Automatic extraction of texture-periodicity using superposition of distance matching functions and their forward differences

Asha, V; Nagabhushan, P.; Bhajantri, Nagappa U.

doi:10.1016/j.patrec.2011.11.027

Cited by 23 publications

(13 citation statements)

References 12 publications

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“…The Dutch artist, Escher took a particular interest in patterns formed by repeating asymmetric shapes and discovered a local structure leading to the same wallpaper patterns; his work on symmetry is examined in [7]. Regular repetitive patterns such as Wallpaper and Frieze groups are even utilized in quite practical problems; for example, to analyze human gait [8] or to achieve automatic fabric defect detection in 2D patterned textures [9,10,11].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Planar Ornament Patterns via Motif Asymmetry Assumption and Local Connections

Adanova

2018

J Math Imaging Vis

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Planar ornaments, a.k.a. wallpapers, are regular repetitive patterns which exhibit translational symmetry in two independent directions. There are exactly 17 distinct planar symmetry groups. We present a fully automatic method for complete analysis of planar ornaments in 13 of these groups, specifically, the groups called p6m, p6, p4g, p4m, p4, p31m, p3m, p3, cmm, pgg, pg, p2 and p1. Given the image of an ornament fragment, we present a method to simultaneously classify the input into one of the 13 groups and extract the so called fundamental domain (FD), the minimum region that is sufficient to reconstruct the entire ornament. A nice feature of our method is that even when the given ornament image is a small portion such that it does not contain multiple translational units, the symmetry group as well as the fundamental domain can still be defined. This is because, in contrast to common approach, we do not attempt to first identify a global translational repetition lattice. Though the presented constructions work for quite a wide range of ornament patterns, a key assumption we make is that the perceivable motifs (shapes that repeat) alone do not provide clues for the underlying symmetries of the ornament. In this sense, our main target is the planar arrangements of asymmetric interlocking shapes, as in the symmetry art of Escher.

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Section: Related Workmentioning

confidence: 99%

Analysis of Planar Ornament Patterns via Motif Asymmetry Assumption and Local Connections

Adanova

2018

J Math Imaging Vis

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“…This characteristic makes our algorithm flexible and controllable. In our proposal, this parameter is adjusted empirically, but still, it can be automatically approximated with methods to obtain the fundamental pattern or texel size, like the ones proposed in [18,19]. In the same way, the boundary zone should avoid mismatching features across the borders, but at the same time, be tolerant to the border constraints.…”

Section: Dynamic Texture Synthesis In Spatial Domainmentioning

confidence: 99%

Local spatiotemporal features for dynamic texture synthesis

et al. 2014

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In this paper, we study the use of local spatiotemporal patterns in a non-parametric dynamic texture synthesis method. Given a finite sample video of a texture in motion, dynamic texture synthesis may create a new video sequence, perceptually similar to the input, with an enlarged frame size and longer duration. In general, non-parametric techniques select and copy regions from the input sample to serve as building blocks by pasting them together one at a time onto the outcome. In order to minimize possible discontinuities between adjacent blocks, the proper representation and selection of such pieces become key issues. In previous synthesis methods, the block description has been based only on the intensities of pixels, ignoring the texture structure and dynamics. Furthermore, a seam optimization between neighboring blocks has been a fundamental step in order to avoid discontinuities. In our synthesis approach, we propose to use local spatiotemporal cues extracted with the local binary pattern from three orthogonal plane (LBP-TOP) operator, which allows us to include in the video characterization the appearance and motion of the dynamic texture. This improved representation leads us to a better fitting and matching between adjacent blocks, and therefore, the spatial similarity, temporal behavior, and continuity of the input can be successfully preserved. Moreover, the proposed method simplifies other approximations since no additional seam optimization is needed to get smooth transitions between video blocks. The experiments show that the use of the LBP-TOP representation outperforms other methods, without generating visible discontinuities or annoying artifacts. The results are evaluated using a double-stimulus continuous quality scale methodology, which is reproducible and objective. We also introduce results for the use of our method in video completion tasks. Additionally, we hereby present that the proposed technique is easily extendable to achieve the synthesis in both spatial and temporal domains.

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“…The periodicity and regularity of the grid allows for the calculation of registration parameters under deformation. In the literature, methods for identifying the periodicity of structural textures have been proposed based on Fourier spectral analysis (FSA) [22], DIC [26], co-occurrence matrix [20], and discrepancy norm [21]. These methods have various weaknesses: FSA is not suitable for the periodicity estimation of textures of insufficient large size which lack salient peaks in the power spectrum; Cooccurrence matrix methods are unacceptably slow for large textures and sensitive to distortion [20]; Discrepancy norm is sensitive to image contrast because it relies on the difference between pixel values [23].…”

Section: Introductionmentioning

confidence: 99%