| 000 -LEADER |
| fixed length control field |
01968nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
| control field |
ft8913 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
| control field |
20251218154657.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251218b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
| Classification number |
QA76.9 A43 A43 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
| Personal name |
Amadeo III, Alfedo M.; Bautista, Bella Marie B.; Lingad, Alexandei Paul A. |
| 245 ## - TITLE STATEMENT |
| Title |
An enhancement of Estacio-Laurente oriented fast and rotated brief (ORB) algorithm for non-real-time image-based product research |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
| Source |
volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
| Formatted contents note |
ABSTRACT: The Estacio-Laurente ORB algorithm is an improved version of the traditional ORB however, it can still detect key points from image noises and it cannot emphasize image edges that can help in detecting distinct features in that area. It can also retain only up to 1000 key points for feature matching. This limits the ability of the ORB to match all the possible distinct points an image can have. The last problem was the mismatches produced by the FLANN matcher. The existing algorithm does not have the ability to filter out outliers. The enhanced method is to improve the quality of the detected features, use the maximum possible number of detected features for matching, and improve the feature matching accuracy of the algorithm. To do this, methods such as Gaussian Blur, Canny Edge, Bayesian Optimization, and MAGSAC++ are used. The result shows that the changes in the existing algorithm improved the ability to detect good quality of features, it can detect and retain more distinct key points for feature matching, and it improves the accuracy and reliability of the matches as it is able to filter out outliers. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |