Image and Video Technology – PSIVT 2013 Workshops

The skeletal stroke method is a general brush tool which can take a straight vector artwork as “ink”. It is easy to apply, but it is limited by the requirement of straight inputs. To offer additional input options, we present inverse skeletal strokes, a method for straightening warped vector artworks. Our method takes a user stroke to help understanding the structure of an input artwork. The key-idea is finding a set of arcs which show the “directional trend” of the artwork, and map the artwork into a new version in which these arcs are straightened. We propose a measure representing the degree of parallelism between two arcs. Using this measure, we select a set of arcs from the input artwork which are approximately parallel to the given user stroke. This is a condensed representation of a user’s intention. Then we transform the user stroke with the goal to maximize the degree of parallelism to each of the selected approximately parallel arcs. At last, we parametrize the artwork with respect to the optimized stroke, and map it into a straight version.

[1]  Mei Han,et al.  Efficient hierarchical graph-based video segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  José de Jesús Medel Juárez,et al.  Artificial vision and identification for intelligent orientation using a compass , 2011 .

[3]  Ehud Rivlin,et al.  Color Invariants for Person Reidentification , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  M. Bikdash,et al.  Automated electric utility pole detection from aerial images , 2009, IEEE Southeastcon 2009.

[5]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[6]  Harry Shum,et al.  Video tooning , 2004, ACM Trans. Graph..

[7]  Chikahito Nakajima,et al.  Automatic recognition of facility drawings and street maps utilizing the facility management database , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[8]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[9]  José de Jesús Medel Juárez,et al.  Automatic Object Recognition Based on Dimensional Relationships , 2011 .

[10]  S. Halawani,et al.  Interaction between Sunlight and the Sky Colour with 3D Objects in the Outdoor Virtual Environment , 2010, 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation.

[11]  P. Peebles.  Probability, Random Variables and Random Signal Principles , 1993 .

[12]  Joan Batlle,et al.  A review on strategies for recognizing natural objects in colour images of outdoor scenes , 2000, Image Vis. Comput..

[13]  H. Yokoyama,et al.  POLE-LIKE OBJECTS RECOGNITION FROM MOBILE LASER SCANNING DATA USING SMOOTHING AND PRINCIPAL COMPONENT ANALYSIS , 2012 .

[14]  Daniel Berwick,et al.  A chromaticity space for specularity, illumination color- and illumination pose-invariant 3-D object recognition , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..