Abstract
This paper presents an approach for automatic visual inspection of chicken entrails in RGB-D data. The point cloud is first over-segmented into supervoxels based on color, spatial and geometric information. Color, position and texture features are extracted from each of the resulting supervoxels and passed to a Random Forest classifier, which classifies the supervoxels as either belonging to heart, lung, liver or misc. The dataset consists of 150 individual entrails, with 30 of these being reserved for evaluation. Segmentation performance is evaluated on a voxel-by-voxel basis, achieving an average Jaccard index of 61.5 % across the four classes of organs. This is a 5.9 % increase over the 58.1 % achieved with features derived purely from 2D.
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Thanks to GUDP for financial support and to Danpo for providing access to their facilities. The work has been partially supported by Spanish project TIN2013-43478-P.
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Philipsen, M.P., Jørgensen, A., Escalera, S., Moeslund, T.B. (2016). RGB-D Segmentation of Poultry Entrails. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_17
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DOI: https://doi.org/10.1007/978-3-319-41778-3_17
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