Our chief goals in this project were to produce a color edge detector, compare the results with existing edge detection tools, and evaluate methods of parallelization and their possible benefits. Our edge detection algorithm used a similar process as the Canny edge detector, but changed some of the calculations to include color information. This algorithm is described in the Theory and Implementation sections above. Our version of color edge detection is called imgCED, and our Program Flow section describes how this tool should be used. We then tested our algorithm on several images and compared results against the imgCanny tool. These results can be found in the Color Edge Detection Examples and Color Edge Detection Results sections. We also explored methods of parallelization, descibed in the Analysis and Parallelization Study section above.
Although our color edge detector does not result in a great improvement over the Canny edge detector, some subtle differences produced using imgCED can provide useful information about certain images that imgCanny misses. Significant improvement occurs in areas of fine detail and shadow, like human faces. In two different images, facial features were better defined using our color edge detector. Other images that contained large amounts of shadow also illustrated the usefulness of color edge detection. Although the differences may not seem significant, the color edge detection using our algorithm may be viewed as a mild upgrade of the classical edge detection process.