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I'm working on my first computer vision project where I take a picture of a chart and detect the symbols on it. There are 5 distinct shapes that need to be detected.
This is the result of my edge detection algorithm: http://i.imgur.com/mh9Ol.jpg. The 5 symbols are a single dot, 4 dots grouped together, a rectangle, and 2 oddly shaped symbols.
At this point I'm not sure which algorithms I should look at that will allow me to pick out these shapes and discern them from each other. Any ideas would be very helpful. Thanks!
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Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision.
Canny Operator; Canny edge detection algorithm (Canny, 1986) known as optimal edge detection algorithm and the most commonly used edge detection algorithm in practice.
There are three types of edges: Horizontal edges. Vertical edges. Diagonal edges.
The goals of the Canny Operator were stated explicitly. Good Detection: the ability to locate and mark all real edges. Good Localisation: minimal distance between the detected edge and real edge. Clear Response: only one response per edge.
It seems like Shape Context would be a natural choice for this type of problem.
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