Feature Based Multi View Image Registration by Detecting the Feature with Fuzzy Logic for Corner Detection

This paper aim to Present accurate feature base registration by detecting the feature with Fuzzy logic for corner detection. Image registration is process used to match two or more partially overlapping image taken for example at different times ,from different sensors, or from different viewpoints and stitch these image into one panoramic image comprising whole scene. It is a fundamental image processing technique very useful in integrating information from different sensors, finding changes in image taken at different time, inferring three-dimensional information from stereo images and recognizing model-based objects. The paper presents a corner detection algorithm for feature detection which employs such fuzzy reasoning. The robustness of the proposed algorithm is compared to well-known conventional Harris corner detectors and its performance is also tested over a noise image. DOI: 10.17762/ijritcc2321-8169.150616

Fuzzification of Gabor Filter for License Plate Detection Application

Disertacija prikazuje novi algoritam za detekciju i izdvajanje registarskih tablica iz slike vozila koristeći fazi 2D Gaborov filtar. Parametri filtra: orijentacija i talasna dužina su fazifikovani u cilju optimizacije odziva Gaborovog filtra i postizanja dodatne selektivnosti filtra. Prethodno navedeni parametri dominiraju u rezultatu filtriranja. Bellova i trougaona funkcija pripadnosti pokazale su se kao najbolji izbor pri fazifikaciji parametara filtra. Algoritam je evaluiran nad više baza slika i postignuti su zadovoljavajući rezultati. Komponente od interesa su efikasno izdvojene i postignuta značajna otpornost na šum i degradaciju na slici.The thesis presents a new algorithm for detection and extraction of license plates from a vehicle image using a fuzzy two-dimensional Gabor filter. The filter parameters, orientation and wavelengths are fuzzified to optimize the Gabor filter’s response and achieve a greater selectivity. It was concluded that Bell’s function and triangular membership function are the most efficient methods for fuzzification. Algorithm was evaluated on several databases and has provided satisfactory results. The components of interest were efficiently extracted, and the procedure was found to be very noise-resistant