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Open AccessArticle

Organ Segmentation in Poultry Viscera Using RGB-D

Media Technology, Aalborg University, 9000 Aalborg, Denmark
IHFood, Carsten Niebuhrs Gade 10, 2. tv., 1577 Copenhagen, Denmark
Mathematics and Informatics, University of Barcelona, 08007 Barcelona, Spain
Computer Vision Center, Bellaterra, 08193 Barcelona, Spain
Author to whom correspondence should be addressed.
Sensors 2018, 18(1), 117;
Received: 11 November 2017 / Revised: 27 December 2017 / Accepted: 27 December 2017 / Published: 3 January 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11 % is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28 % using only basic 2D image features. View Full-Text
Keywords: semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN
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Philipsen, M.P.; Dueholm, J.V.; Jørgensen, A.; Escalera, S.; Moeslund, T.B. Organ Segmentation in Poultry Viscera Using RGB-D. Sensors 2018, 18, 117.

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