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Sensors 2018, 18(1), 117; https://doi.org/10.3390/s18010117

Organ Segmentation in Poultry Viscera Using RGB-D

1
Media Technology, Aalborg University, 9000 Aalborg, Denmark
2
IHFood, Carsten Niebuhrs Gade 10, 2. tv., 1577 Copenhagen, Denmark
3
Mathematics and Informatics, University of Barcelona, 08007 Barcelona, Spain
4
Computer Vision Center, Bellaterra, 08193 Barcelona, Spain
*
Author to whom correspondence should be addressed.
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)
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Abstract

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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