This paper proposes a unique filter, called "iris filter", which evaluates the degree of convergence of gradient vectors in the neighborhood of the pixel of interest. The generalized iris filter and its simplified one are given. The degree of convergence is related to the distribution of orientations of gradient vectors. The region of support of the iris filter is controlled so that the degree of convergence of gradient vectors in it becomes maximum. It means that the size and the shape of the region of support changes adaptively according to the distribution pattern of gradient vectors around the pixel of interest. Theoretical analysis using models of a rounded convex region and a semi-cylindrical region is given. It shows that rounded convex regions are mostly enhanced even if their original contrasts to their background are weak and elongated objects are suppressed. However, the filter output is 1//spl pi/ at the boundaries of rounded convex regions and semi-cylindrical ones in spite of their contrast. This absolute value can be used to detect boundaries of those objects. The proposed filter is effective to enhance and detect rounded convex regions with various sizes and contrasts.