A new descriptor resistant to affine transformation and monotonic intensity change

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• 作者：Zeyi Huang ; Wenxiong Kang ; Qiuxia Wu ; Xiaopeng Chen
• 关键词：Local invariant descriptor ; Affine transformation ; Monotonic intensity change ; Intensity order
• 刊名：Computer Vision and Image Understanding
• 出版年：March, 2014
• 年：2014
• 卷：120
• 期：Complete
• 页码：117-125
• 全文大小：2430 K

文摘

A substantial number of local feature extraction and description methodologies have been proposed as image recognition algorithms. However, these algorithms do not exhibit adequate performance with regard to repeatability, accuracy, and time consumption for both affine transformation and monotonic intensity change. In this paper, we propose a new descriptor, named Resistant to Affine Transformation and Monotonic Intensity Change (RATMIC). Unlike traditional descriptors, we utilize an adaptive division strategy and intensity order to construct the new descriptor, which is actually resistant to affine transformation and monotonic intensity change. Extensive experiments demonstrate the effectiveness and efficiency of the new descriptor compared to existing state-of-the-art descriptors.