This paper deals with infrared and visible image fusion problems by maximum a posteriori probability (MAP) estimation. We use imaging mode to construct the conditional probability distribution, assume the fusion image approximate the visible image, and treat sparse property of image gradient as fusion image prior probability distribution. According Bayesian theorem, the fusion image’s posterior probability distribution is deduced. The fusion results are obtained by maxim the posterior probability distribution. In experiments, we conduct subjective and objective evaluation. The comparisons show that the MAP-based image fusion method has better performance in both subjective and objective evaluations. The MAP-based image fusion method can be applied to image interpretation, detection and recognition tasks.
Corrosion is a serious issue causing damage in steel facilities. Timely inspection and repair is essential to avoid unprecedented structural failures. Employing non-destructive methods of manual inspection for large number of antennas to detect corrosion and related damages is time consuming and expensive. In addition to this, safety of inspector to climb structures possibly weakened by corrosion. In such a situation non-contact approach of automated visual inspection for corrosion and related damage detection through image processing of aerial based images is a viable option. For robust corrosion segmentation and detection, we investigate color classification based on random forest. A random forest is a statistical framework with a very high generalization accuracy and quick training times. We evaluate random forest based corrosion detection and compare it to Bayesian network, Multilayer Perceptron, SVM, Naive Bayes and RBF network. Results on a database of real images with manually annotated pixel-level ground truth show that with the IHLS colour space, the random forest approach outperforms other approaches.
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