by Andreza Aparecida dos Santos, Sandra Avila e Thiago Teixeira Santos [en] In this work, we modeled the problem of … More
by Thiago Teixeira Santos, Luı́s Henrique Bassoi, Henrique Oldoni and Roberto Luvisutto Martins This work presents a methodology for 3-D … More
We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way.
[pt-BR] Um método para detecção automática de bagas de café em imagens digitais tomadas em campo é apresentado. Ele consiste em uma fase de detecção preliminar de regiões candidatas seguida por uma etapa de classificação capaz de filtrar falsos positivos. Nossos resultados evidenciam que a detecção automática de frutos é factível e pode ser aplicada em metodologias de predição de safra e sistemas de agricultura de precisão.
In this work, structure from motion is employed to estimate the position of a hand-held camera, moving around plants, and to recover a sparse 3D point cloud sampling the plants’ surfaces. Multiple-view stereo is employed to extend the sparse model to a dense 3D point cloud. The model is automatically segmented by spectral clustering, properly separating the plant’s leaves whose surfaces are estimated by fitting trimmed B-splines to their 3D points.
A stereo approach for 3D plant modelling is presented. Using only a set of photographies, the method produces a dense 3D point cloud sampling the plant surface. Clustering automatically segments the plant structure in meaningful parts, which are classified in elements of interest as leaves and internodes. Measurements can be computed for each element, as area or surface normals.
This paper shows as the state of the art in structure from motion and multiple view stereo is able to produce accurate 3D models for specimens presenting sparse canopies. Three-dimensional triangular meshes are computed from a set of non-calibrated images, modeling a basil and an Ixora specimens and accurately representing their leaves and branches.