Image Enhancement for the Improved Extraction of Local Image Features Using Image Offsets

In this thesis, an algorithm for improved feature extraction is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Comparative keypoint detection testing on the enhanced images is conducted to test if more keypoints can be extracted using the method. Keypoint strength experiments are also conducted as well as quality assessment experiments to test the validity of the proposed algorithm as an image enhancement method. Image matching experiments though SIFT and SURF are also conducted. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam, Heinly, and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or similar matching accuracy compared with related enhancement algorithms using SIFT and SURF.