Anisotropic-Scale Junction Detection and Matching for Indoor Images

Xue, Nan; Xia, Gui-Song; Bai, Xiang; Zhang, Liangpei; Shen, Weiming

doi:10.1109/tip.2017.2754945

Cited by 39 publications

(25 citation statements)

References 42 publications

(123 reference statements)

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“…The detection of junctions has been studied for years but a detail review of them is out of the scope in this paper. Here, we briefly describe the ASJ junction detector by [Xue et al, 2017], that will be used in our work.…”

Section: Introductionmentioning

confidence: 99%

“…While buildings are often rectangular objects with unequal length of edges. To deal with this problem, [Xue et al, 2017] introduced an improved version of ACJ, called anisotropic-scale junction (ASJ) detector. Based on the junctions detected by ACJ, ASJ can get anisotropic scales in various directions of junction's branches (see Fig.2, taken from [Xue et al, 2017]).…”

Section: Introductionmentioning

confidence: 99%

“…To deal with this problem, [Xue et al, 2017] introduced an improved version of ACJ, called anisotropic-scale junction (ASJ) detector. Based on the junctions detected by ACJ, ASJ can get anisotropic scales in various directions of junction's branches (see Fig.2, taken from [Xue et al, 2017]). More details on ACJ and ASJ detectors can be found in the work of [Xia et al, 2014] and [Xue et al, 2017].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the junctions detected by ACJ, ASJ can get anisotropic scales in various directions of junction's branches (see Fig.2, taken from [Xue et al, 2017]). More details on ACJ and ASJ detectors can be found in the work of [Xia et al, 2014] and [Xue et al, 2017]. Obviously, L-junction is the main type in both two areas.…”

Section: Introductionmentioning

confidence: 99%

“…Template of isotropic-scale junction (left) defined in ACJ and anisotropic-scale junction (ASJ) (right), taken from[Xue et al, 2017]. Junctions detected by ASJ have its own scale for each branch.…”

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confidence: 99%

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Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods. * All results are available at

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confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptively Transforming Graph Matching

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Recently, many graph matching methods that incorporate pairwise constraint and that can be formulated as a quadratic assignment problem (QAP) have been proposed. Although these methods demonstrate promising results for the graph matching problem, they have high complexity in space or time. In this paper, we introduce an adaptively transforming graph matching (ATGM) method from the perspective of functional representation. More precisely, under a transformation formulation, we aim to match two graphs by minimizing the discrepancy between the original graph and the transformed graph. With a linear representation map of the transformation, the pairwise edge attributes of graphs are explicitly represented by unary node attributes, which enables us to reduce the space and time complexity significantly. Due to an efficient Frank-Wolfe methodbased optimization strategy, we can handle graphs with hundreds and thousands of nodes within an acceptable amount of time. Meanwhile, because transformation map can preserve graph structures, a domain adaptation-based strategy is proposed to remove the outliers. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph matching algorithms.

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