Segmentación de Imágenes Basada en Entropía de Pixel
In this research, a new methodology for color image segmentation is proposed. In this approach, a pixel entropy derivative based rule is used. The algorithm is tested not only with good quality images, but also with some of them with light scattering and absortion problems. Preliminary results shows good performance of this algorithm.
Keywords: Image processing, segmentation, entropy, feedback.
 Grigorescu S., Ristić-Durrant D., Vuppala S. and Gräser A. (2008). “Closed-Loop Control in Image Processing for Improvement of Object Recognition”. Proceedings of the 17th World Congress of the International Federation of Automatic Control. Seoul, Korea, July 6-11, 2008
 Koik B. and Ibrahim H. (2013). “A Literature Survey on Blur Detection Algorithms for Digital Imaging”. 2013 First International Conference on Artificial Intelligence, Modelling & Simulation.
 Lei, X. and Fu A. (2008). “Two-Dimensional Maximum Entropy Image Segmentation Method Based on Quantum-behaved Particle Swarm Optimization Algorithm”. Fourth International Conference on Natural Computation.
 Peng W., Hongling X., Wenlin L. and Wenlong S. (2016). “Harris Scale Invariant Corner Detection Algorithm Based on the Significant Region”. International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9, No.3 (2016), pp.413- 420.
 Ristić-Durrant D and Gräser D (2008). “Closed-Loop Control of Segmented Image Quality for Improvement of Digital Image Processing”. Automatic Control and Robotics Vol. 7, No 1, 2008, pp. 27 – 34.
 Sahoo P. and Arora G. (2004). “Thresholding method based on two-dimensional Renyi’s entropy”. Pattern Recognition 37 (2004) 1149 – 1161
 Szeliski R. (2010). “Computer Vision: Algorithms and Applications”. Springer 2010.
 Sun L., Wang S. and Xing J. (2014). “An Improved Harris Corner Detection Algorithm for Low Contrast Image”. The 26th Chinese Control and Decision Conference.