Line-Based Modified Iterated Hough Transform for Autonomous Single-Photo Resection

Habib, Ayman; Lin, Hsiang Tseng; Morgan, Michel

doi:10.14358/pers.69.12.1351

Cited by 16 publications

(5 citation statements)

References 6 publications

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“…There are various methods such as SPR [24][25][26][27], the law of cosine [28], Procrustes algorithm [29][30][31], PnP algorithm [32], DLT algorithm [33], and pinhole camera-based algorithm [34][35][36] to estimate the position and orientation of the sensor using the camera image. Among these methods, SPR is a widely recognized algorithm for estimating EOPs [26,37].…”

Section: Related Work 121 Estimating Camera Position and Orientationmentioning

confidence: 99%

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model

Yoo,

Kim

2023

J. Imaging

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This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m.

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Section: Related Work 121 Estimating Camera Position and Orientationmentioning

confidence: 99%

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model

Yoo,

Kim

2023

J. Imaging

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“…Habib and Kelley (2001) introduced the Modified Iterated Hough Transform (MIHT) to estimate the Relative Orientation Parameters (ROP) between stereo-pair of images using linear features [11]. Habib and Al-Ruzouq (2004) used linear features for the coregistration of satellite imagery [12]. In the MIHT, we assume no knowledge of conjugate entities and do not require complete correspondence between the entities.…”

Section: Registrationmentioning

confidence: 99%

Line-based registration for UAV remote sensing imagery of wide-spanning river basin

Zang

Lin

Zhang

et al. 2011

2011 19th International Conference on Geoinformatics

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With the development of Unmanned Aerial Vehicles (UAVs) remote sensing technique, some researchers began to apply the UAV imagery to investigate Yellow River's dike hazards. There has been an urgent need for establishing automatic and accurate registration techniques of UAV remote sensing imagery. High-precision image registration will enhance the reliability of the investigation. As the Yellow River is very wide, it is very difficult to find enough Ground Control Points (GCP) and the limited GCPs are usually distributed unevenly. Therefore, the traditional point-based image registration approach cannot satisfy the requirements of imagery registration. However, the river basin contains linear features richly. This paper proposes a line-based registration approach for UAV remote sensing imagery of the wide-spanning river basin. This method regards the segment straight line as the registration primitives. After detect the primitives of input images, we estimate the parameters of transformation model between reference-image and registering-image with Modified Iterated Hough Transform (MIHT). Experiments using the UAV imagery of Ning-Meng section of Yellow River conclude that this is a robust method for registration for UAV remote sensing imagery of wide-spanning river basin.

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“…In SPR, a solution is obtained by repeatedly adjusting three or more control points. SPR has been studied to increase its efficiency [ 5 , 6 , 7 ]. However, because SPR is sensitive to the initial values of EOPs, it is difficult to use if those values are not specified [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Absolute IOP/EOP Estimation Models without Initial Information of Various Smart City Sensors

Kim

Baek

Kim

2023

Sensors

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In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems.

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Line-Based Modified Iterated Hough Transform for Autonomous Single-Photo Resection

Cited by 16 publications

References 6 publications

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model

Line-based registration for UAV remote sensing imagery of wide-spanning river basin

Absolute IOP/EOP Estimation Models without Initial Information of Various Smart City Sensors

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