Detecting and matching interest points in relative scale

• Autorzy: Islam M. S. , Sluzek A. , Zhu L.
• Języki publikacji: EN

Abstrakty

EN

The same object seen in two different images can be geometrically and photometrically transformed. In this paper, a method of interest point detection and matching is described for the same object in different images. One of the main considerations is the change in the object scale. In this method, a reference scale is assigned to a particular instance of the object, and the change of scale is represented by a relative scale. Then, Harris' relative scale method is used for interest point detection. This method is robust to linear geometric transformations. A heuristic method for threshold selection is also described for robustness to intensity changes in a cluttered environment with partial occlusions. The repeatability rate of interest points for this method is higher then that for the existing methods. For the matching process, a local invariant descriptor is computed in the relative scale for each of the detected interest points. A hashing technique is applied to find the matches efficiently. The matching method enables finding a good number of correct matches for different types of transformations in a cluttered environment and one with partial occlusions. The proposed single scale detection and matching method could be effectively used for many practical applications, where the relative scale of the object can be predicted in advance.

Słowa kluczowe

EN

relative scale; interest point; local invariant descriptor; repeatability rate; moment invariant; information content; multidimensional indexing

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2005
• Tom: Vol. 14, No. 3
• Strony: 259--283
• Opis fizyczny: Bibliogr. 24 poz., il., wykr.

Twórcy

autor

Islam M. S.

• Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore 639798

autor

Sluzek A.

• Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore 639798
• SWPS, Warsaw, Poland

autor

Zhu L.

• Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore 639798

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