Optimum design of chamfer distance transforms

Butt, Muhammad Akmal; Maragos, Petros

doi:10.1109/83.718487

Cited by 132 publications

(18 citation statements)

References 13 publications

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“…At this point, the recognition of correspondences among point clouds is essential to compute plant movements of the plant as displacements between multi-temporal datasets. Therefore, first cloud-to-cloud differences between sequential TLiDAR datasets are computed using a chamfer distance approach that exploits an octree organization (Akmal Butt and Maragos, 1998 ). Second, the distance statistics resulting from this first step are used to refine the displacements.…”

Section: Methodsmentioning

confidence: 99%

Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR

2018

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Plant leaf movement is induced by some combination of different external and internal stimuli. Detailed geometric characterization of such movement is expected to improve understanding of these mechanisms. A metric high-quality, non-invasive and innovative sensor system to analyze plant movement is Terrestrial LiDAR (TLiDAR). This technique has an active sensor and is, therefore, independent of light conditions, able to obtain accurate high spatial and temporal resolution point clouds. In this study, a movement parameterization approach of leaf plants based on TLiDAR is introduced. For this purpose, two Calathea roseopicta plants were scanned in an indoor environment during 2 full-days, 1 day in natural light conditions and the other in darkness. The methodology to estimate leaf movement is based on segmenting individual leaves using an octree-based 3D-grid and monitoring the changes in their orientation by Principal Component Analysis. Additionally, canopy variations of the plant as a whole were characterized by a convex-hull approach. As a result, 9 leaves in plant 1 and 11 leaves in plant 2 were automatically detected with a global accuracy of 93.57 and 87.34%, respectively, compared to a manual detection. Regarding plant 1, in natural light conditions, the displacement average of the leaves between 7.00 a.m. and 12.30 p.m. was 3.67 cm as estimated using so-called deviation maps. The maximum displacement was 7.92 cm. In addition, the orientation changes of each leaf within a day were analyzed. The maximum variation in the vertical angle was 69.6° from 12.30 to 6.00 p.m. In darkness, the displacements were smaller and showed a different orientation pattern. The canopy volume of plant 1 changed more in the morning (4.42 dm3) than in the afternoon (2.57 dm3). The results of plant 2 largely confirmed the results of the first plant and were added to check the robustness of the methodology. The results show how to quantify leaf orientation variation and leaf movements along a day at mm accuracy in different light conditions. This confirms the feasibility of the proposed methodology to robustly analyse leaf movements.

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Section: Methodsmentioning

confidence: 99%

Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR

2018

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“…The algorithm begins at the basin outlet and performs the segmentation in the upstream direction (Figure 1b), so that the vector of intermediate outlets is sorted descending in terms of drainage area. After setting an accumulated length value of zero to the basin outlet (green square in Figure 1b), the algorithm starts tracing in the upstream direction and the accumulated length is updated at each pixel using the Distance Transforms method (Butt & Maragos, 1998) to improve distance calculations (Paz & Collischonn, 2007). Whenever the length threshold is exceeded, as indicated by the break lines in Figure 1b, the accumulated length is reset to zero and a unique number (ID) is assigned to all pixels belonging to the same river reach.…”

Section: A Length-delimited Vector-based River Discretization For Hydrologic-hydrodynamic Modelingmentioning

confidence: 99%

On the discretization of river networks for large scale hydrologic-hydrodynamic models

Fan

Siqueira

Fleischmann

et al. 2021

RBRH

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The discretization of river networks is a critical step for computing flow routing in hydrological models. However, when it comes to more complex hydrologic-hydrodynamic models, adaptations in the spatial representation of model calculation units are further required to allow cost-effective simulations, especially for large scale applications. The objective of this paper is to assess the impacts of river discretization on simulated discharge, water levels and numerical stability of a catchment-based hydrologic-hydrodynamic model, using a fixed river length (Δx) segmentation method. The case study was the Purus river basin, a sub-basin of the Amazon, which covers an area that accounts for rapid response upstream reaches to downstream floodplain rivers. Results indicate that the maximum and minimum discharges are less affected by the adopted Δx (reach-length), whereas water levels are more influenced by this selection. It is showed that for the explicit local inertial one-dimensional routing, Δx and the α parameter of CFL (Courant-Friedrichs-Lewy) condition must be carefully chosen to avoid mass balance errors. Additionally, a simple Froude number-based flow limiter to avoid numerical issues is proposed and tested.

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“…The approximated global distance is a superposition of the local distances multiplied by corresponding global coordinate. Based on the above-mentioned principles, we select chamfer distance transform algorithm, 11 which operates quickly and simply. This algorithm also obtains calculated distance close enough to the real Euclidean distance.…”

Section: Second Stage Of Detectionmentioning

confidence: 99%

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

et al. 2018

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An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

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Optimum design of chamfer distance transforms

Cited by 132 publications

References 13 publications

Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR

Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR

On the discretization of river networks for large scale hydrologic-hydrodynamic models

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

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