1. Field of the Invention
The present invention relates in general to a method and system for detecting edges in digital images in which fuzzy reasoning is employed to determine the degree to which each pixel in an image represents an edge.
2. Description of the Background Art
Accurate detection in images of edges, which contain the most important information, is vital to performing advanced image processing and analysis. Unfortunately, images of real scenes frequently contain data that is ambiguous and incomplete. As a result, the problem of determining what is and what is not an edge is confounded by the fact that edges are very often partially hidden or distorted by various effects such as uneven lighting and image acquisition noise. Furthermore, images frequently contain data with edge-like characteristics, but a confident classification of this data can be best solved when high-level constraints are imposed on the interpretation of an image.
Most known edge detector techniques require the selection of parameters (e.g. thresholds in gradient edge detectors, thresholds in Laplacian edge detectors, and s in Laplacian of Gaussian edge detectors) when no information about the images is known in advance. Edge detection based on mathematical models can only detect specific kinds of noticeable edges. For example, an optimal mathematical-model-based step edge detector can be ineffective for ramp edges. Moreover, the parameters in some of the mathematical models are difficult to determine when little information about the image is known.
Human beings, on the other hand, are able to make some sense of even unfamiliar objects, which necessarily have an imperfect high-level representation. To perceive unfamiliar objects, or to perceive familiar objects with imperfect images, it appears that humans apply heuristic algorithms to understand such images. Although these algorithms may be “implemented” in the wetware of the human vision system, it is feasible to believe that it is possible to characterize an equivalent process systematically. One would therefore suspect that a system that employs human like heuristic algorithms would be particularly suited for image edge detection considering the indeterminate nature of edge detection data. Such a system may well be found to out perform other, mathematical based edge detection techniques.