How easy is matching 2D line models using local search?

Beveridge, J. Ross; Riseman, Edward M.

doi:10.1109/34.601245

Cited by 73 publications

(32 citation statements)

References 34 publications

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“…In the context of line and segment matching, Beveridge and Riseman [3] addressed this problem via exhaustive local search. Although their method found good matches reliably and efficiently (due to their choice of the objective function and a small neighborhood size), it is unclear how the approach can be generalized to other types of feature graphs and objective functions.…”

Section: Related Workmentioning

confidence: 99%

Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs

Demirci

Shokoufandeh

Dickinson

et al. 2004

Lecture Notes in Computer Science

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Abstract. In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge-weighted directed graph. The algorithm was based on a metric-tree representation of labeled graphs and their metric embedding into normed vector spaces, using the embedding algorithm of Matousek [13]. However, the method was limited by the fact that two graphs to be matched were typically embedded into vector spaces with different dimensionality. Before the embeddings could be matched, a dimensionality reduction technique (PCA) was required, which was both costly and prone to error. In this paper, we introduce a more efficient embedding procedure based on a spherical coding of directed graphs. The advantage of this novel embedding technique is that it prescribes a single vector space into which both graphs are embedded. This reduces the problem of directed graph matching to the problem of geometric point matching, for which efficient many-to-many matching algorithms exist, such as the Earth Mover's Distance. We apply the approach to the problem of multi-scale, view-based object recognition, in which an image is decomposed into a set of blobs and ridges with automatic scale selection.

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Section: Related Workmentioning

confidence: 99%

Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs

Demirci

Shokoufandeh

Dickinson

et al. 2004

Lecture Notes in Computer Science

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“…An example of successful matching between ground readings and aerial image for localization is given in [6] and for matching of building outlines in [27].…”

Section: Future Workmentioning

confidence: 99%

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Persson

Duckett

Lilienthal

2008

Robotics and Autonomous Systems

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This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

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“…In this way, the task is related to others we have considered before, such as optimal matching of 2D line segment models to cluttered and complex line data [4,3], matching 3D line models to 2D image features assuming 3D perspective projection [1,2,8], optimal matching of 3D models to multi-modal data [11], and recent advances in combinatorial line matching using local search within genetic algorithms [14].…”

Section: Curve Selectionmentioning

confidence: 99%