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Proceedings ArticleDOI

Reflection Symmetry Detection by Embedding Symmetry in a Graph

TL;DR: This work exploits the estimated boundary of the object and describes a boundary pixel using only the estimated normal of the boundary segment around the pixel to embed the symmetry axes in a graph as cliques to robustly detect the symmetry axis.
Abstract: Reflection symmetry is ubiquitous in nature and plays an important role in object detection and recognition tasks. Most of the existing methods for symmetry detection extract and describe each keypoint using a descriptor and a mirrored descriptor. Two keypoints are said to be mirror symmetric key-points if the original descriptor of one keypoint and the mirrored descriptor of the other keypoint are similar. However, these methods suffer from the following issue. The background pixels around the mirror symmetric pixels lying on the boundary of an object can be different. Therefore, their descriptors can be different. However, the boundary of a symmetric object is a major component of global reflection symmetry. We exploit the estimated boundary of the object and describe a boundary pixel using only the estimated normal of the boundary segment around the pixel. We embed the symmetry axes in a graph as cliques to robustly detect the symmetry axes. We show that this approach achieves state-of-the-art results in a standard dataset.
Citations
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Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper presented two shape signature-based reflection symmetry detection methods with their theoretical underpinning and empirical evaluation, which can effectively deal with compound shapes which are challenging for traditional contour-based methods.

8 citations

Journal ArticleDOI
TL;DR: In this article, a stable metric is proposed to extract subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences, and the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries.
Abstract: This work addresses the challenging problem of reflection symmetry detection in unconstrained environments. Starting from the understanding on how the visual cortex manages planar symmetry detection, it is proposed to treat the problem in two stages: i) the design of a stable metric that extracts subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences; ii) the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries. Since these operations are related to the way the human brain performs planar symmetry detection, a better correspondence can be established between the outcomes of the proposed algorithm and a human-constructed ground truth. When compared to the testing sets used in recent symmetry detection competitions, a remarkable performance gain can be observed. In additional, further validation has been achieved by conducting perceptual validation experiments with users on a newly built dataset.

6 citations

Proceedings ArticleDOI
01 Oct 2022
TL;DR: This work points out an efficient detector of reflectionally symmetric shapes by addressing a class of projection-based signatures that are structured by a generalized $\mathcal{R}_{fm}$-transform model in accordance with reflectional symmetry detection.
Abstract: Analyzing reflectionally symmetric features inside an image is one of the important processes for recognizing the peculiar appearance of natural and man-made objects, biological patterns, etc. In this work, we will point out an efficient detector of reflectionally symmetric shapes by addressing a class of projection-based signatures that are structured by a generalized $\mathcal{R}_{fm}$-transform model. To this end, we will firstly prove the $\mathcal{R}_{fm^{-}}$transform in accordance with reflectional symmetry detection. Then different corresponding $\mathcal{R}_{fm}$-signatures of binary shapes are evaluated in order to determine which the corresponding exponentiation of the $\mathcal{R}_{fm}$-transform is the best for the detection. Experimental results of detecting on single/compound contour-based shapes have validated that the exponentiation of 10 is the most discriminatory, with over 2.7% better performance on the multiple-axis shapes in comparison with the conventional one. Additionally, the proposed detector also outperforms most of other existing methods. This finding should be recommended for applications in practice.