Thin Nets and Crest Lines: Application to Satellite Data and Medical Images

In this paper, we describe a new approach for extractingthin netsin gray-level images. The key point of our approach is to model thin nets as crest lines of the image surface. Crest lines are lines where the magnitude maximum curvature is a local maximum in the corresponding principal direction. We define these lines using first, second, and third derivatives of the image. The image derivatives are computed using recursive filters approximating the Gaussian filter and its derivatives. Using an adaptive scale factor, we apply this approach to the extraction of roads in satellite data, blood vessels in medical images, and actual crest lines in depth maps of human faces.

[1]  J. Canny.  Finding Edges and Lines in Images , 1983 .

[2]  Rachid Deriche,et al.  On corner and vertex detection , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  C. Lanczos.  Applied Analysis , 1961 .

[4]  J. Thirion,et al.  The 3D marching lines algorithm and its application to crest lines extraction , 1992 .

[5]  Jean-Marc Braemer,et al.  Géométrie des courbes et des surfaces , 1976 .

[6]  R. Haralick.  CUBIC FACET MODEL EDGE DETECTOR AND RIDGE-VALLEY DETECTOR: IMPLEMENTATION DETAILS , 1986 .

[7]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Julian E. Boggess,et al.  Identification of Roads in Satellite Imagery Using Artificial Neural Networks: A Contextual Approach , 1993 .

[9]  Olivier Monga,et al.  Using differential geometry in R 4 to extract typical surface features , 1993, CVPR 1993.

[10]  J. Alison Noble,et al.  Finding Corners , 1988, Alvey Vision Conference.

[11]  Manfredo P. do Carmo,et al.  Differential geometry of curves and surfaces , 1976 .

[12]  Olivier Monga,et al.  Using differential geometry in R/sup 4/ to extract typical features in 3D images , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Andrew P. Witkin,et al.  Analyzing Oriented Patterns , 1985, IJCAI.

[14]  Rachid Deriche,et al.  Extraction of the zero-crossings of the curvature derivatives in volumic 3D medical images: a multi-scale approach , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[15]  R. Deriche.  Recursively Implementing the Gaussian and its Derivatives , 1993 .

[16]  Daniel Q. Naiman.  Pattern Recognition in Practice II , 1988 .

[17]  R. Haralick.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Martin A. Fischler,et al.  Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique☆ , 1981 .

[19]  Alexis Gourdon,et al.  The Marching lines algorithm : new results and proofs , 1993 .

[20]  Rachid Deriche,et al.  Crest lines extraction in volume 3D medical images: a multi-scale approach , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[21]  O. Monga,et al.  Using partial Derivatives of 3D images to extract typical surface features , 1992, Proceedings of the Third Annual Conference of AI, Simulation, and Planning in High Autonomy Systems 'Integrating Perception, Planning and Action'..

[22]  Jean Ponce,et al.  Toward a surface primal sketch , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[23]  D. Geman,et al.  Detection Of Roads In Satellite Images , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.