Local intensity discontinuities, commonly referred to as edges, are important attributes of an image. Many imaging scenarios produce image regions exhibiting complex two-dimensional (2D) local structure, such as when several edges meet to form corners and vertices. Traditional derivative-based edge operators, which typically assume that an edge can be modeled as a one-dimensional (1D) piecewise smooth step function, give misleading results in such situations. Leclerc and Zucker introduced the concept of local structure as an aid for locating intensity discontinuities. They proposed a detailed procedure for detecting discontinuities in a 1D function. They had only given a preliminary version of their scheme, however, for 2D images. Three related edge-detection methods are proposed that draw upon 2D local structural information. The first method greatly expands upon Leclerc and Zucker's 2D method. The other two methods employ a mechanism similar to that used by the maximum-homogeneity filter (a filter used for image enhancement). All three methods permit the detection of multiple edges at a point and have the flexibility to detect edges at differing spatial and angular acuity. Results show that the methods typically perform better than other operators.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence