Effect of image resolution on intensity based scene illumination classification using neural network

References

• Zhou, L., Zhou, Z. and Hu, D. Scene classification using a multi-resolution bag-of-features model. Pattern Recognition, 2013, 46, 424–433. doi: 10.1016/j.patcog.2012.07.017
   Web of Science ®Google Scholar
• Tominaga, S., Ebisui, S. and Wandell A, B. Scene illuminant classification: brighter is better. JOSA A, 2001, 18, 55–64. doi: 10.1364/JOSAA.18.000055
   Google Scholar
• Boom, B., Beumer, G., Spreeuwers, L. J. and Veldhuis, R. N. The effect of image resolution on the performance of a face recognition system. 2006.
   Google Scholar
• Wang, J., Zhang, C. and Shum, H. -Y. Face image resolution versus face recognition performance based on two global methods., in Proc. Asian Conf. on 'Computer vision, Jeju Island, Korea 2004.
   Google Scholar
• Lemieux, A. and Parizeau, M. Experiments on eigenfaces robustness. Proc. 16th Int. Conf. on 'Pattern recognition, 2002, pp. 421–424.
   Google Scholar
• Fookes, C., Lin, F., Chandran, V. and Sridharan, S. Evaluation of image resolution and super-resolution on face recognition performance. J. Visual Commun. Image Represent, 2012, 23, 75–93. doi: 10.1016/j.jvcir.2011.06.004
   Web of Science ®Google Scholar
• Chen, L., Tokuda, N., Nagai, A. and Chen, X. Is high resolution representation more effective for content based image classification?, Proc. Int. Joint Conf. on 'Neural networks, IJCNN'06 2006, pp. 4045–4050..
   Google Scholar
• Deng, J., Dong, W., Socher, R., Li, L. -J., Li, K. and Fei-Fei, L. Imagenet: a large-scale hierarchical image database., Proc. IEEE Conf. on 'Computer vision and pattern recognition, CVPR 2009, pp. 248–255..
   Google Scholar
• Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J. and Davis, J. Scape: shape completion and animation of people. ACM Trans. Graphics, 2005, 408–416. doi: 10.1145/1073204.1073207
   Web of Science ®Google Scholar
• Tong, J., Zhou, J., Liu, L., Pan, Z. and Yan, H. Scanning 3d full human bodies using kinects. IEEE Trans. Visual. Comput. Graphics, 2012, 18, 643–650. doi: 10.1109/TVCG.2012.56
   PubMed Web of Science ®Google Scholar
• Boellaard, R., Krak, N. C., Hoekstra, O. S. and Lammertsma, A. A. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study. J. Nucl. Med., 2004, 45, 1519–1527.
   PubMed Web of Science ®Google Scholar
• Hesamian, M., Mashohor, S., Saripan, M. I. and Wan Adnan, W. Scene illumination classification using illumination histogram analysis and neural network., Proc. IEEE Int. Conf. on 'Control system, computing and engineering (ICCSCE) 2013..
   Google Scholar
• Barnard, K., Cardei, V. and Funt, B. A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data. IEEE Trans. Image Process, 2002, 11, 972–984. doi: 10.1109/TIP.2002.802531
   PubMed Web of Science ®Google Scholar
• Ebner, M. Color constancy, 2007 (John Wiley & Sons, New York), Vol. 6.
   Google Scholar
• Hagan, M. T. and Menhaj, M. B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks, 1994, 5, 989–993. doi: 10.1109/72.329697
   PubMed Web of Science ®Google Scholar
• Pradeep, J., Srinivasan, E. and Himavathi, S. Diagonal based feature extraction for handwritten alphabets recognition system using neural network, arXiv preprint arXiv. 2011, 11030365.
   Google Scholar
• Weintraub, M., Beaufays, F., Rivlin, Z. e., Konig, Y. and Stolcke, A. Neural-network based measures of confidence for word recognition., Proc. IEEE Int. Conf. on 'Acoustics, speech, and signal processing 1997, 887–887..
   Google Scholar
• Devijver, P. A. and Kittler, J. Pattern recognition: a statistical approach., 1982 (Prentice-Hall, London), Vol. 761.
   Google Scholar
• Shahsavari, S., Bagheri, G., Mahjub, R., Bagheri, R., Radmehr, M., Rafiee-Tehrani, M., et al. Application of artificial neural networks for optimization of preparation of insulin nanoparticles composed of quaternized aromatic derivatives of chitosan. Drug Res., 2014, 64, 151–158.
   PubMedGoogle Scholar
• Armstrong, J. S. Illusions in regression analysis., 2011.
   Google Scholar