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Title: Organ Segmentation in Poultry Viscera Using RGB-D
Author: Philipsen, Mark Philip
Velling Dueholm, Jacob
Jørgensen, Anders
Escalera Guerrero, Sergio
Moeslund, Thomas Baltzer
Keywords: Ocells
Xarxes neuronals (Neurobiologia)
Neural networks (Neurobiology)
Issue Date: 3-Jan-2018
Publisher: MDPI
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.
Note: Reproducció del document publicat a:
It is part of: Sensors, 2018, vol. 18(1), num. 117
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ISSN: 1424-8220
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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