Shape recognition with generalized beam angle statistics

We develop a new shape descriptor and matching algorithm in order to find a given template shape in an edge detected image without performing boundary extraction. The shape descriptor, based on generalized beam angle statistics (GBAS), defines the angles between the lines connecting each boundary point with the rest of the points as a random variable. Then, it assigns a feature vector to each point using the moments of the beam angles. The proposed matching algorithm performs shape recognition by matching the feature vectors of boundary points on the template shape and of the edge pixels on the image. The matching process also considers the spatial distance of the edge pixels. Experiments performed on an MPEG-7 data set show that the template shapes are found successfully on noisy images.