Enhanced image coverage using evolutionary keypoint selection

References

• Bostanci E, Kanwal N, Clark AF. Spatial statistics of image features for performance comparison. IEEE Trans Image Process. Jan. 2014;23(1):153–162. doi: 10.1109/TIP.2013.2286907
   PubMed Web of Science ®Google Scholar
• Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110. doi: 10.1023/B:VISI.0000029664.99615.94
   Web of Science ®Google Scholar
• Hartley R, Zisserman A. Multiple view geometry in computer vision. Cambridge: Cambridge University Press; 2003.
   Google Scholar
• Sola J. Towards visual localization, mapping and moving objects tracking by a mobile robot: a geometric and probabilistic approach, PhD thesis, Institut National Politechnique de Toulouse, 2007.
   Google Scholar
• Ma Y, Soatto S, Kosecka J, et al. An invitation to 3-D vision: from images to geometric models. New York: Springer; 2004.
   Google Scholar
• Nixon MS, Aguado AS. Feature extraction and image processing Electronics & Electrical. Oxford: Newnes; 2002.
   Google Scholar
• Neubeck A, Van Gool L. Efficient non-maximum suppression. 18th International Conference on ‘Pattern Recognition 2006 (ICPR 2006)’, 2006; 3:850–855.
   Google Scholar
• Smith SM, Brady JM. Susan: a new approach to low level image processing. Int J Comput Vis. 1997;23(1):45–78. doi: 10.1023/A:1007963824710
   Web of Science ®Google Scholar
• Rosten E, Porter R, Drummond T. Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell. 2010;32:105–119. doi: 10.1109/TPAMI.2008.275
   PubMed Web of Science ®Google Scholar
• Förstner W, Dickscheid T, Schindler F. Detecting interpretable and accurate scale-invariant keypoints. 12th IEEE International Conference on ‘Computer Vision’ (ICCV'09), Kyoto, Japan, 2009.
   Google Scholar
• Perdoch M, Matas J, Obdrzalek S. Stable affine frames on isophotes. ICCV, 2007.
   Google Scholar
• Dickscheid T, Förstner W. Evaluating the suitability of feature detectors for automatic image orientation systems. ICVS 2009, LNCS: 5815, 2009, pp. 305–314.
   Google Scholar
• Tuytelaars T. Dense interest points. IEEE Conf. on ‘Computer vision and pattern recognition (CVPR’10)', 2010, pp. 2281–2288.
   Google Scholar
• Ehsan S, Kanwal N, Clark AF, et al. Measuring the coverage of interest point detectors. Image Analysis and Recognition (ICIAR'11), LNCS:6753, 2011, pp. 253–261.
   Google Scholar
• Dickscheid T, Schindler F, Förstner W. Coding images with local features. Int J Comput Vis. 2011;94:154–174. doi: 10.1007/s11263-010-0340-z
   Web of Science ®Google Scholar
• Aanæs H, Dahl AL, Pedersen KS. Interesting interest points—a comparative study of interest point performance on a unique data set. Int J Comput Vis. 2011, 1–18.
   Web of Science ®Google Scholar
• Ehsan S, Clark AF, McDonald-Maier KD. Rapid online analysis of local feature detectors and their complementarity. Sensors. 2013;13(8):10876–10907. doi: 10.3390/s130810876
   PubMed Web of Science ®Google Scholar
• Ripley BD. The second-order analysis of stationary point processes. J Appl Probab. 1976;13:255–266. doi: 10.1017/S0021900200094328
   Web of Science ®Google Scholar
• Ripley BD. Modelling spatial patterns. J R Stat Soc. 1977;39:172–212.
   Google Scholar
• Bostanci E, Kanwal N, Clark AF. Feature coverage for better homography estimation: an application to image stitching. Proceedings of the IEEE international Conference on ‘Systems, Signals and Image Processing’; 2012.
   Google Scholar
• Crawley MJ. The R book. West Sussex: Wiley-Blackwell; 2007.
   Google Scholar
• Bivand RS, Pebesma EJ, Gomez-Rubio V. Applied spatial analysis with R. New York: Springer; 2008.
   Google Scholar
• Chen L, Chen B, Chen Y. Image feature selection based on ant colony optimization. ‘AI 2011: Advances in Artificial Intelligence’, Vol. 7106 of Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. p. 580–589.
   Google Scholar
• Ozkan D. Feature selection for face recognition using a genetic algorithm, Department of Computer Engineering, Bilkent University, 2006.
   Google Scholar
• Dixon PM. Ripley's K function. Encycl Environmetrics. 2002;3:1796–1803.
   Google Scholar
• Eiben AE, Smith JE. Introduction to evolutionary computing. Heidelberg: Springer Verlag; 2003.
   Google Scholar
• Goldberg DE. Genetic algorithms in search, optimization and machine learning, 1st edn. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.; 1989.
   Google Scholar
• Still M. The definitive guide to ImageMagick (Definitive Guide). Berkely CA USA: Apress; 2005.
   Google Scholar
• Bostanci B, Bostanci E. An evaluation of classification algorithms using mc nemar's test. Proceedings of the 7th International Conference on ‘Bio-inspired computing: theories and applications (BIC-TA 2012)’, Vol. 201 of Advances in Intelligent Systems and Computing. India: Springer; 2013. pp. 15–26.
   Google Scholar
• Bostanci E. Is hamming distance only way for matching binary image feature descriptors? IET Electron Lett. May 2014;50(11):806–808. doi: 10.1049/el.2014.0773
   Web of Science ®Google Scholar
• Student. The probable error of a mean. Biometrika. 1908;6(1):1–25. doi: 10.1093/biomet/6.1.1
   Google Scholar
• McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika. 1947;12(2):153–157. doi: 10.1007/BF02295996
   PubMed Web of Science ®Google Scholar
• Clark A, Clark C. Performance evaluation in computer vision: a tutorial, 1999.
   Google Scholar
• Pospichal P, Jaros J, Schwarz J. Parallel genetic algorithm on the CUDA architecture. ‘Applications of evolutionary computation’, Vol. 6024 of Lecture Notes in Computer Science, 2010, pp. 442–451. Springer, Berlin, Heidelberg.
   Google Scholar