The Global–Local transformation for noise resistant shape representation

Raftopoulos, Konstantinos A.; Kollias, Stefanos

doi:10.1016/j.cviu.2011.03.009

Cited by 10 publications

(22 citation statements)

References 49 publications

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“…Due to their location at the extremes of the boundary, Global Vertices are considered undisputed points of extreme curvature exposing this way a hidden relationship between location and curvature. The present paper builds on the results of [1] and [25] adding the following elements:…”

Section: Contributionmentioning

confidence: 99%

“…An attempt to address the above problems is presented, based on a global definition of curvature that permits noise invariance and differentiation based on location perceptual characteristics. The global definition of curvature presented herein is based on the theoretical findings of [1]. Curvature at a point is seen in relation to the rate of change (per infinitesimal arc length) of the point's distance, to the rest of the curve.…”

Section: Introductionmentioning

confidence: 99%

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On the Beneficial Effect of Noise in Vertex Localization

et al. 2017

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A theoretical and experimental analysis related to the effect of noise in the task of vertex identification in unknown shapes is presented. Shapes are seen as real functions of their closed boundary. An alternative global perspective of curvature is examined providing insight into the process of noiseenabled vertex localization. The analysis reveals that noise facilitates in the localization of certain vertices. The concept of noising is thus considered and a relevant global method for localizing Global Vertices is investigated in relation to local methods under the presence of increasing noise. Theoretical analysis reveals that induced noise can indeed help localizing certain vertices if combined with global descriptors. Experiments with noise and a comparison to localized methods validate the theoretical results.

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Section: Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Beneficial Effect of Noise in Vertex Localization

et al. 2017

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“…To introduce enhanced noise resistance to the extracted skeleton, we use the Global-Local transformation [2] to map the noise contour back to a smoothed version. Starting then from the skeleton endpoints (marked as small circles in the images below), the pseudorandom watermark sequence is embedded in the DCT domain of the blocks along the skeleton's Eulerian tour.…”

Section: The Methodsmentioning

confidence: 99%

“…The situation is illustrated in [1] where elaborate methods of high complexity are proposed to alleviate this problem. The main contribution of this paper is that it incorporates a certain noise resistant method of extracting shape information [2] during the block-based watermark embedding process in such a way that the watermarked block's location is readily identifiable at the retrieval phase, even after RST transformation, cropping or Gaussian boundary noise. The proposed system is tested in its ability to provide geometrically resistant copyright protection of semantic objects having an explicit boundary, most suited for protecting certain objects in an image, or explicit creations of artists that have to be distributed and reused in different contexts.…”

Section: Introductionmentioning

confidence: 99%

Watermark Resynchronization: An Efficient Approach Based on Eulerian Tours around a Robust Skeleton

Raftopoulos¹,

Ntalianis²,

Tzouveli³

et al. 2013

Communications and Multimedia Security

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Abstract. The existing block-based approaches, albeit successful in resisting frequency domain attacks, are sensitive to geometric distortions due to the lack of reference in repositioning the block grid. In this paper an RST-invariant block-based approach is proposed, aiming at protecting image objects. The term "image object" refers to semantically contiguous parts of images that have a specific contour boundary. The proposed approach is based on shape information, since the watermark is embedded in image blocks, the location and orientation of which are defined by Eulerian tours that are appropriately arranged in layers, around an object's robust skeleton. Images from the Polymathic project are used to illustrate the technique.

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“…There are multiple linear and nonlinear approaches for mapping data from source to target space [19,27,31]. Most of the approaches use the more discriminant space for classification or segmentation purposes.…”

Section: Related Workmentioning

confidence: 99%

Mapping Forests: A Comprehensive Approach for Nonlinear Mapping Problems

Jampour

Moin²,

et al. 2017

J Math Imaging Vis

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A new and robust mapping approach is proposed entitled mapping forests (MFs) for computer vision applications based on regression transformations. Mapping forests relies on learning nonlinear mappings deduced from pairs of source and target training data, and improves the performance of mappings by enabling nonlinear transformations using forests. In contrast to previous approaches, it provides automatically selected mappings, which are naturally nonlinear. MF can provide accurate nonlinear transformations to compensate the gap of linear mappings or can generalize the nonlinear mappings with constraint kernels. In our experiments, we demonstrate that the proposed MF approach is not only on a par or better than linear mapping approaches and the state-of-the-art, but also is very time efficient, which makes it an attractive choice for real-time applications. We evaluated the efficiency and performance of the MF approach using the BU3DFE and Multi-PIE datasets for multi-view facial expression recognition application, and Set5, Set14 and SuperTex136 datasets for single image super-resolution application.B Mahdi Jampour

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The Global–Local transformation for noise resistant shape representation

Cited by 10 publications

References 49 publications

On the Beneficial Effect of Noise in Vertex Localization

On the Beneficial Effect of Noise in Vertex Localization

Watermark Resynchronization: An Efficient Approach Based on Eulerian Tours around a Robust Skeleton

Mapping Forests: A Comprehensive Approach for Nonlinear Mapping Problems

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