The detection of junction features in images

Laganière, Robert; Elias, Rimon

doi:10.1109/icassp.2004.1326609

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…In Fig. 7, we also compare the detection accuracy with the line junction detector [5] and show that, under the same recall value, our method achieves better performance.…”

Section: A Performance Evaluation Of Node Detectionmentioning

confidence: 86%

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Ridge Network Detection in Crumpled Paper via Graph Density Maximization

Hsu

Huang²

2012

IEEE Trans. on Image Process.

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Crumpled sheets of paper tend to exhibit a specific and complex structure, which is described by physicists as ridge networks. Existing literature shows that the automation of ridge network detection in crumpled paper is very challenging because of its complex structure and measuring distortion. In this paper, we propose to model the ridge network as a weighted graph and formulate the ridge network detection as an optimization problem in terms of the graph density. First, we detect a set of graph nodes and then determine the edge weight between each pair of nodes to construct a complete graph. Next, we define a graph density criterion and formulate the detection problem to determine a subgraph with maximal graph density. Further, we also propose to refine the graph density by including a pairwise connectivity into the criterion to improve the connectivity of the detected ridge network. Our experimental results show that, with the density criterion, our proposed method effectively automates the ridge network detection.

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“…In Fig. 7, we also compare the detection accuracy with the line junction detector [5] and show that, under the same recall value, our method achieves better performance.…”

Section: A Performance Evaluation Of Node Detectionmentioning

confidence: 86%

“…Manuscript Nevertheless, since the structure of ridge network is usually very complex, automatic detection of ridge network is still a very challenging task. Several detectors [2]- [5] have been adopted to detect lines or points with high curvature. In [3], Laplacian operators are adopted on range data to detect local extremes.…”

Section: Introductionmentioning

confidence: 99%

Ridge Network Detection in Crumpled Paper via Graph Density Maximization

Hsu

Huang²

2012

IEEE Trans. on Image Process.

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“…A junction is defined as an image point where several edges meet [8]. In other words, junctions in images occur when several nearly uniform regions join at one prominent point, (i.e., the point of junction) where the boundaries of the adjacent regions meet.…”

Section: Adaptive Feature Extractionmentioning

confidence: 99%

“…Existing detectors generally search junctions on circular areas of radius A in the image, hence this value is of great importance for the behavior of the detector. This is the case for the detector introduced in [8], which is used for feature detection in this work. Naturally, in a stereo pair, the area of projection of a feature detected in the first image onto the second image depends on the relationship between cameras and on the 3D location of the feature with respect to them.…”

Section: Adaptive Feature Extractionmentioning

confidence: 99%

Multi-camera Very Wide Baseline Feature Matching Based on View-Adaptive Junction Detection

Perez¹,

Salgado²,

Arróspide³

et al. 2012

2012 Third International Conference on Emerging Security Technologies

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Abstract-This paper presents a strategy for solving the feature matching problem in calibrated very wide-baseline camera settings. In this kind of settings, perspective distortion, depth discontinuities and occlusion represent enormous challenges. The proposed strategy addresses them by using geometrical information, specifically by exploiting epipolar constraints. As a result it provides a sparse number of reliable feature points for which 3D position is accurately recovered. Special features known as junctions are used for robust matching. In particular, a strategy for refinement of junction end-point matching is proposed which enhances usual junction-based approaches. This allows to compute cross-correlation between perfectly aligned plane patches in both images, thus yielding better matching results. Evaluation of experimental results proves the effectiveness of the proposed algorithm in very wide-baseline environments.

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“…The junctions are defined as points where linear ridges in the gradient domain intersect [7]. The radial lines composing the junctions are identified by searching in a circular neighborhood, for directional maxima of the intensity gradient.…”

Section: A the Algorithm Of Junction Detectionmentioning

confidence: 99%

An Area and Feature-Based Matching Method for 3D Reconstruction

Chun-hua¹,

Li²,

Wang³

et al. 2007

2007 International Conference on Convergence Information Technology (ICCIT 2007)

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This paper proposes a junction detection method in images by searching for the intersection of the linear ridges in gradient domain, and then presents a stereo matching method based on this new algorithm of junction detection, in order to meet the need of 3D reconstruction system. The images are uncalibrated and can be taken by different cameras or a single camera at different time instants. The proposed method adopts a strategy from rough matching to delicate matching. In the first matching process, the probability of mismatching is decreased on the cost of reducing the matching number. Thus, only the junctions with high strength are matched and the direction angles of the junctions' branches are used as a matching constraint. Then, the fundamental matrix is estimated by RANSAC and normalized 8-point algorithm. At last, the parameters of the junction detection are modified and the Harries corner detection is added to get more feature points. More matching points could be obtained through the guide of the fundamental matrix in the second matching process. The experiment results show that this algorithm has good performance on speed, accuracy and could obtain more edge matching points.

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The detection of junction features in images

Cited by 19 publications

References 9 publications

Ridge Network Detection in Crumpled Paper via Graph Density Maximization

Ridge Network Detection in Crumpled Paper via Graph Density Maximization

Multi-camera Very Wide Baseline Feature Matching Based on View-Adaptive Junction Detection

An Area and Feature-Based Matching Method for 3D Reconstruction

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