This papers presents a novel quantised transform (the Sinclair-Town or ST transform for short) that subsumes the rolls of both edge-detector, MSER style region detector and corner detector. The transform is similar to the $unsharp$ transform but the difference from the local mean is quantised to 3 values (dark-neutral-light). The transform naturally leads to the definition of an appropriate local scale. A range of methods for extracting shape features form the transformed image are presented. The generalized feature provides a robust basis for establishing correspondence between images. The transform readily admits more complicated kernel behaviour including multi-scale and asymmetric elements to prefer shorter scale or oriented local features.

翻译：本文展示了一种新型的量化变换(辛克莱-唐或ST变换短),将边缘检测器、MSER风格区域探测器和角探测器的卷子相混合。变换与美元变换类似,但与当地平均值的差数被量化为3值(暗中光),变换自然导致对适当的本地尺度的定义。提出了一系列提取形状特征的方法,形成变换图像。通用特征为图像之间建立对接提供了坚实的基础。变换很容易接受更复杂的内核行为,包括多尺度和不对称元素,以更倾向于更短尺度或更定向的本地特征。