Search Results for author: James Underwood

Found 7 papers, 1 papers with code

A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees

no code implementations7 Feb 2021 Fredrik Westling, James Underwood, Mitch Bryson

We present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function with a focus on improving light distribution throughout the canopy.

Decision Making Management

SimTreeLS: Simulating aerial and terrestrial laser scans of trees

1 code implementation24 Nov 2020 Fredrik Westling, Mitch Bryson, James Underwood

We present an open source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters.

BIG-bench Machine Learning Material Classification

Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

no code implementations4 Sep 2017 Alexander Wendel, James Underwood

This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates.

Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields

no code implementations9 Jun 2017 Mikkel Kragh, James Underwood

Results showed that for a two-class classification problem (ground and nonground), only the camera leveraged from information provided by the other modality with an increase in the mean classification score of 0. 5%.

General Classification Semantic Segmentation

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

no code implementations25 Oct 2016 Suchet Bargoti, James Underwood

This paper presents an image processing framework for fruit detection and counting using orchard image data.

Image Segmentation Segmentation +1

Deep Fruit Detection in Orchards

no code implementations12 Oct 2016 Suchet Bargoti, James Underwood

This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples.

Data Augmentation object-detection +1

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