{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T09:08:57Z","timestamp":1777626537071,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31601218"],"award-info":[{"award-number":["31601218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31601219"],"award-info":[{"award-number":["31601219"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673218"],"award-info":[{"award-number":["61673218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.<\/jats:p>","DOI":"10.3390\/s20030812","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:18:48Z","timestamp":1580872728000},"page":"812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System"],"prefix":"10.3390","volume":"20","author":[{"given":"Hanbing","family":"Deng","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongyu","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuncheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Miao","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1093\/ije\/dyu063","article-title":"The genotype conception of heredity","volume":"43","author":"Johannsen","year":"2014","journal-title":"Int. J. Epidemiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1164\/ajrccm.156.4.12-tac-5","article-title":"Genetics of complex disease: Approaches, problems, and solutions","volume":"156","author":"Schork","year":"1997","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroscience.2009.09.009","article-title":"From the genome to the phenome and back: Linking genes with human brain function and structure using genetically informed neuroimaging","volume":"164","author":"Siebner","year":"2009","journal-title":"Neuroscience"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neuroscience.2009.01.027","article-title":"Phenomics: The systematic study of phenotypes on a genome-wide scale","volume":"164","author":"Bilder","year":"2009","journal-title":"Neuroscience"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1038\/nrg2897","article-title":"Phenomics: The next challenge","volume":"11","author":"Houle","year":"2010","journal-title":"Nat. Rev. Genet."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","article-title":"Phenomics-technologies to relieve the phenotyping bottleneck","volume":"16","author":"Furbank","year":"2011","journal-title":"Trends Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.fcr.2012.04.003","article-title":"Field-based phenomics for plant genetics research","volume":"133","author":"White","year":"2012","journal-title":"Field Crop. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.pbi.2015.02.006","article-title":"Lights, camera, action: High-throughput plant phenotyping is ready for a close-up","volume":"24","author":"Fahlgren","year":"2015","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1146\/annurev-arplant-050312-120137","article-title":"Future scenarios for plant phenotyping","volume":"64","author":"Fiorani","year":"2013","journal-title":"Annu. Rev. Plant Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1038\/s41438-019-0151-5","article-title":"Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production","volume":"6","author":"Bauer","year":"2019","journal-title":"Horticult. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"giy153","DOI":"10.1093\/gigascience\/giy153","article-title":"Computer vision-based phenotyping for improvement of plant productivity: A machine learning perspective","volume":"8","author":"Mochida","year":"2019","journal-title":"Gigascience"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Prey, L., Von, B.M., and Schmidhalter, U. (2018). Evaluating RGB imaging and multispectral active and hyperspectral passive sensing for assessing early plant vigor in winter wheat. Sensors, 18.","DOI":"10.3390\/s18092931"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/j.compag.2019.05.043","article-title":"Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis","volume":"162","author":"Xiang","year":"2019","journal-title":"Comp. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guan, H.O., Liu, M., and Ma, X.D. (2018). Three-dimensional reconstruction of soybean canopies using multisource imaging for phenotyping analysis. Remote Sens., 10.","DOI":"10.3390\/rs10081206"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, H.J., Xu, L.B., and Shi, S.G. (2018). A high throughput integrated hyperspectral imaging and 3D measurement system. Sensors, 19.","DOI":"10.3390\/s18041068"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2016.09.008","article-title":"Image segmentation and bias correction using local inhomogeneous intensity clustering (LINC): A region-based level set method","volume":"219","author":"Feng","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_17","unstructured":"Ryu, T., Wang, P., and Lee, S.H. (2013, January 11\u201314). Image compression with meanshift based inverse colorization. Proceedings of the IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2018.01.022","article-title":"Contour-aware network for semantic segmentation via adaptive depth","volume":"284","author":"Jiang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69184","DOI":"10.1109\/ACCESS.2019.2918700","article-title":"Scene-aware deep networks for semantic segmentation of images","volume":"7","author":"Yi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neucom.2012.01.050","article-title":"Integrating low-level and semantic features for object consistent segmentation","volume":"119","author":"Fu","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., and Ren, S.Q. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., and Jia, Y.Q. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_25","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.13031\/aea.13406","article-title":"Self-adversarial training and attention for multi-task wheat phenotyping","volume":"35","author":"Hu","year":"2019","journal-title":"Appl. Eng. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.13031\/trans.13302","article-title":"Identifying Fagaceae species in Taiwan using leaf images","volume":"62","author":"Lee","year":"2019","journal-title":"Trans. ASABE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"042621","DOI":"10.1117\/1.JRS.11.042621","article-title":"Deep convolutional neural network for classifying Fusarium Wilt of Radish from Unmanned Aerial Vehicles","volume":"11","author":"Ha","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_30","first-page":"W3","article-title":"Real-time Blob-Wise Sugar Beets vs Weeds classification for monitoring fields using convolutional neural networks","volume":"4","author":"Milioto","year":"2017","journal-title":"ISPRS"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., and Darrell, T. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., and Girshick, R. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3390\/rs11020196","article-title":"Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection","volume":"11","author":"Omid","year":"2019","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TPAMI.2015.2502579","article-title":"Very deep convolutional neural networks for classification and detection","volume":"38","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"(2014, January 19). ImageNet Large Scale Visual Recognition Challenge 2014. Available online: http:\/\/image-net.org\/challenges\/LSVRC\/2014\/."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Roy, C.A., and Maji, S. (2015, January 11\u201318). Bilinear CNN models for fine-grained visual recognition. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.170"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shou, Z., Chan, J., and Zareian, A. (2017, January 21\u201326). CDC: Convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.155"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/812\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:54:09Z","timestamp":1760172849000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,3]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030812"],"URL":"https:\/\/doi.org\/10.3390\/s20030812","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,3]]}}}