Features - Description And Extraction For Images With Contours
Krishnan R. Tampi, Chetlur S. Sridhar · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1987
Images are divided into equal area cells. The contour encountered in these cells are given WORD names, which describe the type of contour. Each cell has 9 pixels in a 3x3 matrix format and herutthere are a possible 512 different WORDs. However, for a class of images where the outline is sufficient for recognition purposes, a much smaller number actually exists. These images include lower case English handwriting and blood cell images. It is shown in this work that there are only 34 BASIC WORDs and all the others encountered in these images are rotations of these BASIC WORDs. Using this fact,features are defined for this class of images, standardized in size to 16 cells described above. Three types of features are defined namely, Independent, Dependent and Related to aid in application of syntactic methods of recognition from sentence structures defined using the above features. It is also shown that matrix algebra can be used for developing data reduction and recognition algorithms.