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A Novel Method for Performance Analysis of classifiers in Haze Detection

机译:雾度检测中分类器性能分析的一种新方法

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Nowadays quality of outdoor as well as indoor images are very important. But outdoor images are deteriorated by haze, fog, rain etc. These atmosphere conditions adversely affect the visibility of the images and reduces the contrast. It is very much adverse in applications especially in military. Image de-hazing algorithm are commonly used to restore the original image which is affected by haze. Fine details in the images are lost due to haze. But if we are applying haze removal algorithms for non-hazy images then the image may get blurred. So haze detection algorithms are necessary before the application of haze removal algorithms. Herein, an algorithm for detection of haze using different classifiers has been implemented and the performance of classifiers are measured and tabulated. In this work initially a number of image features are estimated to train different classifiers such as support vector machine, logistic regression na?ve Bayes Classifiers and Decision Tree.
机译:如今室外和室内图像的质量非常重要。 但户外图像由雾度,雾,雨等恶化。这些大气条件对图像的可见性产生不利影响并降低对比度。 它在尤其是军队中的应用是非常不利的。 图像De-HATHing算法通常用于恢复受阴霾影响的原始图像。 由于雾度,图像中的细节丢失。 但是,如果我们正在为非朦胧图像应用雾霾去除算法,那么图像可能会被模糊。 因此,在雾霾去除算法应用之前需要雾度检测算法。 这里,已经实现了一种用于使用不同分类器检测雾度的算法,并测量分类器的性能和制表。 在这项工作中,最初估计许多图像特征以训练不同的分类器,例如支持向量机,逻辑回归Na ve贝雷斯分类器和决策树。

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