文献类型：期刊文献

英文题名：Thermal image colorization using Markov decision processes

作者：Gu, Xiaojing[1];He, Mengchi[1];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China

年份：2017

卷号：9

期号：1

起止页码：15

外文期刊名：MEMETIC COMPUTING

收录：;EI(收录号：20162202441056);WOS:【SCI-EXPANDED(收录号:WOS:000394345000003)】；

基金：The work was supported by National Natural Science Foundation of China under Grant No. 61205017, 61502293, 61573144, 61375007 and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Thermal image; Color night-vision; Object recognition; Markov decision processes; Probabilistic color blending

摘要：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.