Fast Edge Detection Using Structured Forests

1 B-404,Neelsidhi Atlantis, PIIT, Navi Mumbai, India 2 Sector 19A,Nerul, New Panvel, Navi Mumbai, India Email: bindu_k_nair@hotmail.com, ujwal.harode@gmail.com ABSTRACT An important and very crucial component of many systems involving images is the edge detection which includes object detectors and image segmentation algorithms. Edge patches always shows shapes of its inherent structure, like T junctions or lines. Taking advantage of the shapes available in the images we get to know of an edge detector which is accurate and efficient. In this approach the structured labels is robustly mapped to a space that is discrete by which evaluation of the measure of the standard information gain is possible. The outcome is a way that shows a real time performance which is many times faster than any of the traditional and modern approaches available today, which also achieves the best results for edge detection both the Berkeley Segmentation Dataset and Benchmark (BSDS 500) and the NYU depth dataset. This approach also shows its strength as a an edge detector that can be used for any regular purpose by displaying how this method generalize across all datasets

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  J. Canny.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Sean Dougherty,et al.  Edge detector evaluation using empirical ROC curves , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[7]  Zhuowen Tu,et al.  Supervised Learning of Edges and Object Boundaries , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Frédéric Jurie,et al.  Groups of Adjacent Contour Segments for Object Detection , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[10]  Kurt Keutzer,et al.  Efficient, high-quality image contour detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Peter Kontschieder,et al.  Structured class-labels in random forests for semantic image labelling , 2011, 2011 International Conference on Computer Vision.

[13]  Nathan Silberman,et al.  Indoor scene segmentation using a structured light sensor , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[14]  Antonio Criminisi,et al.  Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2012, Found. Trends Comput. Graph. Vis..

[15]  Xiaofeng Ren,et al.  Discriminatively Trained Sparse Code Gradients for Contour Detection , 2012, NIPS.

[16]  Joseph J. Lim,et al.  Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Jitendra Malik,et al.  Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  C. Lawrence Zitnick,et al.  Fast Edge Detection Using Structured Forests , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.