Neighbourhood Vector as Shape Parameter for Pattern Recognition

We present a neighbourhood vector representation as shape parameter for binary images. This method is based on the pixel neighbourhood relation. Each pixel is transformed into a vector, V = (n, e, s, w), where each element of the vector represents the total number of neighbour pixels in the respective direction, north, east, south, west. A binary object is represented by a set of neighbourhood vectors (NV), in which the information of the shape structure is retained. The k-means and fuzzy c-means clustering methods are used to reduce the total amount of NV and the probability distribution of the reduced NV is used to characterize a class of image. We applied this method for handwritten character recognition, using neural network as classifiers. The results show that the shape parameter can be used as a general method of feature extraction for problems in image processing and pattern recognition. In addition, we present an application of this representation scheme for the neighbourhood image operator.