Color Normalization for Digital Video Processing

Sánchez, J. García; Binefa, Xavier

doi:10.1007/3-540-40053-2_17

Cited by 4 publications

(2 citation statements)

References 11 publications

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Section: Identify the Relative Coordinates Of The Trees From Below Camentioning

confidence: 99%

“…Therefore, the color of the PPC created at each location should be normalized, to ensure consistency throughout the stand. Among the multiple algorithms [33,34] and equations available for normalization we have used the feature scaling [35], which does not produce unnatural appearances, an issue of complex algorithms, while homogenizing the hue throughout the stand:…”

Section: Identify the Relative Coordinates Of The Trees From Below Camentioning

confidence: 99%

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Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

2019

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Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure-from-motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.needed. Stand level information is particularly needed for intermediate cuts, when the decision is taken considering all the trees not only a portion of them. Although total height of each tree in a stand can be relatively easy estimated from point clouds, lidar or photogrammetric, there are at least two attributes that are difficult to estimate accurately and precisely for all trees: dbh and location. Many algorithms have been developed to estimate dbh from above canopy data (e.g., point clouds or images) with significant success [4][5][6][7]. However, while expected accuracy is in many instances achieved, the precision of estimating dbh from above canopy is limited, which restricts its operational usage. The effort placed in estimation of tree location mirror the one for tree size, but the success was clearly less impressive, as the accuracy is still measured in meters [8].Point clouds play a significant role in forest operations, particularly in road design and road maintenance [9,10]. Point clouds generated without an active sensor, such as lidar, or stereopsis are sometimes labeled photogrammetric point clouds [11] or phodar [12]. Photogrammetric point clouds (PPC) made inroads in harvesting, but they were employed similarly with aerial laser scanning, as the applications were based on t...

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Section: Identify the Relative Coordinates Of The Trees From Below Camentioning

confidence: 99%