A Multiobjective Approach to Homography Estimation

In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm.

[1]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[2]  WangHanzi,et al.  Robust Adaptive-Scale Parametric Model Estimation for Computer Vision , 2004 .

[3]  H. S. Li,et al.  Non-dominated sorting differential evolution algorithm for multi-objective optimal integrated generation bidding and scheduling , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[4]  Jiannong Cao,et al.  Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems , 2013, IEEE Transactions on Cybernetics.

[5]  Cang Ye,et al.  NCC-RANSAC: A Fast Plane Extraction Method for 3-D Range Data Segmentation , 2014, IEEE Transactions on Cybernetics.

[6]  Shaogang Gong,et al.  Hallucinating multiple occluded face images of different resolutions , 2006, Pattern Recognit. Lett..

[7]  Shang-Hong Lai,et al.  A consensus sampling technique for fast and robust model fitting , 2009, Pattern Recognit..

[8]  Josechu J. Guerrero,et al.  Visual correction for mobile robot homing , 2005, Robotics Auton. Syst..

[9]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[10]  Peter Meer,et al.  Beyond RANSAC: User Independent Robust Regression , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[11]  James P. Ostrowski,et al.  Visual motion planning for mobile robots , 2002, IEEE Trans. Robotics Autom..

[12]  Peter Meer,et al.  ROBUST TECHNIQUES FOR COMPUTER VISION , 2004 .

[13]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[15]  Matthias Ehrgott,et al.  Minmax robustness for multi-objective optimization problems , 2014, Eur. J. Oper. Res..

[16]  Richard Szeliski,et al.  Creating full view panoramic image mosaics and environment maps , 1997, SIGGRAPH.

[17]  Ronald Chung,et al.  Homography-based partitioning of curved surface for stereo correspondence establishment , 2007, Pattern Recognit. Lett..

[18]  Jiri Matas,et al.  Randomized RANSAC with T(d, d) test , 2002, BMVC.

[19]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[20]  Peter J. Fleming,et al.  Methods for multi-objective optimization: An analysis , 2015, Inf. Sci..

[21]  Durga Prasad Mohapatra,et al.  Multi-objective test prioritization via a genetic algorithm , 2014, Innovations in Systems and Software Engineering.

[22]  Jiri Matas,et al.  Randomized RANSAC with Td, d test , 2004, Image Vis. Comput..

[23]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[24]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

[25]  Muhittin Gökmen,et al.  Super-resolution reconstruction of faces by enhanced global models of shape and texture , 2012, Pattern Recognit..

[26]  Shengxiang Yang,et al.  Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization , 2014, IEEE Transactions on Cybernetics.

[27]  Carlos Sagüés,et al.  Distributed multi-camera visual mapping using topological maps of planar regions , 2011, Pattern Recognit..

[28]  David Suter,et al.  Robust adaptive-scale parametric model estimation for computer vision , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[30]  Carlos Hitoshi Morimoto,et al.  Multiple camera people detection and tracking using support integration , 2011, Pattern Recognit. Lett..

[31]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[32]  Junping Zhang,et al.  Super-resolution of human face image using canonical correlation analysis , 2010, Pattern Recognit..

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

[34]  Stuart B. Heinrich.  Efficient and robust model fitting with unknown noise scale , 2013, Image Vis. Comput..

[35]  Gonzalo López-Nicolás,et al.  Visual control of vehicles using two-view geometry , 2010 .

[36]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[37]  Erik Valdemar Cuevas Jiménez,et al.  A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm , 2015, Comput. Intell. Neurosci..

[38]  Ming Wang,et al.  Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach for Discrete Problems , 2013, IEEE Transactions on Cybernetics.

[39]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[40]  Hugo Jair Escalante,et al.  Multi-objective model type selection , 2014, Neurocomputing.

[41]  Rahul Sukthankar,et al.  Fast and accurate global motion compensation , 2011, Pattern Recognit..

[42]  Ronald Chung,et al.  Image mosaicking for polyhedral scene and in particular singly visible surfaces , 2008, Pattern Recognit..

[43]  Jing Liu,et al.  A Multiobjective Evolutionary Algorithm Based on Similarity for Community Detection From Signed Social Networks , 2014, IEEE Transactions on Cybernetics.