2 research outputs found
Texture Object Segmentation Based on Affine Invariant Texture Detection
To solve the issue of segmenting rich texture images, a novel detection
methods based on the affine invariable principle is proposed. Considering the
similarity between the texture areas, we first take the affine transform to get
numerous shapes, and utilize the KLT algorithm to verify the similarity. The
transforms include rotation, proportional transformation and perspective
deformation to cope with a variety of situations. Then we propose an improved
LBP method combining canny edge detection to handle the boundary in the
segmentation process. Moreover, human-computer interaction of this method which
helps splitting the matched texture area from the original images is
user-friendly.Comment: 6pages, 15 figure
A robotic vision system to measure tree traits
The autonomous measurement of tree traits, such as branching structure,
branch diameters, branch lengths, and branch angles, is required for tasks such
as robotic pruning of trees as well as structural phenotyping. We propose a
robotic vision system called the Robotic System for Tree Shape Estimation
(RoTSE) to determine tree traits in field settings. The process is composed of
the following stages: image acquisition with a mobile robot unit, segmentation,
reconstruction, curve skeletonization, conversion to a graph representation,
and then computation of traits. Quantitative and qualitative results on apple
trees are shown in terms of accuracy, computation time, and robustness.
Compared to ground truth measurements, the RoTSE produced the following
estimates: branch diameter (mean-squared error mm), branch length
(mean-squared error mm), and branch angle (mean-squared error
degrees). The average run time was 8.47 minutes when the voxel resolution was
mm.Comment: 8 pages, IEEE/RSJ IROS 2017 conference pape