원문정보
초록
영어
In this paper, two methods of extracting objects are compared through application to underwater images: one method is to extract objects by removing the background and quantifying it into a codebook by measuring the Mahalanobis distance for accurate object segmentation and extraction, and the other is to extract objects by removing the background and quantifying it into a codebook by measuring the Euclidean distance. In an experiment relating to the comparison and analysis, a standard underwater sample image was learned. Then, the background color’s average value and the input image’s stochastic distances were computed through the color similarity algorithm, and then the object was extracted after the background color could be removed. For the performance evaluation on the two algorithms, an underwater image was used to run some computer simulations. The experiment showed that an image applied with the color similarity algorithm had a better image segmentation performance than that with the different image technique.
목차
1. Introduction
2. Related Work
3. The Design of Algorithms for Comparison
3.1. Underwater Background Learning
3.2. Similarity Measure-Mahalanobis Distances
3.3. Similarity Measure-Euclidean Distances
4. Experiments
4.1. Experimental Methods
4.2. Results
5. Conclusion
References