Affine Invariant

Affine invariance refers to a property of certain methods or features in data analysis that remain consistent under affine transformations—these include operations like scaling, translating, rotating, or skewing the data. Essentially, if a data set or an image undergoes these transformations, an affine-invariant technique can still accurately identify or analyze features without being affected. This robustness makes affine-invariant methods valuable in applications like computer vision or pattern recognition, where objects may appear in different positions, sizes, or orientations, ensuring the analysis focuses on the true structure rather than the way it’s presented.