A Scale and Rotational Invariant Key-point Detector based on Sparse Coding

TL;DR

This paper introduces SRI-SCK, a novel key-point detector that combines scale and rotation invariance with sparse coding, improving robustness against geometric transformations and illumination variations in images.

Contribution

The paper presents a new scale and rotation invariant key-point detector based on sparse coding, integrating image pyramids and rotated dictionaries for enhanced robustness.

Findings

Achieves high repeatability on public datasets.

Demonstrates superior matching scores compared to existing detectors.

Effective in handling scale, rotation, and illumination variations.

Abstract

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.