Automatic Mosaic generation from overlapping image pairs using Maximally Stable Extremal Regions (MSER) Feature Algorithm.

Abstract

Various researchers in Digital Image Processing have developedkeen interest in the automation of object detection, description and extraction process used for various applications, and this has led to the development of series of feature detection and extraction models, one of which is the Maximally Stable Extremal Regions Features Algorithm (MSER). This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth online image data repository and image pairs acquired from an Unmanned Aerial Vehicle (UAV) flight mission. Sum of Square Difference (SSD) and Bilinear interpolation models were used to establish the similarity measure between the registered images, resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus (RANSAC) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy. While an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. The obtained speed and accuracy shows that MSER is a very robust model and as such, can be deployed as a feature detection and extraction model in the development of an automatic image registration scheme.