Perfecting a Video Game with Game Metrics
Vol 14, No 2: June 2016

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

Samuel Alvin Hutama (Satya Wacana Christian University)
Saptadi Nugroho (Satya Wacana Christian University)
Darmawan Utomo (Satya Wacana Christian University)

Article Info

Publish Date
01 Jun 2016

Abstract

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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Journal Info
Perfecting a Video Game with Game Metrics
Website

Abbrev

TELKOMNIKA

Publisher

Universitas Ahmad Dahlan

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...