Basit öğe kaydını göster

dc.contributor Graduate Program in Computer Engineering.
dc.contributor.advisor Akarun, Lale.
dc.contributor.author Eryılmaz, Giray Naim.
dc.date.accessioned 2023-10-15T06:43:05Z
dc.date.available 2023-10-15T06:43:05Z
dc.date.issued 2022
dc.identifier.other CMPE 2022 E79
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/19698
dc.description.abstract Blur impairs the sharpness of visual features and the clarity of details. It may sometimes be desired for artistic effect. However, in general, it is regarded as a defect. There are different problems studied about blur, such as blur detection, segmentation, estimation, and deblurring, but despite its abundance in visual media such as pho tographs and videos, there is limited annotated data about blur. This lack of data inhibits the usage of deep learning models because they require a lot of annotated data. Annotating that much data is expensive and cumbersome. In this thesis, we in vestigate blur-vs-sharp classification using deep learning, also we experiment with weak supervision as a remedy against the lack of data for blur assessment and localization. We compare our results with the classical approaches found in the literature. We use the data we annotated from four different datasets, three of which are sign language datasets and the other one is an action recognition dataset. We focus our research on sign language videos where motion blur is frequently encountered. Sign languages are the primary communication method of Deaf community and for that reason sign language recognition (SLR) is an important task. Determining the intensity of blur and its location may be beneficial for SLR research.
dc.publisher Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022.
dc.subject.lcsh Image processing.
dc.subject.lcsh Image analysis.
dc.subject.lcsh Sign language -- Research.
dc.title Blur assessment in sign language videos
dc.format.pages xi, 61 leaves


Bu öğenin dosyaları

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster

Dijital Arşivde Ara


Göz at

Hesabım