Simulation and Performance Analysis of Feature Extraction and Matching Algorithms for Image Processing Applications

Feature extraction and matching has the limelight in all almost all the fields ranging from biomedical to exploratory research. It has ubiquitous applications in present world that is moving at a breathtaking pace towards automation. The algorithms used for feature extraction are application specific, i.e. the one that yields better performance for face recognition does not guarantee the same performance for lane detection. A lot of time is invested in identifying algorithms that are best suited for an application. In the interest of time, an attempt has been made to develop few good algorithm combinations that assist in the selection of algorithms. The features of input image and the target image are extracted, described and matched using various algorithm combinations like SURF, FAST, MSER, and Harris Corner Detector. The combination of all these algorithms is simulated on MATLAB using computer vision and image processing toolboxes. A Graphical User Interface (GUI) is developed for better user experience. Face recognition is considered as an example to perform the simulation. The results reflect that the combination of SURF and MSER performs better compared to other algorithm combinations when an image is scaled and rotated, however there are no good matches when there is also a pose variation. Proper thresholding of ‘Match Threshold’, ‘Reject Ratio’, and ‘Inlier Threshold’ must be carried out through trial and error method to get better results.