Thermal image colorization using Markov decision processes
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- 作者：Xiaojing Gu ; Mengchi He ; Xingsheng Gu
- 关键词：Thermal image ; Color night ; vision ; Object recognition ; Markov decision processes ; Probabilistic color blending
- 刊名：Memetic Computing
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：9
- 期：1
- 页码：15-22
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- 刊物类别：Engineering
- 刊物主题：Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Complex Systems; Control, Robotics, Mechatronics; Bioinformatics; Applications of Mathematics;
- 出版者：Springer Berlin Heidelberg
- ISSN：1865-9292
- 卷排序：9
文摘
Displaying night-vision thermal images with day-time colors is paramount for scene interpretation and target tracking. In this paper, we employ object recognition methods for colorization, which amounts to segmenting thermal images into plants, buildings, sky, water, roads and others, then calculating colors to each class. The main thrust of our work is the introduction of Markov decision processes (MDP) to deal with the computational complexity of the colorization problem. MDP provides us with the approaches of neighborhood analysis and probabilistic classification which we exploit to efficiently solve chromatic estimation. We initially label the segments with a classifier, paving the way for the neighborhood analysis. We then update classification confidences of each class by MDP under the consideration of neighboring consistency and scenery layout. Finally we calculate the colors for every segment by blending the characteristic colors of each class it belongs to in a probabilistic way. Experimental results show that the colorized appearance of our algorithm is satisfactory and harmonious; the computational speed is quite fast as well.