Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.
Description
Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.
%0 Journal Article
%1 Harmsen20097
%A Harmsen, Stephan R.
%A Koenderink, Nicole J.J.P.
%D 2009
%J Computers and Electronics in Agriculture
%K Flower-counting Multi-target-tracking Particle-cloud motion-model
%N 1
%P 7 - 18
%R DOI: 10.1016/j.compag.2008.07.004
%T Multi-target tracking for flower counting using adaptive motion models
%U http://www.sciencedirect.com/science/article/B6T5M-4TBVPMX-1/2/330273d70342196926b1b53666fbfa24
%V 65
%X Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.
@article{Harmsen20097,
abstract = {Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.},
added-at = {2009-04-06T20:47:53.000+0200},
author = {Harmsen, Stephan R. and Koenderink, Nicole J.J.P.},
biburl = {https://www.bibsonomy.org/bibtex/211c9b73f2b36d81077f5534cd3d0b97b/nicole_koenderink},
description = {Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.},
doi = {DOI: 10.1016/j.compag.2008.07.004},
interhash = {625b95411df43be7dcc2b12b56b8ca5a},
intrahash = {11c9b73f2b36d81077f5534cd3d0b97b},
issn = {0168-1699},
journal = {Computers and Electronics in Agriculture},
keywords = {Flower-counting Multi-target-tracking Particle-cloud motion-model},
number = 1,
pages = {7 - 18},
timestamp = {2009-04-07T08:16:47.000+0200},
title = {Multi-target tracking for flower counting using adaptive motion models},
url = {http://www.sciencedirect.com/science/article/B6T5M-4TBVPMX-1/2/330273d70342196926b1b53666fbfa24},
volume = 65,
year = 2009
}