Sparse view stereo matching

Elias, Rimon

doi:10.1016/j.patrec.2007.04.009

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…The algorithm for extracting 2D JUDOCA junction features is described in [8] and [5]. Basically, a 2D JUDOCA junction feature is defined by a triangle corresponding to the vertex where two edge fragments meet.…”

Section: D Junction Features (3d-judoca)mentioning

confidence: 99%

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An approach-path independent framework for place recognition and mobile robot localization in interior hallways

Yousef

Park

Kak

2013

2013 IEEE International Conference on Robotics and Automation

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Our work provides a fast approach-pathindependent framework for the problem of place recognition and robot localization in indoor environments. The approachpath independence is achieved by using highly viewpointinvariant 3D junction features extracted from stereo pairs of images; these are based on stereo reconstructions of the JUDOCA junctions extracted from the individual images of a stereo pair. The speed in place-recognition and robot-localization is achieved by using a novel cylindrical data structure -we refer to it as the Feature Cylinder -for representing either all of the 3D junction features found in a hallway system during the learning phase of the robot or a set of locale signatures derived from the data. For the case when all data is placed on the Feature Cylinder, we can use the 3D-POLY polynomial-time in a hypothesize-and-verify approach to place recognition. On the other hand, in the locale signature based approach, we can use the same data structure for constant-time place recognition.

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Section: D Junction Features (3d-judoca)mentioning

confidence: 99%

“…Only the left images of the stereo pairs are shown. Also shown are the projection and overlapping regions of the images numbered (2)(3)(4)(5) to the reference image 1. Fig.…”

Section: Viewpoint Invariance Of 3d-judocamentioning

confidence: 99%

An approach-path independent framework for place recognition and mobile robot localization in interior hallways

Yousef

Park

Kak

2013

2013 IEEE International Conference on Robotics and Automation

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“…Obviously, the above is applicable considering the left image as the reference, but if we consider the right one as the reference, the search for correspondences in the left one is made in the opposite direction. Based on the work of [ 34 ], given a centroid of a region in the left image we search for its corresponding centroid in the right one following the epipolar lines drawn in Figure 8(b) and then given a centroid in the right image, we search in the reverse sense in the left one, also following the epipolar lines.…”

Section: Correspondence Processmentioning

confidence: 99%

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

Herrera

Pajares

Guijarro

et al. 2009

Sensors

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This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion.

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“…Recently, Elias and Laganière [21] propose JUnction Detection Operator based on Circumferential Anchors (JUDOCA), which represents the latest research result on junction point detection algorithms. JUDOCA has been successfully used to solve many problems, such as 3-D reconstruction, camera parameter enhancing, and indoor and obstacle localization [22][23][24]. However, JUDOCA also has some drawbacks; for example, it only computes integervalued junction points and cannot achieve subpixel position precision and uses the path directions instead of the dip angles of junction branches, which brings extra errors, and its algorithm is sensitive to fractured 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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Sparse view stereo matching

Cited by 12 publications

References 29 publications

An approach-path independent framework for place recognition and mobile robot localization in interior hallways

An approach-path independent framework for place recognition and mobile robot localization in interior hallways

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

Junction Point Detection Algorithm for SAR Image

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