Integrating Region Growing and Edge Detection

A method that combines region growing and edge detection for image segmentation is presented. The authors start with a split-and merge algorithm wherein the parameters have been set up so that an over-segmented image results. Region boundaries are then eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree in this case). The algorithms were implemented in the C language on a Sun 3/160 workstation running under the Unix operating system. Simple tool images and aerial photographs were used to test the algorithms. The impression of human observers is that the method is very successful on the tool images and less so on the aerial photograph images. It is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise. >

[1]  Y.-T. Liow.  Integrating region growing ad edge detection for image analysis , 1990 .

[2]  Theodosios Pavlidis,et al.  Picture Segmentation by a Tree Traversal Algorithm , 1976, JACM.

[3]  David Lee,et al.  Edge detection through residual analysis , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  D.J. Anderson,et al.  Optimal Estimation of Contour Properties by Cross-Validated Regularization , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Ugo Montanari,et al.  On the optimal detection of curves in noisy pictures , 1971, CACM.

[6]  C. Reinsch.  Smoothing by spline functions , 1967 .

[7]  Ellis Horowitz,et al.  Fundamentals of Computer Algorithms , 1978 .

[8]  Theodosios Pavlidis,et al.  Structural pattern recognition , 1977 .

[9]  Allen R. Hanson,et al.  Extracting Straight Lines , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Mark F. Doherty,et al.  Split-And-Merge Segmentation Using An F Test Criterion , 1987, Photonics West - Lasers and Applications in Science and Engineering.

[11]  Patrick C. Chen,et al.  Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm☆ , 1979 .

[12]  Theo Pavlidis,et al.  Algorithms for Graphics and Imag , 1983 .

[13]  G. Wahba.  Convergence rates of "thin plate" smoothing splines wihen the data are noisy , 1979 .

[14]  Alberto Martelli,et al.  Edge detection using heuristic search methods , 1972, Comput. Graph. Image Process..

[15]  H. Feng,et al.  The generation of polygonal outlines of objects from gray level pictures , 1975 .

[16]  Pascal Fua,et al.  Using Generic Geometric Models for Intelligent Shape Extraction , 1987, AAAI.