TY  - JOUR
AU  - Mark Philip Philipsen
AU  - Jacob Velling Dueholm
AU  - Anders Jorgensen
AU  - Sergio Escalera
AU  - Thomas B. Moeslund
PY  - 2018//
TI  - Organ Segmentation in Poultry Viscera Using RGB-D
T2  - SENS
JO  - Sensors
SP  - 117
VL  - 18
IS  - 1
KW  - semantic segmentation
KW  - RGB-D
KW  - random forest
KW  - conditional random field
KW  - 2D
KW  - 3D
KW  - CNN
N2  - 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.
UR  - http://dx.doi.org/10.3390/s18010117
N1  - HUPBA; no proj
ID  - Mark Philip Philipsen2018
ER  -