@Article{MarkPhilipPhilipsen2018,
author="Mark Philip Philipsen
and Jacob Velling Dueholm
and Anders Jorgensen
and Sergio Escalera
and Thomas B. Moeslund",
title="Organ Segmentation in Poultry Viscera Using RGB-D",
journal="Sensors",
year="2018",
volume="18",
number="1",
pages="117",
optkeywords="semantic segmentation",
optkeywords="RGB-D",
optkeywords="random forest",
optkeywords="conditional random field",
optkeywords="2D",
optkeywords="3D",
optkeywords="CNN",
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.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3072), last updated on Thu, 17 Jan 2019 13:38:00 +0100",
doi="10.3390/s18010117"
}