Grouping of Semantically Similar Image Positions

Priese, Lutz; Schmitt, Frank; Hering, Nils

doi:10.1007/978-3-642-02230-2_74

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Unfortunately, the intersection neighborhood defines no topology in the mathematical sense and a cluster analysis technique without a concept of a distance is required. For this an adaption of the AGS algorithm introduced in (Priese et al, 2009) is used that computes an intersection rate of two sets as a substitute for their distance.…”

Section: Vanishing Point Detectionmentioning

confidence: 99%

On Line-based Homographies in Urban Environments

Hering¹,

Priese²,

Schmitt³

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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This paper contributes to matching and registration in urban environments. We develop a new method to extract only those line segments that belong to contours of buildings. This method includes vanishing point detection, removal of wild structures, sky and roof detection. A registration of two building facades is achieved by computing a line-based homography between both. The known crucial instability of line homography is overcome with a line-panning and iteration technique. This homography approach is also able to separate single facades in a building.

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Section: Vanishing Point Detectionmentioning

confidence: 99%

On Line-based Homographies in Urban Environments

Hering¹,

Priese²,

Schmitt³

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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“…Today emphasis is more on learning of rules and stochastic modeling of constraints and relations [12]. Automatic understanding of buildings currently also includes façade analysis [17] including the grouping of semantically similar SIFT instances in lattices [13]. The main economic motive is apparently application in the games industry.…”

Section: Related Workmentioning

confidence: 99%

On the Construction of Gestalt-Algebra Instances and a Measure for their Similarity

Michaelsen¹

2010

Proceedings of the Third International Workshop on Image Mining Theory and Applications

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Four construction principles that compose more complicated perceptual gestalts from less complex ones are defined in detail: Mirror gestalts, lattice gestalts, rotational mandalas, and clusters, respectively. These can be encapsulated as constructions in a production system. Since any of the four constructions can work on any gestalt, a recursive and very expressive scheme is set up with many prospective applications in image mining. Of particular interest is such analysis for aerial and satellite images and for façade images of buildings.

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“…Therefore we apply the AGS (Automatic Grouping of Semantics) algorithm that was introduced by Priese, Schmitt, Hering in [1] for an automatic grouping of locations of a similar semantics. The advantage of the AGS is that it can find groupings in spaces with a concept of a neighborhood but without a topology.…”

Section: Cluster Analysis With the Intersection Point Neighborhoodmentioning

confidence: 99%

“…We add new ideas to i) in 5.1, where we add a wildness map to reduce noise in the Hough transformation, to ii) in 4.1, by using a finite part of Z 2 with a new intersection point neighborhood as a substitute for an "accumulator" of intersection points, and to iii) in 4.2, where we cluster intersection points to candidates for vanishing points with the AGS algorithm of [1]. The introduction of the intersection point neighborhood is our main contribution.…”

Section: Introductionmentioning

confidence: 99%