Multi-scale edge detection on range and intensity images

Multi-scale feature extraction has become prominent in recent years. Additionally, processing images containing sparse or irregularly distributed data has become increasingly important, in particular with respect to the use of range image data. We present a family of multi-scale gradient-based edge detection algorithms that are suitable for use on either regularly or irregularly distributed image data; these algorithms can be applied directly to the range and intensity images without any image pre-processing. We quantitatively evaluate our algorithms on synthetic intensity and range images and also provide comparative visual output, using real images. The results demonstrate that this approach can be successfully applied to both range and intensity images, providing results that for intensity images are more accurate than from traditional gradient operators and for range images are more accurate than from the scan-line approximation.

[1]  I.E. Abdou,et al.  Quantitative design and evaluation of enhancement/thresholding edge detectors , 1979, Proceedings of the IEEE.

[2]  Moncef Gabbouj,et al.  Multi-Scale Edge Detection and Object Extraction for Image Retrieval , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  Martin Rumpf,et al.  A finite element method for surface restoration with smooth boundary conditions , 2004, Comput. Aided Geom. Des..

[4]  Leslie George Tham,et al.  Finite element modeling of geomaterials using digital image processing , 2003 .

[5]  Roland Siegwart,et al.  Results on Range Image Segmentation for Service Robots , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[6]  J. Tinsley Oden,et al.  Finite elements: An introduction , 1991 .

[7]  Raimondo Schettini,et al.  Face^3 a 2D+3D Robust Face Recognition System , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[8]  Dieter Fritsch,et al.  EXTRACTION AND REGISTRATION OF REAL WORLD SCENES , 2006 .

[9]  Harry E. Martz,et al.  Finite element analysis of human joints: image processing and meshing issues , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[10]  Carlos Vázquez,et al.  Reconstruction of irregularly-sampled images by regularization in spline spaces , 2002, Proceedings. International Conference on Image Processing.

[11]  Bryan W. Scotney,et al.  Scalable Operators for Feature Extraction on 3-D Data , 2008, EUROS.

[12]  Ellen C. Hildreth,et al.  Edge Detection , 1985, Encyclopedia of Database Systems.

[13]  Michael G. Strintzis,et al.  Integration of 2D and 3D images for enhanced face authentication , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[14]  Paulo Dias,et al.  Combining intensity and range images for 3D modelling , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[15]  Rachid Benlamri,et al.  Free-form object segmentation and representation from registered range and color images , 2004, Image Vis. Comput..

[16]  Horst Bunke,et al.  Edge Detection in Range Images Based on Scan Line Approximation , 1999, Comput. Vis. Image Underst..

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

[18]  Bahram Parvin,et al.  Adaptive multiscale feature extraction from range data , 1989, Comput. Vis. Graph. Image Process..

[19]  Bryan W. Scotney,et al.  Content-adaptive feature extraction using image variance , 2005, Pattern Recognit..

[20]  L. J. Kitchen,et al.  The effect of spatial discretization on the magnitude and direction response of simple differential edge operators on a step edge , 1987, Comput. Vis. Graph. Image Process..

[21]  Markus H. Gross,et al.  Simulating facial surgery using finite element models , 1996, SIGGRAPH.

[22]  Ben Taskar,et al.  Discriminative learning of Markov random fields for segmentation of 3D scan data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Dan Wang,et al.  Range image segmentation based on randomized Hough transform , 2005, Pattern Recognit. Lett..

[24]  Anil K. Jain,et al.  Reconstruction and Boundary Detection of Range and Intensity Images Using Multiscale MRF Representations , 1996, Comput. Vis. Image Underst..

[25]  Edward Roy Davies.  Design of optimal gaussian operators in small neighbourhoods , 1987, Image Vis. Comput..

[26]  Carlos Vázquez,et al.  Wavelet-based reconstruction of irregularly-sampled images: application to stereo imaging , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[27]  Benjamin B. Kimia,et al.  2D-3D registration based on shape matching , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[28]  Jose L. Marroquin,et al.  El método de los elementos finitos en el registro de imágenes de resonancia magnética , 2003 .

[29]  Hongbin Zha,et al.  Coarse-to-fine vision-based localization by indexing scale-Invariant features , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Arun K. Sood,et al.  Range image segmentation combining edge-detection and region-growing techniques with applications sto robot bin-picking using vacuum gripper , 1990, IEEE Trans. Syst. Man Cybern..

[31]  Yukio Tada,et al.  Analysis of trabeculae in femoral head by image processing and estimation of bone strength , 1995 .

[32]  Markus Gross,et al.  Simulating facial surgery using finite element models , 1996 .

[33]  Bryan W. Scotney,et al.  Improving angular error via systematically designed near-circular Gaussian-based feature extraction operators , 2007, Pattern Recognit..