Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
An Autonomous Robot for Harvesting Cucumbers in Greenhouses
Autonomous Robots
Robuste Kalibrierung von CCD-Sensoren für autonome, mobile Systeme
Autonome Mobile Systeme 1995, 11. Fachgespräch
A Machine Vision System Using a Laser Radar Applied to Robotic Fruit Harvesting
CVBVS '99 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications
Automated micro-propagation of plant material
M2VIP '97 Proceedings of the 4th Annual Conference on Mechatronics and Machine Vision in Practice
3D Pose Estimation of Cactus Leaves using an Active Shape Model
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Extraction of Curved Lines from Images
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
An automatic machine vision-guided grasping system for Phalaenopsis tissue culture plantlets
Computers and Electronics in Agriculture
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Within the present study, a system for the automated harvest of Gerbera jamesonii pedicels with the help of image analytic methods was developed. The study can be divided mainly into two parts: the development of algorithms for the identification of pedicels in digital images and the development of procedures for harvesting these pedicels with a robot. Images of plants were taken with a stereo camera system, which consisted of two high-resolution CCD-cameras with near-infrared filters. The plant was positioned on a rotatable working desk and images of eight different positions were shot. The developed image processing algorithm segmented the potential pedicel regions in the images, removed noise, differentiated overlapping pedicels by using different algorithms and combined the remaining regions to pedicel objects. From the data of both images and eight plant positions three-dimensional models of the pedicels were created by triangulation. The remaining parts of the plants were modeled in a simple fashion. The evaluated 3D model is used to calculate spatial coordinates for the applied robot control. For harvesting the pedicels, an industrial robot with six axes (plus an additional linear axis) was used. A pneumatic harvest grabber was developed, which harvested the pedicels by cutting them off. In order to guarantee the collision free path of the robot, a path planning module was integrated, which includes the three-dimensional model of the plant and the test facility. With the applied techniques it was possible to correctly detect all pedicels on about 72% of the images. Regarding the whole image series of the respective plant, all pedicels could be detected in at least one photographing position in 97% of all cases. In the harvest experiments 80% of all pedicels could be harvested. The harvest rates decreased with increasing numbers of pedicels on a plant. Therefore, 98% of the pedicels could be harvested of plants with one or two pedicels, but only 51% were harvested of plants with five or more pedicels. In horticultural practice, an identification system for evaluating the stage of maturity should be included. An implementation for harvesting pedicels of different species with similar basic characteristics is imaginable.