论文标题
对更快对象识别的图像预处理的研究
A Study of Image Pre-processing for Faster Object Recognition
论文作者
论文摘要
图像质量始终在对象识别或分类速率中起着至关重要的作用。优质的图像比任何未经处理的嘈杂图像都能提供更好的识别或分类率。从降低对象识别或分类率的未加工图像中提取功能更加困难。为了克服问题，由于质量较低而发生的问题，通常在从图像中提取特征之前进行预处理。我们的项目提出了一种图像预处理方法，以使所选的机器学习算法或深度学习算法的性能随着准确性的提高或减少培训图像的数量而增加。在后面的一部分中，我们通过使用我们的方法与先前的使用方法来比较性能结果。
Quality of image always plays a vital role in in-creasing object recognition or classification rate. A good quality image gives better recognition or classification rate than any unprocessed noisy images. It is more difficult to extract features from such unprocessed images which in-turn reduces object recognition or classification rate. To overcome problems occurred due to low quality image, typically pre-processing is done before extracting features from the image. Our project proposes an image pre-processing method, so that the performance of selected Machine Learning algorithms or Deep Learning algorithms increases in terms of increased accuracy or reduced the number of training images. In the later part, we compare the performance results by using our method with the previous used approaches.
