Implementation and Analysis of the SUSAN Operator in Image Processing

In image processing, the SUSAN (Smallest Univalue Segment Assimilating Nucleus) operator is a widely used method for detecting edges and corners in digital images. The operator functions by analyzing circular mask regions around each pixel and comparing intensity values with the nucleus pixel, making it particularly effective for noise-resistant feature detection. MATLAB implementations of the SUSAN operator typically involve creating a circular mask template and computing the similarity area through intensity threshold comparisons. A common approach includes: 1. Defining a 37-pixel circular mask using coordinates relative to the nucleus 2. Implementing the core similarity function: USAN(r) = Σ[exp(-((I(r)-I(r0))/t)^6)] 3. Applying geometric thresholding for corner/edge classification 4. Using non-maximal suppression for refined feature detection The implementation is validated using test images that demonstrate the operator's capability in handling various scenarios, including noise interference and complex textures. Through MATLAB experimentation with provided test images, researchers can gain deeper insights into the SUSAN operator's working mechanism, parameter tuning effects, and practical applications in computer vision systems. This hands-on approach facilitates better understanding of both the theoretical foundations and implementation nuances of modern image processing techniques.