International Conference on Pattern Recognition (ICPR)
Edition
23rd
Pages
549-554
Doc link
http://dx.doi.org/10.1109/ICPR.2016.7899691
File
Authors
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Martín García, Germán
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Husain, Syed Farzad
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Schulz, Hannes
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Frintrop, Simone
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Torras Genís, Carme
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Behnke , Sven
Projects associated
Abstract
Reliable object discovery in realistic indoor scenes is a necessity for many computer vision and service robot applications. In these scenes, semantic segmentation methods have made huge advances in recent years. Such methods can provide useful prior information for object discovery by removing false positives and by delineating object boundaries. We propose a novel method that combines bottom-up object discovery and semantic priors for producing generic object candidates in RGB-D images. We use a deep learning method for semantic segmentation to classify colour and depth superpixels into meaningful categories. Separately for each category, we use saliency to estimate the location and scale of objects, and superpixels to find their precise boundaries. Finally, object candidates of all categories are combined and ranked. We evaluate our approach on the NYU Depth V2 dataset and show that we outperform other state-of-the-art object discovery methods in terms of recall.
Categories
computer vision, object detection, object recognition, robot vision, service robots.
Author keywords
Semantic segmentation, object discovery, deep learning
Scientific reference
G. Martín, F. Husain, H. Schulz, S. Frintrop, C. Torras and S. Behnke. Semantic segmentation priors for object discovery, 23rd International Conference on Pattern Recognition, 2016, Cancún, Mexico, pp. 549-554.
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