{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:43:46Z","timestamp":1775796226770,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,3]],"date-time":"2018-01-03T00:00:00Z","timestamp":1514937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18010117","type":"journal-article","created":{"date-parts":[[2018,1,3]],"date-time":"2018-01-03T12:00:06Z","timestamp":1514980806000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Organ Segmentation in Poultry Viscera Using RGB-D"],"prefix":"10.3390","volume":"18","author":[{"given":"Mark","family":"Philipsen","sequence":"first","affiliation":[{"name":"Media Technology, Aalborg University, 9000 Aalborg, Denmark"}]},{"given":"Jacob","family":"Dueholm","sequence":"additional","affiliation":[{"name":"Media Technology, Aalborg University, 9000 Aalborg, Denmark"}]},{"given":"Anders","family":"J\u00f8rgensen","sequence":"additional","affiliation":[{"name":"Media Technology, Aalborg University, 9000 Aalborg, Denmark"},{"name":"IHFood, Carsten Niebuhrs Gade 10, 2. tv., 1577 Copenhagen, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0617-8873","authenticated-orcid":false,"given":"Sergio","family":"Escalera","sequence":"additional","affiliation":[{"name":"Media Technology, Aalborg University, 9000 Aalborg, Denmark"},{"name":"Mathematics and Informatics, University of Barcelona, 08007 Barcelona, Spain"},{"name":"Computer Vision Center, Bellaterra, 08193 Barcelona, Spain"}]},{"given":"Thomas","family":"Moeslund","sequence":"additional","affiliation":[{"name":"Media Technology, Aalborg University, 9000 Aalborg, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,3]]},"reference":[{"key":"ref_1","unstructured":"Sun, D.W. 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