Search Results for author: Gert Kootstra

Found 8 papers, 3 papers with code

Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes

1 code implementation28 Nov 2023 Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra

We propose a gradient-based NBV planner using differential ray sampling, which directly estimates the local gradient direction for viewpoint planning to overcome occlusion and improve perception.

3D Reconstruction

3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud

no code implementations8 Nov 2023 Jianchao Ci, Xin Wang, David Rapado-Rincón, Akshay K. Burusa, Gert Kootstra

A 21 comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the 22 performance of different parts of the method.

3D Pose Estimation Keypoint Detection

Efficient Search and Detection of Relevant Plant Parts using Semantics-Aware Active Vision

no code implementations16 Jun 2023 Akshay K. Burusa, Joost Scholten, David Rapado Rincon, Xin Wang, Eldert J. van Henten, Gert Kootstra

To automate harvesting and de-leafing of tomato plants using robots, it is important to search and detect the relevant plant parts, namely tomatoes, peduncles, and petioles.

Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking

no code implementations4 Nov 2022 David Rapado Rincon, Eldert J. van Henten, Gert Kootstra

The accuracy of the representation was evaluated in a real-world environment, where successful representation and localisation of tomatoes in tomato plants were achieved, despite high levels of occlusion, with the total count of tomatoes estimated with a maximum error of 5. 08% and the tomatoes tracked with an accuracy up to 71. 47%.

3D Multi-Object Tracking

Attention-driven Active Vision for Efficient Reconstruction of Plants and Targeted Plant Parts

no code implementations21 Jun 2022 Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra

We conclude that an attention mechanism for active-vision is necessary to significantly improve the quality of perception in complex agro-food environments.

3D Reconstruction

Active learning with MaskAL reduces annotation effort for training Mask R-CNN

3 code implementations13 Dec 2021 Pieter M. Blok, Gert Kootstra, Hakim Elchaoui Elghor, Boubacar Diallo, Frits K. van Evert, Eldert J. van Henten

In our study, MaskAL was compared to a random sampling method on a broccoli dataset with five visually similar classes.

Active Learning

T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information

1 code implementation16 Apr 2021 Helena Russello, Rik van der Tol, Gert Kootstra

On occluded data, our temporal approach outperformed the static one by up to 32. 9%, suggesting that using temporal data was beneficial for pose estimation in environments prone to occlusions, such as dairy farms.

Animal Pose Estimation

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