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Edge detection is an important task in Computer Vision for extracting meaningful information from digital images. The main goal of the authors' proposed technique is to obtain thin edges, so that the result is more suitable for further application such as boundary detection, image segmentation, motion detection/estimation, texture analysis, object identification and so on. They tested four edge detectors that use different methods for detecting edges and compared their results under a variety of situations to determine which detector was preferable under different sets of conditions. This data could then be used to create a multi-edge-detector system, which analyzes the scene and runs the edge detector best suited for the current set of data.
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