Grayscale Feature Point-Based Acquisition Method

The SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm is an image edge extraction method based on grayscale feature point acquisition. It operates by analyzing circular mask regions around each pixel and comparing their intensity values with the nucleus (center pixel). Pixels within the mask that have similar intensity to the nucleus form the "USAN" area, with edge points characterized by small USAN areas. The algorithm effectively determines edge locations through threshold-based analysis of these feature points, providing robust edge detection even in noisy conditions. Widely applied in image processing, the SUSAN algorithm excels at capturing object boundaries and delivering precise edge detection results. Its implementation typically involves configuring parameters like the brightness threshold (t) and geometric threshold (g) to balance sensitivity and noise resistance. Through SUSAN, we gain better understanding of image structures and shapes, thereby supporting subsequent image analysis and processing tasks. This makes the algorithm particularly valuable for computer vision applications, including object recognition and 3D reconstruction. Key implementation steps include: 1. Applying circular masks to each pixel 2. Calculating USAN area size using intensity comparisons 3. Applying thresholds to identify edge/corner points 4. Optional non-maximum suppression for refined edges The algorithm's efficiency comes from its simple arithmetic operations without gradient calculations, making it computationally favorable for real-time systems.