Image Restoration Using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique
نام عام مواد
[Thesis]
نام نخستين پديدآور
Martin-King, Chloe Furness
نام ساير پديدآوران
Allali, Mohamed
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Chapman University
تاریخ نشرو بخش و غیره
2019
يادداشت کلی
متن يادداشت
142 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
Chapman University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also effective in object removal tasks. Lastly, we present a segmentation system for distinguishing glands, stroma, and cells in slide images, in addition to current results, as one component of an ongoing project to aid in colon cancer prognostication.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Artificial intelligence
اصطلاح موضوعی
Histology
اصطلاح موضوعی
Mathematics
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )