Real-Time Image Matching.pdfVIP

Studies in Surveying and Mapping Science (SSMS) Volume 1 Issue 2, June 2013 /ssms 17 Real-Time Image Matching Using the Trajkovic and Hedley Algorithm Mary Jane Samonte, Rizan Joseph Alcantara Jr., Marvin Evangelista, Kevin Patrick Rabang School of Information Technology, Mapua Institute of Technology Makati City, Philippines mjcsamonte@.ph; rjjdalcantara@.ph; mpevangelista@.ph; kporabang@.ph Abstract One way to acquire information is through surveillance videos. Changes in the video feed can be seen more accurately through image matching. There are several approaches to image matching; and one of these is the SIFT algorithm. Aside from SIFT, Trajkovic and Hedley is another, if not the fastest and most efficient, of the corner detection algorithms. By replacing the keypoint extraction of the SIFT algorithm to the Trajkovic and Hedley corner detection algorithm, the SIFT algorithm will become faster. The study is composed of different phases, including Pre-Processing, Feature Extraction and the Matching Phase. This study has two objectives: to introduce a new method when it comes to image matching, especially with the use of the SIFT algorithm, and the use of other approaches as alternatives used in surveillance and security. Keywords Trajkovic and Hedley; SIFT Algorithm; Keypoint Matching; Corner Detection Introduction “Image matching” is the process of matching two images to see whether they remain the same or if changes have occurred. Matching is also a relevant and fundamental aspect for many problems in computer vision such as scene recognition, motion tracking and others. There are several approaches to image matching, one of which is keypoint matching. Most keypoint matching methods use the SIFT algorithm (Scale Invariant Feature Transform). SIFT is an image processing algorithm which can be used to detect distinct features between two images. The SIFT algorithm has