2 research outputs found
Salient Image Matting
In this paper, we propose an image matting framework called Salient Image
Matting to estimate the per-pixel opacity value of the most salient foreground
in an image. To deal with a large amount of semantic diversity in images, a
trimap is conventionally required as it provides important guidance about
object semantics to the matting process. However, creating a good trimap is
often expensive and timeconsuming. The SIM framework simultaneously deals with
the challenge of learning a wide range of semantics and salient object types in
a fully automatic and an end to end manner. Specifically, our framework is able
to produce accurate alpha mattes for a wide range of foreground objects and
cases where the foreground class, such as human, appears in a very different
context than the train data directly from an RGB input. This is done by
employing a salient object detection model to produce a trimap of the most
salient object in the image in order to guide the matting model about
higher-level object semantics. Our framework leverages large amounts of coarse
annotations coupled with a heuristic trimap generation scheme to train the
trimap prediction network so it can produce trimaps for arbitrary foregrounds.
Moreover, we introduce a multi-scale fusion architecture for the task of
matting to better capture finer, low-level opacity semantics. With high-level
guidance provided by the trimap network, our framework requires only a fraction
of expensive matting data as compared to other automatic methods while being
able to produce alpha mattes for a diverse range of inputs. We demonstrate our
framework on a range of diverse images and experimental results show our
framework compares favourably against state of art matting methods without the
need for a trima
Analysis of Big Data Technology for Health Care Services
Deep learning and other big data technologies have over time become very
powerful and accurate. There are algorithms and models developed that have near
human accuracy in their task. In health care, the amount of data available is
massive and hence, these technologies have a great scope in health care. This
paper reviews a few interesting contributions to the field specifically to
medical imaging, genomics and patient health records.Comment: Accepted at the International Conference on Intelligent Technologies
and Applications(INTAP) 201