A Camera Self-Calibration Method Based on Plane Lattice and Orthogonality

Zhao, Yue; Lei, Jianchong

doi:10.12928/telkomnika.v11i4.1198

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…This study used an assembled stereo camera. The calibration process used a toolbox by Jean-Yves Bouguet [25][26][27]. Both cameras connected to the computer via a USB port and shown in Figure 1 [3].…”

Section: Methodsmentioning

confidence: 99%

A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

Rahmani¹,

Harjoko

Priyambodo

2020

IJEECS

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This paper presents a new rules-based of a real-time decision system for an autonomous wheeled robot with the holonomic-drive system. The robot uses decisions to avoid collisions with obstacles. The decision rules based on Grid-Edge-Depth Map. The Grid-Edge-Depth map represents the obstacle’s position and distance in the environment. The generation process of the Grid-Edge-Depth map presented in previous research. The decisions of the first scenario with no destination point are forward, stop, 90o right turn, and 90o left turn. The decisions of the second, and third scenarios with a destination point are forward, stop, 90o right turn, 90o left turn, 45o forward to the right, 45o forward to the left, slide to the right, and slide to the left. The proposal tested in a 5x3 metre living environment. Finally, the experiment resulted in 93.3% of navigation’s success for all the scenarios.

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Section: Methodsmentioning

confidence: 99%

A vision-based real-time obstacle avoidance’s rules utilising grid-edge-depth map

Rahmani¹,

Harjoko

Priyambodo

2020

IJEECS

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“…This subject deals with a computer"s ability to see and understand objects in the real world through visual sensors like cameras. The two subtopics of keypoint detection and description enable a computer to understand an object"s unique fingerprint while pose estimation allows the computer to gauge an object"s position relative to it [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Development of Pose Estimation Algorithm for Quranic Arabic Word

2018

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The study carried out in this report proposes the best keypoint detection, description, and pose estimation algorithm combination for Quranic Arabic words. Oriented-FAST Rotated-BRIEF (ORB) and Accelerated-KAZE (AKAZE) are used as the keypoint detection and description algorithms while Random Sample Consensus (RANSAC) and Least Median Squares (LMEDS) are used to evaluate the homography for pose estimation algorithms. The algorithms are combined with each other to provide four different techniques to estimate the pose of Quranic Arabic words. The algorithms are tested on a limited dataset chosen from a phrase within the Quran. Performance of each algorithm is measured in real-time through inlier to keypoint ratio which determines pose accuracy.

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“…Finding correspondences in a set of images of a scene is a fundamental part of most computer vision tasks, such as camera self-calibration, registration, structure and motion recovery, object recognition and so on [1].…”

Section: Introductionmentioning

confidence: 99%