Reflection Symmetry Detection via Appearance of Structure Descriptor
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 recognize
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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.
Keywords: Symmetry detection
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· Structure · Feature · Reflection
Introduction
An object with repeated patterns in balance such as rotation and bilateral reflection symmetry can be easily recognized out of background. Symmetry pattern on an object works as a salient visual feature attracting human attention. Various types of symmetry (rotation, reflection, translation, etc.) are mathematically defined and represented by a set of similar patterns located under certain repetition rules. Symmetry is omnipresent in real world objects such as snow crystal, face, flower, butterfly, and most of human-made objects such as buildings, cars, clothes, etc. Symmetry has been studied in computer vision as a discriminative visual clue in object recognition, shape matching and scene understanding [1]. Reflection symmetry is the most common and essential type that can be found almost everywhere in the surroundings. However, reflection symmetry detection from real world images is not a trivial task due to image noises, partial occlusion, perspective distortion and the lack of robust features to support the symmetry. Many researchers have devoted to practical and robust reflection symmetry detection method under various challenging environments as extensively summarized in [2]. Most of symmetry detection methods are based on sparsely detected c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part III, LNCS 9907, pp. 3–18, 2016. DOI: 10.1007/978-3-319-46487-9 1
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I.R. Atadjanov and S. Lee
Fig. 1. Proposed reflection symmetry detection method using our appearance of structure (AoS) feature
feature points describing respective local neighborhood. Marola [3] introduces an algebraic technique for detecting a planar bilateral symmetry in Euclidean space. They fit polynomials to input image and detect bilateral symmetry on those fitted polynomials. Prasad and Yegnanarayana [4] propose gradient vector flow and symmetry saliency map for bilateral symmetry detection. They use edge gradients in order to be robust to illumination change. Mitra et al. [5] define general regularity in 3D geometry based on a region based matching. In order to figure out pot
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