As we all know

Shape description is a central problem in pattern recognition. However

most simple algorithms for generating 2D shape descriptors

whether they are based on boundary features or regional features

can only be used in certain applications or have deficiencies in some performances. To address such problem

based on the coordinate relationship between the current boundary pixel and its two lockstep boundary pixels

a general-purpose method ( so called region histogram ) for region shape representing and describing is presented in this paper. Region histogram is the sequenced horizontal intervals along the region boundary in accordance with some classification and calculation criterion

which actually represent the region's area and its boundary length in terms of pixels. In order to quantify the recognition ability of the region histogram method and compare it with the analogous ones

a series of tests were carried out using a set of standard shapes

which have been proposed by Rosen and Gleason

comprising of a disc

a square

a rectangle and an equilateral. The results show that region histogram method is robust and effective to region shape representing and describing; even it does better with complex regions which have much more global and local contour information. Therefore

this method presents a new effective way for shape representing and describing

and can be used in pattern recognition such as robot vision applications.