Wu, K.; Qi, Y.; Ma, Y.; Liu, C.; Jiang, D. Reflection Symmetry Axes Localization Framework for Object Intelligent Perception based on Salient Symmetry Feature. Preprints2020, 2020100335. https://doi.org/10.20944/preprints202010.0335.v1
APA Style
Wu, K., Qi, Y., Ma, Y., Liu, C., & Jiang, D. (2020). Reflection Symmetry Axes Localization Framework for Object Intelligent Perception based on Salient Symmetry Feature. Preprints. https://doi.org/10.20944/preprints202010.0335.v1
Chicago/Turabian Style
Wu, K., Cheng Liu and Dazhi Jiang. 2020 "Reflection Symmetry Axes Localization Framework for Object Intelligent Perception based on Salient Symmetry Feature" Preprints. https://doi.org/10.20944/preprints202010.0335.v1
Abstract
This paper presents an optimized feature-centered reflection symmetry axis detection and localization framework for object perception. The proposed framework is formed to obtain an improved reflection symmetry axis based on the salient symmetry feature. It starts with a refined Multi-scale Saliency Symmetry Model (MSSM), which is realized by applying isotropic symmetry operator on salient points in scale-space rather than all pixels. In each scale, salient points are initially extracted as local extremal from an image, and they are further refined by a multi-scale implementation for generating salient symmetry feature maps. A Symmetric Transformation Matrix is then computed using the optimal feature matching pairs, which can be explicitly used as an abstract representation of the constraint regions of symmetry objects in an image to optimize the performance of the potential symmetry axis detection. The framework has been investigated experimentally both on the classical dataset from a symmetry detection challenge and the latest dataset. It has shown that the framework can get a better or comparative result and also can be further adapted into terminated human--computer equipment for reflection symmetry object perception and tracking.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
