Detection and characterization of junctions in a 2D image

Bergevin, Robert; Bubel, A.

doi:10.1016/j.cviu.2003.10.003

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…Such an approach has been considered because these differential properties are currently used by algorithms for segmentation, recognition and registration of range images [13][14][15]. Fig.…”

Section: Surface Damage Recognition By Curvature Computationmentioning

confidence: 99%

Contactless recognition of concrete surface damage from laser scanning and curvature computation

Teza

Galgaro

Moro

2009

NDT & E International

100

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Section: Surface Damage Recognition By Curvature Computationmentioning

confidence: 99%

Contactless recognition of concrete surface damage from laser scanning and curvature computation

Teza

Galgaro

Moro

2009

NDT & E International

100

View full text Add to dashboard Cite

“…Cazorla et al [17,18] propose two Bayesian methods for junction classification that evolved from the Kona method: a region-based method and an edge-based method. Bergevin and Bubel [19] propose a junction characterization and a validation method where junction branches of volumetric objects are extracted at points of interest in a 2D image using a topologically constrained grouping process and a binary split tree. Perwass [20] proposes a method to extract the intersections between the conic curves and to determine all possible linear support domains, then to determine the edges from the image gradients and to determine the type of extracted junction points by the local geometry structural analysis of edges.…”

Section: Introductionmentioning

confidence: 99%

Junction Point Detection Algorithm for SAR Image

Zhang

Luo

Gao

et al. 2013

International Journal of Antennas and Propagation

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In this paper, we propose a novel junction point detector based on an azimuth consensus for remote sensing images. To eliminate the impact of noise and some noncorrelated edges of SAR image, an azimuth consensus constraint is developed. In addition to detecting the locations of junctions at the subpixel level, this operator recognizes their structures as well. A new formula that includes a minimization criterion for the total weighted distance is proposed to compute the locations of junction points accurately. Compared with other well-known detectors, including Forstner, JUDOCA, and CPDA, the experimental results indicate that our operator outperforms them both in location accuracy of junction points and in angle accuracy of branch edges. Moreover, our method possesses satisfying robustness to the impact of noise and changes of the SAR images. Our operator can be potentially used to solve a number of problems in computer vision, such as SAR image registration, wide-baseline matching, and UAV navigation system.

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“…Several approaches can be found on the literature about T-junctions estimation but, unlike the proposed system, many of them rely on a hard threshold to detect these points [13], [22], and [30].…”

Section: A T-junction Candidates Estimationmentioning

confidence: 99%

Monocular Depth Ordering Using T-Junctions and Convexity Occlusion Cues

Palou

Salembier

2013

IEEE Trans. on Image Process.

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Abstract-This paper proposes a system that relates objects in an image using occlusion cues and arranges them according to depth. The system does not rely on a priori knowledge of the scene structure and focuses on detecting special points, such as T-junctions and highly convex contours, to infer the depth relationships between objects in the scene. The system makes extensive use of the binary partition tree as hierarchical region-based image representation jointly with a new approach for candidate T-junction estimation. Since some regions may not involve T-junctions, occlusion is also detected by examining convex shapes on region boundaries. Combining T-junctions and convexity leads to a system which only relies on low level depth cues and does not rely on semantic information. However, it shows a similar or better performance with the state-of-the-art while not assuming any type of scene.As an extension of the automatic depth ordering system, a semi-automatic approach is also proposed. If the user provides the depth order for a subset of regions in the image, the system is able to easily integrate this user information to the final depth order for the complete image. For some applications, user interaction can naturally be integrated, improving the quality of the automatically generated depth map.

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Detection and characterization of junctions in a 2D image

Cited by 14 publications

References 25 publications

Contactless recognition of concrete surface damage from laser scanning and curvature computation

Contactless recognition of concrete surface damage from laser scanning and curvature computation

Junction Point Detection Algorithm for SAR Image

Monocular Depth Ordering Using T-Junctions and Convexity Occlusion Cues

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