2017/04/20 by Erkan Bostanci, Erkan Bostancı, Nadia Kanwal +7 · 9 citations
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Algorithm #Arithmetic #Artificial intelligence #Binary number #Brute force #Computer science #Computer security #Computer vision #Fuzzy logic #Image (mathematics) #Image Retrieval and Classification Techniques #Matching (statistics) #Mathematics #Pattern recognition (psychology) #Robotics and Sensor-Based Localization #Statistics #cs.CV
paper · pdf · doi:10.48550/arxiv.1704.06018
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/04/20 · openalex publication_date 2017/04/20 · arxiv updated 2017/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Matching of binary image features is an important step in many different computer vision applications. Conventionally, an arbitrary threshold is used to identify a correct match from incorrect matches using Hamming distance which may improve or degrade the matching results for different input images. This is mainly due to the image content which is affected by the scene, lighting and imaging conditions. This paper presents a fuzzy logic based approach for brute force matching of image features to overcome this situation. The method was tested using a well-known image database with known ground truth. The approach is shown to produce a higher number of correct matches when compared against constant distance thresholds. The nature of fuzzy logic which allows the vagueness of information and tolerance to errors has been successfully exploited in an image processing context. The uncertainty arising from the imaging conditions has been overcome with the use of compact fuzzy matching membership functions.