Comparison of Speeded-Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) Methods in Identifying Museum Objects Using Low Light Intensity Images

Abstract

Museum is a place of education and learning in the field of culture and history for all levels of society. As one of the first and largest museums in Lampung, Museum Lampung presents a variety of collections that are conditional on cultural values and are very useful if they can be identified through digital media. Speeded-Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) methods are two examples of feature extraction methods that are relatively robust for object recognition in images by finding key points. Digital media determined by the value of key points in this study are images that are classified as having low intensity with an intensity value of <2250 Lux. This study compares the two methods using the digital object media of museum objects. The image of the museum object is given treatment in terms of rotation, scaling, and cropping to test the durability of the image matching process. In terms of feature matching time, the best time is achieved by SURF with 0.16 seconds in testing of 1/3 image region. Meanwhile, the highest matching percentage was also obtained by SURF method from rotational distortion at an angle of 90 degrees which is 76.79% instead of to 63.79% of the percentage obtained by ORB.

A Review of Common Techniques for Visual Simultaneous Localization and Mapping

“…Setiawan et al compared the three classical algorithms through pictures under dark light: SIFT had the highest rotation processing accuracy despite its slower execution speed than the other two algorithms. ORB and SURF had the best comprehensive ability, but SURF was obviously inferior to the other two algorithms in the face of noise [69].…”

An Optimized Mask R-CNN with Bag-of-Visual Words and Fast+Surf Algorithm in Sharp Object Instance Segmentation for X-ray Security

“…The SURF (speed up robust features) algorithm was based on the Hessian matrix, feature detector, and multi-scale space theory; additionally, the Hessian matrix has been considered to have good accuracy and performance [13]. Scale invariant feature transform (SIFT) was the first before the SURF; the SURF was the development of SIFT, but before SURF was put into the light, the SIFT algorithm was considered weak between robustness and computational time [14].…”

Menjejak Kearifan Lokal: Pendampingan Memahami Budaya Daerah Sebagai Kunci Peningkatan Kebanggaan Identitas

“…Pendidikan mengenai kearifan lokal melalui pendampingan di Museum Lampung tidak hanya menjadi jendela ke masa lalu, tetapi juga menjadi kunci pembuka untuk memahami dan menghargai identitas budaya daerah (Yobo et al, 2023). Bagi mahasiswa, pengalaman ini bukan hanya tentang mengejar pengetahuan, tetapi juga menggali nilai-nilai yang menguatkan ikatan emosional mereka dengan warisan budaya (Setiawan et al, 2020).…”