Levenberg-Marquardt algorithm based nonlinear optimization of camera calibration for relative measurement

Tian, Shaoxiong; Lu, Shan; Liu, Zongming

doi:10.1109/chicc.2015.7260394

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…In order to get the direction of the head, projection from 2D to the 3D image is employed on the camera. [11] used camera calibration based on Direct Linear Transform (DLT) and Lavenberg-Marquardt Optimization methods. These experiments indicate that the method can improve the accuracy and speed of the camera calibration with the average distance between the measured coordinates and the model's coordinates, which is 0.04548.…”

Section: Introductionmentioning

confidence: 99%

Image processing for student emotion monitoring based on fisherface method

Pratomo

Florestyanto

Sania

et al. 2021

SITech

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Monitoring academic emotion is an activity to provide information from students' academic emotions in the class continuously. Some research in the image processing field had done for face recognition but had not been many studies on image processing to detect student emotions. This paper aims to determine the percentage of facial recognition with fisherface and academic emotional recognition by monitoring changes in students' facial expressions using facial landmarks in various distances, camera angles, light, and attributes used on objects. The proposed method uses facial image extraction based on fisherface method for presence. Furthermore, face identification will be made with Euclidean distance by finding the smallest length of training data with test data. Emotion detection is done by facial landmarks and mathematical calculations to detect drowsiness, focus, and not focus on the face. Restful web service is used as a communication architecture to integrate data. The success rate of applications with the fisherface method obtains 96% percent accuracy of face recognition. Meanwhile, facial landmarks and mathematical calculations are used to detect emotions, with 84 %.

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Section: Introductionmentioning

confidence: 99%

Image processing for student emotion monitoring based on fisherface method

Pratomo

Florestyanto

Sania

et al. 2021

SITech

View full text Add to dashboard Cite

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High Precision and Noise Resistance Estimation Based on Optimized GOI Algorithm for Rocket Nozzle

Zhou

Huo

2018

2018 37th Chinese Control Conference (CCC)

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High Precision and Pose Estimation Based on Improved Point Cloud Algorithm for Noncooperative Targets

Liang¹,

Liu²,

Gao³

et al. 2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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Aiming at protecting the space environment such as removing space debris and repairing malfunctioning targets, it is vital importance to measure the pose of space targets. In this paper we propose a high precision pose estimation algorithm based on point cloud. The optimized Random Sample Consensus (RANSAC) algorithm which applies iterative solution can effectively delete false matching points. The experiment system consists of a satellite model and a binocular camera. The proposed method is a vital part of new autonomous spacecraft measurement which only use a binocular camera system at different perspectives to complete the pose estimation. After obtaining the cloud data of a target, we use the improved crust algorithm to accomplish the three-dimensional model. Finally, the experiment has shown that the vision based method can obtain the pose information of the targets effectively.

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Levenberg-Marquardt algorithm based nonlinear optimization of camera calibration for relative measurement

Cited by 4 publications

References 7 publications

Image processing for student emotion monitoring based on fisherface method

Image processing for student emotion monitoring based on fisherface method

High Precision and Noise Resistance Estimation Based on Optimized GOI Algorithm for Rocket Nozzle

High Precision and Pose Estimation Based on Improved Point Cloud Algorithm for Noncooperative Targets

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