Improved tensor voting for missing edge inference

Edges and structures are a critical part for natural images, which can help to improve the overall quality of the reconstructed image. In tasks like object removal, image inpainting or video error concealment, many related methods try to infer and recover the edge splines in the unknown regions using known splines in the neighbours as a pre-possessing procedure. In this paper, the tensor voting method is improved to pursue better inference quality of the missing edge. In the proposed method, the additional spline pairing is used to determine the most possible connectable edge spline pairs existed in the known region, which greatly overcomes the difficulty of choosing the accurate parameter for stick voting. Experiments show that the proposed method can improve the inference performance dramatically with the help of extra pairing work and acquire a better structure inference quality compared to several other inference methods.

[1]  Alan C. Bovik,et al.  Objective quality assessment of multiply distorted images , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[2]  Mircea Nicolescu,et al.  Perceptual Grouping Based on Iterative Multi-scale Tensor Voting , 2006, ISVC.

[3]  Øyvind Hjelle,et al.  Constrained Delaunay Triangulation , 2006 .

[4]  Ales Procházka,et al.  Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation , 2016, IEEE Transactions on Image Processing.

[5]  Jitendra Malik,et al.  Learning Probabilistic Models for Contour Completion in Natural Images , 2008, International Journal of Computer Vision.

[6]  P. Kellman,et al.  A theory of visual interpolation in object perception , 1991, Cognitive Psychology.

[7]  Emmanuel Maggiori,et al.  Comments on “A Closed-Form Solution to Tensor Voting: Theory and Applications” , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Gérard G. Medioni,et al.  A Closed-Form Solution to Tensor Voting: Theory and Applications , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Gérard G. Medioni,et al.  Probabilistic tensor voting for robust perceptual grouping , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  Hanyun Wang,et al.  Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[12]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[13]  Heiko Neumann,et al.  Iterated tensor voting and curvature improvement , 2007, Signal Process..

[14]  Bahram Parvin,et al.  Iterative Tensor Voting for Perceptual Grouping of Ill-Defined Curvilinear Structures , 2011, IEEE Transactions on Medical Imaging.

[15]  Hongdong Li,et al.  Contour Completion Without Region Segmentation , 2016, IEEE Transactions on Image Processing.

[16]  T. S. Lee,et al.  Gestalten of Today: Early Processing of Visual Contours and Surfaces , 1996 .

[17]  Andrew C. Gallagher,et al.  Inference for order reduction in Markov random fields , 2011, CVPR 2011.

[18]  Mi-Suen Lee,et al.  A Computational Framework for Segmentation and Grouping , 2000 .

[19]  Gérard G. Medioni,et al.  Unsupervised Learning Using the Tensor Voting Graph , 2015, SSVM.

[20]  Yang Xu,et al.  Priority Belief Propagation-Based Inpainting Prediction With Tensor Voting Projected Structure in Video Compression , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Gerard Medioni,et al.  Inference of surfaces, 3-D curves, and junctions from sparse 3-D data , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[22]  Gabriele Lombardi,et al.  The Neighbors Voting Algorithm and Its Applications , 2009, Applications of Supervised and Unsupervised Ensemble Methods.

[23]  John Stonham,et al.  Image loss concealment using edge-guided interpolation and multi-scale transformation , 2015, 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE).

[24]  Mircea Nicolescu,et al.  An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds , 2009, Comput. Vis. Image Underst..

[25]  Gabriele Lombardi,et al.  Curvature Estimation and Curve Inference with Tensor Voting: A New Approach , 2008, ACIVS.

[26]  Heiko Neumann,et al.  Are Iterations and Curvature Useful for Tensor Voting? , 2004, ECCV.

[27]  Gérard G. Medioni,et al.  First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Wanda Benesova,et al.  Texture aware image error concealment with fuzzy segmentation , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).