  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 paper, 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.
