The extraction and classification of significant points along a contour is fundamental to many image processing tasks. In this paper, we present a simple process for extracting such points with several appealing properties: the operation is developed in terms of contours which are represented discretely; it is completely local and hence suitable for real time operation in vector or parallel processors; and it is tunable to extract significant points at different resolutions of orientation change along a contour. We also describe its use in linear feature extraction and processing restricted cases of environmental motion where the interest operator associates parameterized attributes with extracted image points. Matching features using these attributes allows for significant computational reductions over schemes based upon correlation matching without any loss of robustness, especially for such cases of restricted motion.
Daryl T. Lawton,
"A Simple Tunable Interest Operator and Some Applications", Proc. SPIE 0548, Applications of Artificial Intelligence II, (5 April 1985); doi: 10.1117/12.948426; https://doi.org/10.1117/12.948426