Automated Identification and Classification of Blur Images, Duplicate Images Using Open CV
Automated Identification and Classification of Blur Images, Duplicate Images Using Open CV
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使用 Open CV 自动识别和分类模糊图像、重复图像
DOI：
10.1007/978-981-16-3660-8_52
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
P. Surekha
中科院分区：
文献类型：
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作者：
Gajula Ramesh;Anusha Anugu;K. Madhavi;P. Surekha
随着数码相机和手机的普及，数码图像的数量迅速增加，因为它们现在在市场上以可承受的价格提供。有各种各样的图像质量退化，其中模糊在自然界中起着至关重要的作用。为了获得质量和量化的图像，用户会重复点击相同的图像。随着图像的快速增长，存储设备的空间占用也在增加。以前的研究人员是单独研究这些问题的，所以这个模型将把它们结合在一个单一的方法中。由于用户手动识别模糊图像、重复图像已成为一项关键任务，我们提出了一种新的顺序模型思想，将模糊图像、重复图像按顺序识别，并将这些图像自动存储在由该模型创建的相应文件夹中，用户可以随时查看，否则可以删除该文件夹。它还提供了原始良好图像的视频（无重复），以便用户快速轻松地查看图像。
The number of digital images increasing rapidly with the popularization of digital cameras and mobile phones, as they are now available at affordable prices in the market. There is various image quality degradants out of which the blur plays a vital role in the nature. To get the quality and quantified images users will move on with the clicking of same image repeatedly. With the rapid increment of images, the occupation of space in the storage devices also increases. Previously researchers are worked on these problems individually, so the model will provide them combinedly in a single methodology. As the identification of blur images, duplicate images manually by the users has become a critical task, we come up with a new idea of sequential model to identify blur, duplicate images sequentially and storing those images in their respective folders automatically which are created by this model such that the user can review if they want, else they can delete the folder. It also provides the video of original good images (without duplicates) to review the images quickly and easily by the user.