초록

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Abstract ▼ AI-Helper

Object recognition-based applications have been more required because widespread use of mobile devices equipped with camera has rapidly grown. Object recognition rate of SIFT(Scale Invariant Feature Transform) matching has disadvantage when detecting same objects but slightly different in detail exc...

Object recognition-based applications have been more required because widespread use of mobile devices equipped with camera has rapidly grown. Object recognition rate of SIFT(Scale Invariant Feature Transform) matching has disadvantage when detecting same objects but slightly different in detail except for scale and rotation because SIFT is one-to-one matching-based object recognition. To solve this problem, we propose SIFT template feature which represents features extracted from an object in many pictures. We perform clustering in order to make SIFT template feature then each centroid of clusters is selected as template features. We conduct TF/IDF likely manner while clustering and Adaptive Threshold matching to elevate recognition accuracy. Experimental results using SIFT template based matching show that object recognition rate of proposed method is more higher than original SIFT matching and BoVW(Bag of Visual Word) using SIFT in such case objects, which has low interclass similarity like brand logo and road sign, are recognized.

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