Organ Segmentation in Poultry Viscera Using RGB-D
AbstractWe 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
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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.
Philipsen MP, Dueholm JV, Jørgensen A, Escalera S, Moeslund TB. Organ Segmentation in Poultry Viscera Using RGB-D. Sensors. 2018; 18(1):117.Chicago/Turabian Style
Philipsen, Mark P.; Dueholm, Jacob V.; Jørgensen, Anders; Escalera, Sergio; Moeslund, Thomas B. 2018. "Organ Segmentation in Poultry Viscera Using RGB-D." Sensors 18, no. 1: 117.
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