scholarly journals Iterative feature detection of a coded checkerboard target for the geometric calibration of infrared cameras

2021 ◽  
Vol 10 (2) ◽  
pp. 207-218
Author(s):  
Sebastian Schramm ◽  
Jannik Ebert ◽  
Johannes Rangel ◽  
Robert Schmoll ◽  
Andreas Kroll
Keyword(s):  
Image Processing ◽  
Thermal Imaging ◽  
Feature Detection ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Image Information ◽  
Geometric Calibration ◽  
Circular Pattern ◽  
Infrared Cameras ◽  
Distortion Parameters

Abstract. The geometric calibration of cameras becomes necessary when images should be undistorted, geometric image information is needed or data from more than one camera have to be fused. This process is often done using a target with a checkerboard or circular pattern and a given geometry. In this work, a coded checkerboard target for thermal imaging cameras and the corresponding image processing algorithm for iterative feature detection are presented. It is shown that, due in particular to the resulting better feature detectability at image borders, lower uncertainties in the estimation of the distortion parameters are achieved.

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

An Image Processing Algorithm to Find New and Old Bruises

1995 ◽  
Vol 11 (5) ◽  
pp. 751-757 ◽  
Author(s):  
J. A. Throop ◽  
D. J. Aneshansley ◽  
B. L. Upchurch
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm

scholarly journals Image Processing Algorithm for Weight Estimation of Dairy Cattle

2011 ◽  
Vol 36 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Kwang-Wook Seo ◽  
Hyeon-Tae Kim ◽  
Dae-Weon Lee ◽  
Yong-Cheol Yoon ◽  
Dong-Yoon Choi
Keyword(s):  
Image Processing ◽  
Dairy Cattle ◽  
Processing Algorithm ◽  
Weight Estimation ◽  
Image Processing Algorithm

Marker-guided auto-landing on a moving platform

2017 ◽  
Vol 5 (1) ◽  
pp. 28-42 ◽  
Author(s):  
Iryna Borshchova ◽  
Siu O’Young
Keyword(s):  
Image Processing ◽  
Visual Servoing ◽  
Low Cost ◽  
Field Tests ◽  
Processing Algorithm ◽  
Prototype System ◽  
Image Processing Algorithm ◽  
Suggested Approach ◽  
Content Type ◽  
Moving Platform

Purpose The purpose of this paper is to develop a method for a vision-based automatic landing of a multi-rotor unmanned aerial vehicle (UAV) on a moving platform. The landing system must be highly accurate and meet the size, weigh, and power restrictions of a small UAV. Design/methodology/approach The vision-based landing system consists of a pattern of red markers placed on a moving target, an image processing algorithm for pattern detection, and a servo-control for tracking. The suggested approach uses a color-based object detection and image-based visual servoing. Findings The developed prototype system has demonstrated the capability of landing within 25 cm of the desired point of touchdown. This auto-landing system is small (100×100 mm), light-weight (100 g), and consumes little power (under 2 W). Originality/value The novelty and the main contribution of the suggested approach are a creative combination of work in two fields: image processing and controls as applied to the UAV landing. The developed image processing algorithm has low complexity as compared to other known methods, which allows its implementation on general-purpose low-cost hardware. The theoretical design has been verified systematically via simulations and then outdoors field tests.

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