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Features Deletion on Multiple Objects Recognition using Speeded-Up Robust Features, Scale Invariant Feature Transform and Randomized KD-Tree

Journal from gdlhub / 2016-11-05 03:00:06
Oleh : Samuel Alvin Hutama, Saptadi Nugroho, Darmawan Utomo, Telkomnika
Dibuat : 2016-06-01, dengan 1 file

Keyword : multiple object recognition; SURF; SIFT; randomized KD-Tree;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/3461

This paper presents a multiple objects recognition method using speeded-up robust features (SURF) and scale invariant feature transform (SIFT) algorithm. Both algorithms are used for finding features by detecting keypoints and extracting descriptors on every object. The randomized KD-Tree algorithm is then used for matching those descriptors. The proposed method is deletion of certain features after an object has been registered and repetition of successful recognition. The method is expected to recognize all of the registered objects which are shown in an image. A series of tests is done in order to understand the characteristic of the recognizable object and the method capability to do the recognition. The test results show the accuracy of the proposed method is 97% using SURF and 88.7% using SIFT.

Deskripsi Alternatif :

This paper presents a multiple objects recognition method using speeded-up robust features (SURF) and scale invariant feature transform (SIFT) algorithm. Both algorithms are used for finding features by detecting keypoints and extracting descriptors on every object. The randomized KD-Tree algorithm is then used for matching those descriptors. The proposed method is deletion of certain features after an object has been registered and repetition of successful recognition. The method is expected to recognize all of the registered objects which are shown in an image. A series of tests is done in order to understand the characteristic of the recognizable object and the method capability to do the recognition. The test results show the accuracy of the proposed method is 97% using SURF and 88.7% using SIFT.

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