Reflection Symmetry Detection by Embedding Symmetry in a Graph
12 May 2019-pp 2147-2151
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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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.
Abstract: • A solid theoretical foundation of R-signature and LIP-signature about symmetric properties of a given shape is represented. • A verification process is theoretically justified to remove the false candidates based on an efficient symmetry measure. • Two novel datasets (UTLN-SRA & UTLN-MRA) with single & multiple reflections are designed for evaluating symmetry detectors. • A new evaluation protocol based on a lost measure is presented to evaluate reflectional symmetry detectors. • Comprehensive evaluations have verified that our proposed detectors perform well on binary images compared to state of the art. We present two novel shape signature-based reflection symmetry detection methods with their theoretical underpinning and empirical evaluation. LIP-signature and R-signature share similar beneficial properties allowing to detect reflection symmetry directions in a high-performing manner. For the shape signature of a given shape, its merit profile is constructed to detect candidates of symmetry direction. A verification process is utilized to eliminate the false candidates by addressing Radon projections. The proposed methods can effectively deal with compound shapes which are challenging for traditional contour-based methods. To quantify the symmetric efficiency, a new symmetry measure is proposed over the range [0, 1]. Furthermore, we introduce two symmetry shape datasets with a new evaluation protocol and a lost measure for evaluating symmetry detectors. Experimental results using standard and new datasets suggest that the proposed methods prominently perform compared to state of the art.
8 citations
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
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.
References
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TL;DR: A simple and yet robust Hough transform algorithm is proposed to detect and analyze reflectional symmetry and skew-symmetry (reflectional symmetry under parallel projection) under the presence of noise and occlusion.
Abstract: In this paper, a simple and yet robust Hough transform algorithm is proposed to detect and analyze reflectional symmetry and skew-symmetry (reflectional symmetry under parallel projection). It is applicable to shapes that contain global, local and slightly deformed reflectional/skew-symmetries under the presence of noise and occlusion.
46 citations
"Reflection Symmetry Detection by Em..." refers background in this paper
...The approaches for reflection symmetry detection in real-world images can be categorized as voting based approaches [12, 13, 14, 21, 4, 6, 5, 15, 16, 17] and multiple model fitting approaches in [18, 19, 20]....
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01 Jul 2017
TL;DR: This report provides a detailed summary of the evaluation methodology for each type of symmetry detection algorithm validated, and demonstrates and analyzes quantified detection results in terms of precision-recall curves and F-measures for all algorithms evaluated.
Abstract: Motivated by various new applications of computational symmetry in computer vision and in an effort to advance machine perception of symmetry in the wild, we organize the third international symmetry detection challenge at ICCV 2017, after the CVPR 2011/2013 symmetry detection competitions. Our goal is to gauge the progress in computational symmetry with continuous benchmarking of both new algorithms and datasets, as well as more polished validation methodology. Different from previous years, this time we expand our training/testing data sets to include 3D data, and establish the most comprehensive and largest annotated datasets for symmetry detection to date; we also expand the types of symmetries to include densely-distributed and medial-axis-like symmetries; furthermore, we establish a challenge-and-paper dual track mechanism where both algorithms and articles on symmetry-related research are solicited. In this report, we provide a detailed summary of our evaluation methodology for each type of symmetry detection algorithm validated. We demonstrate and analyze quantified detection results in terms of precision-recall curves and F-measures for all algorithms evaluated. We also offer a short survey of the paper-track submissions accepted for our 2017 symmetry challenge.
44 citations
01 Jan 2009
TL;DR: A novel and robust method for localizing and segmenting bilaterally symmetric patterns from real-world images that overcomes the limitations of the previous local-feature based approaches by efciently exploring the image space to grow symmetry beyond the detected symmetric features.
Abstract: We present a novel and robust method for localizing and segmenting bilaterally symmetric patterns from real-world images. On the basis of symmetrically matched pairs of local features, our method expands and merges condent local symmetric region matches by exploiting both photometric similarity and geometric consistency via our new symmetry-growing framework. It overcomes the limitations of the previous local-feature based approaches by efciently exploring the image space to grow symmetry beyond the detected symmetric features. The experimental evaluation demonstrates that our method successfully detects and segments multiple symmetric patterns from real-world images, and clearly outperforms the state-of-the-art methods in accuracy and robustness.
43 citations
"Reflection Symmetry Detection by Em..." refers background in this paper
...The approaches for reflection symmetry detection in real-world images can be categorized as voting based approaches [12, 13, 14, 21, 4, 6, 5, 15, 16, 17] and multiple model fitting approaches in [18, 19, 20]....
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17 Jun 2006
TL;DR: A method is presented for efficiently detecting bilateral symmetry on planar surfaces under perspective projection able to detect local or global symmetries, locate symmetric surfaces in complex backgrounds, and detect multiple incidences of symmetry.
Abstract: A method is presented for efficiently detecting bilateral symmetry on planar surfaces under perspective projection. The method is able to detect local or global symmetries, locate symmetric surfaces in complex backgrounds, and detect multiple incidences of symmetry. Symmetry is simultaneously evaluated across all locations, scales, orientations and under perspective skew. Feature descriptors robust to local affine distortion are used to match pairs of symmetric features. Feature quadruplets are then formed from these symmetric feature pairs. Each quadruplet hypothesises a locally planar 3D symmetry that can be extracted under perspective distortion. The method is posed independently of a specific feature detector or descriptor. Results are presented demonstrating the efficacy of the method for detecting bilateral symmetry under perspective distortion. Our unoptimised Matlab implementation, running on a standard PC, requires of the order of 20 seconds to process images with 1,000 feature points.
41 citations
"Reflection Symmetry Detection by Em..." refers methods in this paper
...used feature descriptors that are robust to local affine distortion to find the symmetric feature pairs [23]....
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08 Oct 2016
TL;DR: This work proposes a new reflection symmetry detection method extracting robust 4-dimensional Appearance of Structure descriptors based on a set of outstanding neighbourhood edge segments in multiple scales based on sparsely detected local features describing the appearance of their neighborhood.
Abstract: Symmetry in visual data represents repeated patterns or shapes that is easily found in natural and human-made objects. Symmetry pattern on an object works as a salient visual feature attracting human attention and letting the object to be easily recognized. Most existing symmetry detection methods are based on sparsely detected local features describing the appearance of their neighborhood, which have difficulty in capturing object structure mostly supported by edges and contours. In this work, we propose a new reflection symmetry detection method extracting robust 4-dimensional Appearance of Structure descriptors based on a set of outstanding neighbourhood edge segments in multiple scales. Our experimental evaluations on multiple public symmetry detection datasets show promising reflection symmetry detection results on challenging real world and synthetic images.
26 citations