Justin Petluk
ABSTRACT
Measuring characteristics of animals in the wild is not always possible, due to their demeanour and lack of human contact. Remote capture and processing methods, including the segmentation of animal data into relevant body parts, are required. Existing solutions are either costly or too cumbersome to use in the wild. This study explores the use of RGB depth (RGB-D) cameras for data capture of a target animal from a distance. In addition, this study explores the extraction and segmentation of the resulting animal data into point clouds, and the creation of machine learning models for the automated segmentation of this data. Results of this study, including an experimental evaluation, demonstrate the feasibility of utilizing RGB-D cameras for animal data capture, and that classification outperformed clustering for automated animal data segmentation.
- Andreas Berghänel, Oliver Schülke, and Julia Ostner. 2015. Locomotor play drives motor skill acquisition at the expense of growth: a life history trade-off. Science Advances 1, 7 (2015), 8 pages.Google Scholar
- Charlotte A. Brassey and William I. Sellers. 2014. Scaling of convex hull volume to body mass in modern primates, non-primate mammals and birds. PLoS One 9, 3 (2014), 12 pages.Google ScholarCross Ref
- Monica Carfagni, Rocco Furferi, Lapo Governi, Chiara Santarelli, Michaela Servi, Francesca Uccheddu, and Yary Volpe. 2019. Metrological and Critical Characterization of the Intel® D415 Stereo Depth Camera. Sensors 19, 3 (2019), 20 pages.Google Scholar
- Stefan K. Gehrig and Uwe Franke. 2007. Improving stereo sub-pixel accuracy for long-range stereo. In Proceedings of the 11th IEEE International Conference on Computer Vision. 1--7.Google Scholar
- Anders Grunnet-Jepson, John N. Sweetser, and John Woodfill. [n. d.]. Best-known methods for tuning Intel® RealSense™ D400 Depth Cameras for Best Performance. https://www.intel.com/content/dam/support/us/en/documents/emerging-technologies/intel-realsense-technology/BKMs_Tuning_RealSense_D4xx_Cam.pdf (last accessed May 2021).Google Scholar
- Jiawei Han, Micheline Kamber, and Jian Pei. 2011. Data mining concepts and techniques (3rd ed.). Morgan Kaufmann Publishers, Waltham, Mass.Google Scholar
- Lvwen Huang, Shuqin Li, Anqi Zhu, Xinyun Fan, Chenyang Zhang, and Hongyan Wang. 2018. Non-contact body measurement for Qinchuan cattle with LIDAR sensor. Sensors 18, 9 (2018), 21 pages.Google Scholar
- Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. 2018. PointCNN: Convolution on X-transformed Points. In Proceedings of the 32nd Conference on Neural Information Processing Systems. 11 pages.Google Scholar
- Charles R. Qi, Li Yi, Hao. Su, and Leonidas J. Guibas. 2017. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In Proceedings of the 31st Conference on Neural Information Processing Systems. 5105--5114.Google Scholar
- Radu B. Rusu and Steve Cousins. 2011. 3D is here: Point Cloud Library. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation. 4 pages.Google Scholar
- Markus Schoeler, Jeremie Papon, and Florentin Wörgötter. 2015. Constrained Planar Cuts - Object Partitioning for Point Clouds. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. 5207 -- 5215.Google ScholarCross Ref
- Jamie Shotton, Andrew Fitzgibbon, Mat Cook, Toby Sharp, Mark Finocchio, Richard Moore, Alex Kipman, and Andrew Blake. 2011. Real-time human pose recognition in parts from single depth images. In 2011 IEEE Conference on Computer Vision and Pattern Recognition. 1297--1304.Google ScholarDigital Library
- Anh-Vu Vo, Linh Truong-Hong, Debra F. Laefer, and Michela Bertolotto. 2015. Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015), 88--100.Google Scholar
Index Terms
- Point cloud capture and segmentation of animal images using classification and clustering
Recommendations
MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique
Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from ...
Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique
Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging MRI images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering ...
Segmentation of brain MR images using intuitionistic fuzzy clustering algorithm
ICVGIP '12: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image ProcessingA new algorithm for the segmentation of brain MR images, using intuitionistic fuzzy clustering (IFCM), is proposed in this paper. The algorithm uses intuitionistic fuzzy representation of image to deal with variations in pixel intensities of brain MR ...
Comments