Reticular shield detection algorithm using superpixel segmentation

In this paper, we propose a novel reticular shield detection algorithm to restore the images that are obscured by reticular shield such as barbed wires and fences. The whole framework is composed of three stages: stage-1 for accurate superpixel segmentation, stage-2 for deriving the mask of reticular shield and stage-3 for restoring the image by introducing the algorithm. In this paper, to obtain the accurate mask of the shield, a new joint feature CCTP is used to train support vector machine (SVM) classifier and classify all superpixels. Finally, we make comparison of the effects of the proposed algorithm and several other algorithms put forward by other scientists. We introduce two indexes, PSNR and SSIM to evaluate the results and the proposed algorithm is obviously superior to the other two algorithms, which can also be proved by looking into the restoration images.

[1]  Yanxi Liu,et al.  Image de-fencing , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Guangming Shi,et al.  Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach , 2013, IEEE Transactions on Image Processing.

[3]  William T. Freeman,et al.  A computational approach for obstruction-free photography , 2015, ACM Trans. Graph..

[4]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Atsushi Yamashita,et al.  Fence Removal from Multi-focus Images , 2010, 2010 20th International Conference on Pattern Recognition.

[6]  Yanxi Liu,et al.  Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Arif Mahmood,et al.  Image inpainting based on pyramids , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[8]  A. Ms.PatilV..  Region Filling and Object Removal by Exemplar-Based Image Inpainting , 2012 .

[9]  Rajiv R. Sahay,et al.  My camera can see through fences: A deep learning approach for image de-fencing , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[10]  Matti Pietikäinen,et al.  Rotation-invariant texture classification using feature distributions , 2000, Pattern Recognit..

[11]  Mohan S. Kankanhalli,et al.  Seeing through the fence: Image de-fencing using a video sequence , 2013, 2013 IEEE International Conference on Image Processing.

[12]  Zhang Kuang,et al.  Text segmentation based on superpixel fusion , 2016 .

[13]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Arif Mahmood,et al.  Image de-fencing framework with hybrid inpainting algorithm , 2016, Signal Image Video Process..

[15]  Qingquan Li,et al.  Automatic inpainting by removing fence-like structures in RGBD images , 2014, Machine Vision and Applications.

[16]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  George Loizou,et al.  Computer vision and pattern recognition , 2007, Int. J. Comput. Math..

[18]  Mohan S. Kankanhalli,et al.  A multimodal approach for image de-fencing and depth inpainting , 2015, 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR).

[19]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[21]  Yao Li,et al.  Characterness: An Indicator of Text in the Wild , 2013, IEEE Transactions on Image Processing.

[22]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[23]  VekslerOlga,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001 .