Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
This work addresses the challenge of incomplete symmetry detection in computer vision, offering an incremental improvement for applications like image analysis and object recognition.
The paper tackles the problem of detecting global reflection symmetries in images by proposing a new scheme that uses Log-Gabor filters for edge-based feature extraction and a voting scheme with textural and color histograms, achieving superior results on multiple datasets.
Symmetry is one of the significant visual properties inside an image plane, to identify the geometrically balanced structures through real-world objects. Existing symmetry detection methods rely on descriptors of the local image features and their neighborhood behavior, resulting incomplete symmetrical axis candidates to discover the mirror similarities on a global scale. In this paper, we propose a new reflection symmetry detection scheme, based on a reliable edge-based feature extraction using Log-Gabor filters, plus an efficient voting scheme parameterized by their corresponding textural and color neighborhood information. Experimental evaluation on four single-case and three multiple-case symmetry detection datasets validates the superior achievement of the proposed work to find global symmetries inside an image.