Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/122953
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) Birds 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: https://doi.org/10.3390/s18010117 |
It is part of: | Sensors, 2018, vol. 18(1), num. 117 |
URI: | http://hdl.handle.net/2445/122953 |
Related resource: | https://doi.org/10.3390/s18010117 |
ISSN: | 1424-8220 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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