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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/122953
Organ Segmentation in Poultry Viscera Using RGB-D
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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.
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PHILIPSEN, Mark Philip, et al. Organ Segmentation in Poultry Viscera Using RGB-D. Sensors. 2018. Vol. 18(1), num. 117. ISSN 1424-8220. [consulted: 14 of August of 2026]. Available at: https://hdl.handle.net/2445/122953