Selective Video Object Cutout
نویسندگان
چکیده
منابع مشابه
Principal-channels for One-sided Object Cutout
We introduce principal-channels for cutting out objects from an image by one-sided scribbles. We demonstrate that few scribbles, all from within the object of interest, are sufficient to mark it out. One-sided scribbles provide significantly less information than two-sided ones. Thus, it is required to maximize the use of image-information. Our approach is to first analyze the image with a larg...
متن کاملSegmentation Rectification for Video Cutout via One-Class Structured Learning
Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on optimally removing errors from the FB classification is sparse, and the errors often accumulate rapidly, causing significant errors in the propagated frames. In this work, we take the initial steps to addressing this prob...
متن کاملA Holistic Approach for Data-Driven Object Cutout
Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerable background clutter. In contrast to existing cutout methods, which are based mainly on low-level image analysis, we propose a more holistic approach, which considers the entire shape of the object of interest by lev...
متن کاملInteractive Video Object Annotation
We present interactive techniques for visually annotating independently moving objects in a video stream. Features in the video are automatically tracked and grouped in an off-line preprocess that enables later interactive manipulation and annotation. Examples of such annotations include speech and thought balloons, video graffiti, hyperlinks, and path arrows. Our system also employs a directma...
متن کاملVideo Object Segmentation using Tracked Object Proposals
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 [8] challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2017
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2017.2745098