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Automatic Red-Eye Object Classification In Digital Images Using A Boosting-Based Framework
Automatic Red-Eye Object Classification In Digital Images Using A Boosting-Based Framework
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机译:使用基于Boosting的框架对数字图像中的红眼对象进行自动分类
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摘要
Automatic red-eye object classification in digital images using a boosting-based framework. In a first example embodiment, a method for classifying a candidate red-eye object in a digital photographic image includes several acts. First, a candidate red-eye object in a digital photographic image is selected. Next, a search scale set and a search region for the candidate red-eye object where an eye object may reside is determined. Then, the number of subwindows that satisfy an AdaBoost classifier is determined. This number is denoted as a vote. Next, the maximum size of the subwindows that satisfy the AdaBoost classifier is determined. Then, a normalized threshold is calculated by multiplying a predetermined constant threshold by the calculated maximum size. Next, the vote is compared with the normalized threshold. Finally, the candidate red-eye object is transformed into a true red-eye object if the vote is greater than the normalized threshold.
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