Vectorisation of Sketches with Shadows and Shading using COSFIRE filters

Engineering design makes use of freehand sketches to communicate ideas, allowing designers to externalise form concepts quickly and naturally. Such sketches serve as working documents which demonstrate the evolution of the design process. For the product design to progress, however, these sketches are often redrawn using computer-aided design tools to obtain virtual, interactive prototypes of the design. Although there are commercial software packages which extract the required information from freehand sketches, such packages typically do not handle the complexity of the sketched drawings, particularly when considering the visual cues that are introduced to the sketch to aid the human observer to interpret the sketch. In this paper, we tackle one such complexity, namely the use of shading and shadows which help portray spatial and depth information in the sketch. For this reason, we propose a vectorisation algorithm, based on trainable COSFIRE filters for the detection of junction points and subsequent tracing of line paths to create a topology graph as a representation of the sketched object form. The vectorisation algorithm is evaluated on 17 sketches containing different shading patterns and drawn by different sketchers specifically for this work. Using these sketches, we show that the vectorisation algorithm can handle drawings with straight or curved contours containing shadow cues, reducing the salient point error in the junction point location by 91% of that obtained by the off-the-shelf Harris-Stephen's corner detector while the overall vectorial representations of the sketch achieved an average F-score of 0.92 in comparison to the ground truth. The results demonstrate the effectiveness of the proposed approach.

[1]  Karl Tombre,et al.  Robust and accurate vectorization of line drawings , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  A. A. El-Harby,et al.  Automatic line extraction by the square scan algorithm , 1999 .

[3]  Albert M. Vossepoel,et al.  Adaptive Vectorization of Line Drawing Images , 1997, Comput. Vis. Image Underst..

[4]  Gladys Monagan,et al.  Appropriate base representation using a run graph , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[5]  Salvatore Tabbone,et al.  Stable and Robust Vectorization: How to Make the Right Choices , 1999, GREC.

[6]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[7]  Jean-Yves Ramel,et al.  A Coarse Vectorization as an Initial Representation for the Understanding of Line Drawing Images , 1997, GREC.

[8]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  R. Weisberg A-N-D , 2011 .

[10]  Stephen M. Pizer,et al.  Untangling the Blum Medial Axis Transform , 2003, International Journal of Computer Vision.

[11]  Dov Dori,et al.  Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Thomas M. Breuel,et al.  Optimal Line and Arc Detection on Run-Length Representations , 2005, GREC.

[13]  Nicolai Petkov,et al.  Contour detection based on nonclassical receptive field inhibition , 2003, IEEE Trans. Image Process..

[14]  Dov Dori,et al.  A protocol for performance evaluation of line detection algorithms , 1997, Machine Vision and Applications.

[15]  Shijie Cai,et al.  An Object-Oriented Progressive-Simplification-Based Vectorization System for Engineering Drawings: Model, Algorithm, and Performance , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  George Azzopardi,et al.  Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Sudhir P. Mudur,et al.  Mathematical Elements for Computer Graphics , 1985, Advances in Computer Graphics.

[18]  Clark F. Olson,et al.  Constrained Hough Transforms for Curve Detection , 1999, Comput. Vis. Image Underst..

[19]  Vincenzo Eramo,et al.  An interpretation system for land register maps , 1992, Computer.

[20]  Donald D. Hoffman,et al.  Visual intelligence: How we create what we see , 1998 .

[21]  Nicolai Petkov,et al.  Delineation of line patterns in images using B-COSFIRE filters , 2017, 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI).

[22]  Adrien Bousseau,et al.  Fidelity vs. simplicity , 2016, ACM Trans. Graph..

[23]  Jean-Yves Ramel,et al.  Accurate junction detection and characterization in line-drawing images , 2014, Pattern Recognit..

[24]  Federico Thomas,et al.  Overcoming Superstrictness in Line Drawing Interpretation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  P. A. C. Varley,et al.  Making the most of using depth reasoning to label line drawings of engineering objects , 2004, SM '04.

[26]  M. Nidelea,et al.  Method of the Square — A new algorithm for image vectorization , 2012, 2012 9th International Conference on Communications (COMM).

[27]  Qunsheng Peng,et al.  Vectorization of line drawing image based on junction analysis , 2014, Science China Information Sciences.

[28]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  David F. Rogers,et al.  Mathematical elements for computer graphics , 1976 .

[30]  Pedro M. Q. Aguiar,et al.  Connectivity-Enforcing Hough Transform for the Robust Extraction of Line Segments , 2011, IEEE Transactions on Image Processing.

[31]  Mario Costa Sousa,et al.  Sketch-based modeling: A survey , 2009, Comput. Graph..

[32]  Martin Cooper A Rich Labeling Scheme for Curved Objects , 2008 .

[33]  Vaughan R. Pratt,et al.  Direct least-squares fitting of algebraic surfaces , 1987, SIGGRAPH.

[34]  Mikhail Bessmeltsev,et al.  Vectorization of Line Drawings via Polyvector Fields , 2018, ACM Trans. Graph..

[35]  Markus H. Gross,et al.  Topology-driven vectorization of clean line drawings , 2013, ACM Trans. Graph..

[36]  Tong Lu,et al.  A Novel Knowledge-Based System for Interpreting Complex Engineering Drawings: Theory, Representation, and Implementation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Michael R. Lyu,et al.  A Hough transform based line recognition method utilizing both parameter space and image space , 2005, Pattern Recognit..

[38]  John Y. Chiang,et al.  A new approach for binary line image vectorization , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[39]  Koos Eissen,et al.  Sketching: Drawing Techniques for Product Designers , 2009 .