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.

中文翻译：

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一种基于稀疏编码的尺度旋转不变关键点检测器

大多数流行的手工关键点检测器，如 Harris 角点、SIFT、SURF 旨在检测图像中的角点、斑点、连接点或其他人为定义的结构。尽管在某些几何变换方面很稳健，但意外的场景或不均匀的光照变化可能会显着降低它们的性能。因此，非常需要一种新的检测器，该检测器在上下文变化时具有灵活性，同时对几何和非均匀照明变化都具有鲁棒性。在本文中，我们通过将比例和旋转不变设计（命名为 SRI-SCK）结合到最近开发的基于稀疏编码的关键点检测器（SCK）中，提出了一个解决这个具有挑战性的问题的方法。SCK检测器在不同的场景中是灵活的，并且对仿射强度变化完全不变，然而，它并不是为处理具有剧烈比例和旋转变化的图像而设计的。在 SRI-SCK 中，尺度不变性是通过图像金字塔技术实现的，而旋转不变性是通过组合 SCK 稀疏编码步骤中使用的字典的多个旋转版本来实现的。还提出了关键点特征尺度及其亚像素精度位置的计算技术。三个公共数据集的实验结果表明，实现了显着的高可重复性和匹配分数。还提出了关键点特征尺度及其亚像素精度位置的计算技术。三个公共数据集的实验结果表明，实现了显着的高可重复性和匹配分数。还提出了关键点特征尺度及其亚像素精度位置的计算技术。三个公共数据集的实验结果表明，实现了显着的高可重复性和匹配分数。