In this paper, an algorithm for improved image matching 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. Keypoint detection testing on the enhanced images is conducted, as well as image matching using SIFT and SURF. 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 and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or comparable matching accuracy over other related enhancement algorithms using SIFT and SURF.

Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this article, we propose a solution to this challenging problem by incorporating Scale and Rotation Invariant design (named SRI-SCK) into a recently developed Sparse Coding based Key-point detector (SCK). The SCK detector is flexible in different scenarios and fully invariant to affine intensity change, yet it is not designed to handle images with drastic scale and rotation changes. In SRI-SCK, the scale invariance is implemented with an image pyramid technique, while the rotation invariance is realized by combining multiple rotated versions of the dictionary used in the sparse coding step of SCK. Techniques for calculation of key-points’ characteristic scales and their sub-pixel accuracy positions are also proposed. Experimental results on three public datasets demonstrate that significantly high repeatability and matching score are achieved.

Traditional detectors e.g. Harris, SIFT, SFOP... are known inflexible in different contexts as they solely target corners, blobs, junctions or other specific human-designed structures. To account for this inflexibility and additionally their unreliability under non-uniform lighting change, recently, a Sparse Coding based Key-point detector (SCK) relying on no human-designed structures and invariant to non-uniform illumination change was proposed. Yet, geometric transformations such as rotation are not considered in SCK. Thus, a novel Rotational Invariant SCK called RI-SCK is proposed in this paper. To make SCK rotational invariant, an effective use of multiple rotated versions of the original dictionary in the sparse coding step of SCK is proposed. A novel strength measure is also introduced for comparison of key-points across image pyramid levels if scale invariance is required. Experimental results on three public datasets have confirmed that significant gains in repeatability and matching score could be achieved by the proposed detector.

Most popular hand-crafted key-point detectors such as Harris corner, MSER, SIFT, SURF rely on some specific pre-designed structures for detection of corners, blobs, or junctions in an image. The very nature of pre-designed structures can be considered a source of inflexibility for these detectors in different contexts. Additionally, the performance of these detectors is also highly affected by non-uniform change in illumination. To the best of our knowledge, while there are some previous works addressing one of the two aforementioned problems, there currently lacks an efficient method to solve both simultaneously. In this paper, we propose a novel Sparse Coding based Key-point detector (SCK) which is fully invariant to affine intensity change and independent of any particular structure. The proposed detector locates a key-point in an image, based on a complexity measure calculated from the block surrounding its position. A strength measure is also proposed for comparing and selecting the detected key-points when the maximum number of key-points is limited. In this paper, the desirable characteristics of the proposed detector are theoretically confirmed. Experimental results on three public datasets also show that the proposed detector achieves significantly high performance in terms of repeatability and matching score.

All current popular hand-crafted key-point detectors such as Harris corner, MSER, SIFT, SURF ... rely on some specific pre-designed structures for the detection of corners, blobs, junctions ... in an image. In this paper, a novel sparse coding based key-point detector which requires no particular predesigned structures is presented. The key-point detector is based on measuring the complexity level of each block in an image to decide where a key-point should be. The complexity level of a block is defined as the total number of non-zero components of a sparse representation of that block. Generally, a block constructed with more components is more complex and has greater potential to be a good key-point. Experimental results on Webcam and EF datasets [1], [2] show that the proposed detector achieves significantly high repeatability compared to hand-crafted features, and even outperforms the matching scores of the state-of-the-art learning based detector.