Curvature-direction measures for 3D feature detection

Li, Jinjiang; Fan, Hui

doi:10.1007/s11432-013-4991-6

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…To accelerate the selection efficiency of feature points in the PCD of cattle body, the candidate regions of feature points can be extracted. The curvature is the basic characteristics of the surface shape, reflecting the concave or convex degree of the PCD surface, and has a geometrical invariability of translation, rotation, scaling, and other transformation [64]. The mean curvature H [65] and Gaussian curvature K [66] were applied to extract the feature points in the candidate region of Qinchuan cattle.…”

Section: Methodsmentioning

confidence: 99%

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

Huang

Guo

Rao

et al. 2019

Sensors

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For the time-consuming and stressful body measuring task of Qinchuan cattle and farmers, the demand for the automatic measurement of body dimensions has become more and more urgent. It is necessary to explore automatic measurements with deep learning to improve breeding efficiency and promote the development of industry. In this paper, a novel approach to measuring the body dimensions of live Qinchuan cattle with on transfer learning is proposed. Deep learning of the Kd-network was trained with classical three-dimensional (3D) point cloud datasets (PCD) of the ShapeNet datasets. After a series of processes of PCD sensed by the light detection and ranging (LiDAR) sensor, the cattle silhouettes could be extracted, which after augmentation could be applied as an input layer to the Kd-network. With the output of a convolutional layer of the trained deep model, the output layer of the deep model could be applied to pre-train the full connection network. The TrAdaBoost algorithm was employed to transfer the pre-trained convolutional layer and full connection of the deep model. To classify and recognize the PCD of the cattle silhouette, the average accuracy rate after training with transfer learning could reach up to 93.6%. On the basis of silhouette extraction, the candidate region of the feature surface shape could be extracted with mean curvature and Gaussian curvature. After the computation of the FPFH (fast point feature histogram) of the surface shape, the center of the feature surface could be recognized and the body dimensions of the cattle could finally be calculated. The experimental results showed that the comprehensive error of body dimensions was close to 2%, which could provide a feasible approach to the non-contact observations of the bodies of large physique livestock without any human intervention.

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Section: Methodsmentioning

confidence: 99%

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

Huang

Guo

Rao

et al. 2019

Sensors

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“…To form the II-frame, one takes the three frame axes as the surface normal and the directions of maximum / minimum surface curvature. 24,35 As mentioned previously, these axes are extrinsic invariants and the resulting 3D coordinate frame therefore is also an extrinsic invariant. Unfortunately, these axes are well-defined/unique only when the surface has two distinct curvatures.…”

Section: Derivation Of the Igrand Framementioning

confidence: 81%

iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications

Willis

Brink

2016

SPIE Proceedings

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This article describes a new 3D RGBD image feature, referred to as iGRaND, for use in real-time systems that use these sensors for tracking, motion capture, or robotic vision applications. iGRaND features use a novel local reference frame derived from the image gradient and depth normal (hence iGRaND) that is invariant to scale and viewpoint for Lambertian surfaces. Using this reference frame, Euclidean invariant feature components are computed at keypoints which fuse local geometric shape information with surface appearance information. The performance of the feature for real-time odometry is analyzed and its computational complexity and accuracy is compared with leading alternative 3D features.

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Section: Extraction Of the Candidate Areas Of Feature Pointsmentioning

confidence: 99%

Smart Sensing Technologies for Agriculture

2020

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Adamchuk has formed the Precision Agriculture and Sensor Systems (PASS) research team that has been actively involved in research and outreach activities across Canada and the USA as well as a variety of other global initiatives. Kenneth Sudduth leads a multidisciplinary team conducting precision agriculture research on topics including field-scale implementation of precision agriculture systems and evaluation of their economic and environmental effects at the

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Curvature-direction measures for 3D feature detection

Cited by 5 publications

References 20 publications

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications

Smart Sensing Technologies for Agriculture

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