Adaptive Image Thresholding of Yellow Peppers for a Harvesting Robot
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TLDR
The presented work is part of the H2020 project SWEEPER with the overall goal to develop a sweet pepper harvesting robot for use in greenhouses, and visual servoing is used to train the robot.About:
This article is published in Robotics.The article was published on 2018-02-05 and is currently open access. It has received 22 citations till now. The article focuses on the topics: Visual servoing & Thresholding.read more
Citations
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Proceedings Article
Image Processing
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Handbook Of Image And Video Processing
TL;DR: The handbook of image and video processing is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
Journal ArticleDOI
Development of a sweet pepper harvesting robot
Boaz Arad,Jos Balendonck,R. Barth,Ohad Ben-Shahar,Yael Edan,Thomas Hellström,Jochen Hemming,Polina Kurtser,Ola Ringdahl,Toon Tielen,Bart van Tuijl +10 more
TL;DR: This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses that includes a six degrees of freedom industrial arm.
Journal ArticleDOI
Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set
TL;DR: A ripe tomato detection method that combines deep learning with edge contour detection is proposed and it is demonstrated that the proposed method improves tomato detection accuracy and that it can be further applied in the harvesting process of agricultural robots.
Journal ArticleDOI
Controlled Lighting and Illumination-Independent Target Detection for Real-Time Cost-Efficient Applications. The Case Study of Sweet Pepper Robotic Harvesting.
TL;DR: A Flash-No-Flash (FNF) controlled illumination acquisition protocol that frees the system from most ambient illumination effects and facilitates robust target detection while using only modest computational resources and no supervised training is presented.
References
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Technical Note : \cal Q -Learning
Chris Watkins,Peter Dayan +1 more
TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
Journal ArticleDOI
Survey over image thresholding techniques and quantitative performance evaluation
Mehmet Sezgin,Bulent Sankur +1 more
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Proceedings Article
Image Processing
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.