Pattern Recognition Using Neighborhood Coding
We propose a new coding algorithm for binary images based on neighborhood relations. The shape is transformed into a set of representative vectors (position invariant) by coding each pixel according to the number of neighbors in the four directions (north, east, south, west). These neighborhood vectors are transformed into a set of codes satisfying the boundary condition imposed by the size of the image in which the shape is imbedded. A label is attached to the codes to indicate a sequential order of the pixels. The combined code and label characterize an exact shape. Thus following the label ordering and performing simple comparison of the codes an exact shape match is obtained. It is interesting to note that each shape will represent a polyomino. Neighborhood image operators are developed by applying mathematical and logical operations on the code vectors. A code reduction scheme for the purpose of information reduction and generalization of the shape image is proposed. Using the digits 1 and 0 of the NIST handwritten segmented characters set, we show a preliminary application for pattern recognition.
KeywordsNeighborhood Relation Exact Shape Connected Component Analysis Shape Image Morphological Function
Unable to display preview. Download preview PDF.
- 2.Sinha, D., Giardina, C.R.: Discrete Black and White Object Recognition via Morphological Functions. IEEE Transactions on PAMI 12(3), 275–293 (1990)Google Scholar
- 5.Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. Addison-Wesley, Reading (1992)Google Scholar
- 6.Hoshen, J., Kopelman, R.: Percolation and Cluster Distribution. I. Cluster Multiple Labeling Technique and Critical Concentration Algorithm. Phys. Rev. B 14(8), 3438–3445 (1976)Google Scholar
- 8.Garris, M.D., Wilkinson, R.A.: Handwritten Segmented Characters Database, NIST special database 3, NIST Technical Report and CDROM (February 1992)Google Scholar