Multiscale moment-based technique for object matching and recognition

HweeLi Thio; Liya Chen; Eam-Khwang Teoh

doi:10.1117/12.380064

21 March 2000 Multiscale moment-based technique for object matching and recognition

HweeLi Thio, Liya Chen, Eam-Khwang Teoh

Author Affiliations +

Proceedings Volume 3966, Machine Vision Applications in Industrial Inspection VIII; (2000) https://doi.org/10.1117/12.380064
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

A new method is proposed to extract features from an object for matching and recognition. The features proposed are a combination of local and global characteristics -- local characteristics from the 1-D signature function that is defined to each pixel on the object boundary, global characteristics from the moments that are generated from the signature function. The boundary of the object is first extracted, then the signature function is generated by computing the angle between two lines from every point on the boundary as a function of position along the boundary. This signature function is position, scale and rotation invariant (PSRI). The shape of the signature function is then described quantitatively by using moments. The moments of the signature function are the global characters of a local feature set. Using moments as the eventual features instead of the signature function reduces the time and complexity of an object matching application. Multiscale moments are implemented to produce several sets of moments that will generate more accurate matching. Basically multiscale technique is a coarse to fine procedure and makes the proposed method more robust to noise. This method is proposed to match and recognize objects under simple transformation, such as translation, scale changes, rotation and skewing. A simple logo indexing system is implemented to illustrate the performance of the proposed method.

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HweeLi Thio, Liya Chen, and Eam-Khwang Teoh "Multiscale moment-based technique for object matching and recognition", Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); https://doi.org/10.1117/12.380064

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