Robust image salient regional extraction and matching based on DoGSS-MSERs

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• 作者：Meng-meng Zhang ; Ze-ming Li ; Hui-hui Bai ; Yan Sun
• 关键词：DoGSS-MSERs ; SIFT ; MSERs ; Stable salient region ; Region matching
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：February, 2014
• 年：2014
• 卷：125
• 期：3
• 页码：1469-1473
• 全文大小：2368 K

文摘

The current paper presents a robust image salient region extraction and matching algorithm based on the maximally stable extremal regions in the difference of Gaussian scale space (DoGSS-MSERs) combined with scale-invariant feature transform (SIFT) algorithm and the maximally stable extremal regions (MSERs) algorithm. First, the difference of Gaussian scale space (DoGSS) is constructed using image scale-space theory. The maximally stable extremal regions are then calculated and the stable component is extracted with blur-invariant and scale-invariant property using the stable method in the DoGSS. Finally, the regions are described with a novel region descriptor, thereby achieving matching. The experiments show that the feature regions extracted in the current paper inherit the good properties of SIFT and MSERs (scale-invariant and affine-invariant) and are more stable and more accurate for matching.