Contour model based homography estimation of texture-less planar objects in uncalibrated images

Zhang, Yueqiang; Zhou, Langming; Shang, Yang; Zhang, Xiaohu; Yu, Qifeng

doi:10.1016/j.patcog.2015.10.023

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Suppose that p = u, v, 1 T is the homogeneous coordinate of a 3D point P in an image, and p ′ = x, y, 1 T is the homogeneous coordinate of the corresponding point of point P in the matching image. The transformation from point p to its corresponding point p ′ can be obtained through the homography matrix H [28,29]:…”

Section: Characteristicsmentioning

confidence: 99%

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

Wang

Liu³

2019

Journal of Sensors

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This paper proposes a 3D autonomous navigation line extraction method for field roads in hilly regions based on a low-cost binocular vision system. Accurate guide path detection of field roads is a prerequisite for the automatic driving of agricultural machines. First, considering the lack of lane lines, blurred boundaries, and complex surroundings of field roads in hilly regions, a modified image processing method was established to strengthen shadow identification and information fusion to better distinguish the road area from its surroundings. Second, based on nonobvious shape characteristics and small differences in the gray values of the field roads inside the image, the centroid points of the road area as its statistical feature was extracted and smoothed and then used as the geometric primitives of stereo matching. Finally, an epipolar constraint and a homography matrix were applied for accurate matching and 3D reconstruction to obtain the autonomous navigation line of the field roads. Experiments on the automatic driving of a carrier on field roads showed that on straight roads, multicurvature complex roads and undulating roads, the mean deviations between the actual midline of the road and the automatically traveled trajectory were 0.031 m, 0.069 m, and 0.105 m, respectively, with maximum deviations of 0.133, 0.195 m, and 0.216 m, respectively. These test results demonstrate that the proposed method is feasible for road identification and 3D navigation line acquisition.

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Section: Characteristicsmentioning

confidence: 99%

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

Wang

Liu³

2019

Journal of Sensors

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“…where d(•) denotes the distance between two lines in the image, which was defined in the literature [56]. The smaller the value of S(M, S, H), the more similar the model line set is to the image lines under the current homography H. The parameter S max was employed to ensure that the correct homographies are not penalized too severely when a model line is fully occluded in the image.…”

Section: The Disorderly Images and The First Frame: Recognition Of Thmentioning

confidence: 99%

Automatic Pose Estimation of Uncalibrated Multi-View Images Based on a Planar Object with a Predefined Contour Model

Zhou

Wen-he

2016

IJGI

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Abstract:We have presented a framework to obtain camera pose (i.e., position and orientation in the 3D space) with real scale information of the uncalibrated multi-view images and the intrinsic camera parameters automatically. Our framework consists of two key steps. First, the initial value of the intrinsic camera and the pose parameters were extracted from homography estimation based on the contour model of some planar objects. Second, a refinement of the intrinsic camera and pose parameters was operated by the bundle adjustment procedure. Our framework can provide a complete flow of pose estimation of disorderly or orderly uncalibrated multi-view images, which can be used in vision tasks requiring scale information. Real multi-view images were utilized to demonstrate the robustness, flexibility and accuracy of the proposed framework. The proposed framework was also applied in 3D reconstruction.

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“…Over the years, several homography estimation methods have been proposed [2][3][4]. Among these methods, an intensively investigated approach is based on point correspondences [5,6].…”

Section: Introductionmentioning

confidence: 99%

Homography estimation from a single-point correspondence using template matching and particle swarm optimization

Díaz-Ramírez

Juarez-Salazar²,

Zheng

et al. 2022

Appl. Opt.

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Existing feature-based methods for homography estimation require several point correspondences in two images of a planar scene captured from different perspectives. These methods are sensitive to outliers, and their effectiveness depends strongly on the number and accuracy of the specified points. This work presents an iterative method for homography estimation that requires only a single-point correspondence. The homography parameters are estimated by solving a search problem using particle swarm optimization, by maximizing a match score between a projective transformed fragment of the input image using the estimated homography and a matched filter constructed from the reference image, while minimizing the reprojection error. The proposed method can estimate accurately a homography from a single-point correspondence, in contrast to existing methods, which require at least four points. The effectiveness of the proposed method is tested and discussed in terms of objective measures by processing several synthetic and experimental projective transformed images.

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Contour model based homography estimation of texture-less planar objects in uncalibrated images

Cited by 8 publications

References 31 publications

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

Automatic Pose Estimation of Uncalibrated Multi-View Images Based on a Planar Object with a Predefined Contour Model

Homography estimation from a single-point correspondence using template matching and particle swarm optimization

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