Open AccessScale Invariant Feature Transform Descriptor Robustness Analysis to Brightness Changes of Robowaiter Vision Sensor System
Author(s) -
Taufiq Nuzwir Nizar,
Sri Supatmi,
E. P. Putro
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/662/5/052004
Subject(s) - scale invariant feature transform , brightness , artificial intelligence , computer vision , robustness (evolution) , invariant (physics) , affine transformation , computer science , pattern recognition (psychology) , mathematics , feature extraction , biochemistry , chemistry , physics , pure mathematics , optics , mathematical physics , gene
The purpose of this research is to identify problem detection features in computer vision that are affected by changes in brightness. The presented descriptor is Scale Invariant Feature Transform (SIFT). The method used in this study is an algorithm in computer vision to detect and describe local feature in image which robustly identify object and invariant to uniform scaling, orientation, brightness changes, and partially invariant to affine distortion. We implement this algorithm to Robowaiters object detection system that must detect and recognize objects around its task like food, beverage, refrigerator, and any kitchen objects. For this analysis case, we use beverage box image for sample image. The algorithm detects and recognize the image in normal brightness, and then the image brightness value increased and decreased. The result is that the algorithm successfully detects and recognizes the object presented and distinguishes it with a success rate of 93.5% increase in image brightness and 95.5% decrease in image brightness. it can be concluded that the SIFT algorithm is robust enough to change the lighting for our case.