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Methods and datasets on semantic segmentation: A review
Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …
DeepIGeoS: a deep interactive geodesic framework for medical image segmentation
Accurate medical image segmentation is essential for diagnosis, surgical planning and
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …
Semantic image segmentation via deep parsing network
This paper addresses semantic image segmentation by incorporating rich information into
Markov Random Field (MRF), including high-order relations and mixture of label contexts …
Markov Random Field (MRF), including high-order relations and mixture of label contexts …
Incorporating prior knowledge in medical image segmentation: a survey
Medical image segmentation, the task of partitioning an image into meaningful parts, is an
important step toward automating medical image analysis and is at the crux of a variety of …
important step toward automating medical image analysis and is at the crux of a variety of …
Review and discussion: E-learning for academia and industry
This paper presents a high level review and discussion about e-learning and proposes the
use of interactive learning as a recommended method for staff training in industry and …
use of interactive learning as a recommended method for staff training in industry and …
[ספר][B] Decision forests for computer vision and medical image analysis
A Criminisi, J Shotton - 2013 - books.google.com
Decision forests (also known as random forests) are an indispensable tool for automatic
image analysis. This practical and easy-to-follow text explores the theoretical underpinnings …
image analysis. This practical and easy-to-follow text explores the theoretical underpinnings …
Top-down visual saliency via joint CRF and dictionary learning
Top-down visual saliency is an important module of visual attention. In this work, we propose
a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a …
a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a …
Automatic shadow detection and removal from a single image
We present a framework to automatically detect and remove shadows in real world scenes
from a single image. Previous works on shadow detection put a lot of effort in designing …
from a single image. Previous works on shadow detection put a lot of effort in designing …
Segmenting salient objects from images and videos
In this paper we introduce a new salient object segmentation method, which is based on
combining a saliency measure with a conditional random field (CRF) model. The proposed …
combining a saliency measure with a conditional random field (CRF) model. The proposed …
Structured learning and prediction in computer vision
Powerful statistical models that can be learned efficiently from large amounts of data are
currently revolutionizing computer vision. These models possess a rich internal structure …
currently revolutionizing computer vision. These models possess a rich internal structure …