Constructing long edge segments for object recognition
Bryan D. Mielke, Neelima Shrikhande · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Most computer vision algorithms need a good edge description of the scene. The edges are used for high level object recognition. The image of the object is preprocessed to extract edges. Often the extracted edges are short and noisy. This paper describes an algorithm that groups edgelets together to aid in higher level vision object recognition. The input consists of two dimensional and three dimensional range data points from the image of the object. This data is used in testing the edges for grouping properties. These properties include parallelism, collinearity, proximity, and segment length. Short edges are combined into longer line segments using the above criteria. Results are reported for synthetic and real data.