This paper will present the story of a collaborative project between the Imaging Department and the Paintings Conservation Department of the Metropolitan Museum of Art to use 3D imaging technology to restore missing and broken elements of an intricately carved giltwood frame from the late 18th century.
In the competitive online fashion market place, it is common for sellers to add artificial elements to their product images, with the hope to improve the aesthetic quality of their products. Among the numerous types of artificial elements, we focus on detecting artificial frames in fashion images in this paper and we propose a novel algorithm based on traditional image processing techniques for this purpose. On the other hand, even though deep learning methods have been very powerful and effective in many image processing tasks in recent years, they do have their drawbacks in some cases, rendering them ineffective compared to our method for this particular task. Experimental results on 1000 testing images show that our algorithm has comparable performance with some of the state-of-the-art deep learning models that have been used for classification.