Accurate 3D measurement using optical depth information

Traumann, Andres; Daneshmand, Morteza; Escalera, Sérgio; Anbarjafari, Gholamreza

doi:10.1049/el.2015.1345

Cited by 18 publications

(5 citation statements)

References 5 publications

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“…The horizontal and vertical field of view of the sensor are 70 • and 60 • respectively [26], [38]. If the real distance between the plant and the sensor camera is calculated, the height of the plant can be calculated according to the following formulas [39], it is shown as following ( 7), (8), ( 9…”

Section: Plant Height Measurementmentioning

confidence: 99%

Development and Performance Test of a Height-Adaptive Pesticide Spraying System

Chen

Meng

2018

IEEE Access

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This paper investigate an adaptive pesticide spraying system according to the plants height. The system including a depth sensor and a spraying system with multiple nozzles at different height in vertical direction. The entire system is installed on an automatic guided vehicle. In order to implement precision spraying or automatic targeting, plant identification, and plant height calculation are critical for the system with fixed height of nozzles. The designed system will open one or combination of the nozzles of the spraying system since the plant height is identified. To address the plant height-adaptive challenge, a pesticide spraying system with plant height-adaptive is proposed based on a camera sensor to dealt with the figure of the plant. Both of the depth and color data of the figure are obtained and analyzed, the open or close state of the spraying system is optimized for different plants with different height. Various experimental results have demonstrated that the proposed system is considered as well.INDEX TERMS Automatic guided vehicle, precision spraying, depth sensor, height-adaptive, signal processing.

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Section: Plant Height Measurementmentioning

confidence: 99%

Development and Performance Test of a Height-Adaptive Pesticide Spraying System

Chen

Meng

2018

IEEE Access

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“…There exist various techniques for conducting a map-ping from 2D image texture space to a 3D surface. Some examples are intermediate 3D shape [5], direct drawing onto the object [15], or using an exponential fast marching method by applying geodesic distance [37,34]. Many researchers have devoted special attention trying to enhance the realism of virtual garment representation during the last decade [7].…”

Section: Literature Reviewmentioning

confidence: 99%

From 2D to 3D Geodesic-based Garment Matching

Madadi¹,

Avots²,

Escalera³

et al. 2018

Preprint

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A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured by using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by mean square error (MSE) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset.

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“…The Pareto-optimal points The normalized values [68,69], spatial mechanical manipulators [70][71][72][73] and cable-driven parallel robots [74], as well as optimization of 3D measurement [75], reconstruction [76], and post-processing [77] procedures utilized in the contexts of 3D simulation, modeling, and visualization.…”

Section: Ppmmentioning

confidence: 99%

PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

et al. 2017

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Abstract. This paper aims to introduce a proportion-preserving composite objective function for multi-objective optimization, namely, PPCOF, and validate its e ciency through demonstrating its applicability to optimization of the kinetostatic performance of planar parallel mechanisms. It exempts the user from both specifying preference factors and conducting decision-making. It consists of two terms. The rst one adds the normalized objective functions up, where the extrema result from single-objective optimization. To make the composite objective function steer the variations of the objective functions while preserving rational proportions between them, as the main contribution of the paper, it is sought that the normalized objective functions take closely similar values, to which end they are juxtaposed inside a vector, which is then scaled such that its Euclidean norm-2 is equal to that of the vector of all ones with the same dimensions. Then, the second term is constructed as the addition of penalty factors standing for the absolute value of the di erence between each element of the foregoing vector from 1. From the obtained results, with considerably smaller computational cost, the PPCOF obtains an optimal solution that is not dominated by any point from a set of Pareto-optimal solutions o ered by NSGA-II.

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Accurate 3D measurement using optical depth information

Cited by 18 publications

References 5 publications

Development and Performance Test of a Height-Adaptive Pesticide Spraying System

Development and Performance Test of a Height-Adaptive Pesticide Spraying System

From 2D to 3D Geodesic-based Garment Matching

PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

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