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Decision Forests For Computer Vision And Medical Image Analysis
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The article was published on 2016-01-01 and is currently open access. It has received 181 citations till now.read more
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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
Adrian Kaehler,Gary Bradski +1 more
TL;DR: Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.
Journal ArticleDOI
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.
Wenlu Zhang,Rongjian Li,Houtao Deng,Li Wang,Weili Lin,Shuiwang Ji,Dinggang Shen,Dinggang Shen +7 more
TL;DR: This paper proposes to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images, and compared the performance of the approach with that of the commonly used segmentation methods on a set of manually segmented isointENSE stage brain images.
Proceedings ArticleDOI
Fast and accurate image upscaling with super-resolution forests
TL;DR: This paper shows the close relation of previous work on single image super-resolution to locally linear regression and demonstrates how random forests nicely fit into this framework, and proposes to directly map from low to high-resolution patches using random forests.
Journal ArticleDOI
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
TL;DR: This paper presents the culmination of the research in designing a system for computer-aided detection of polyps in colonoscopy videos based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps.
Journal ArticleDOI
Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model
TL;DR: Experimental results show that the learning-based method proposed can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.
References
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Book
Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
Adrian Kaehler,Gary Bradski +1 more
TL;DR: Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.
Journal ArticleDOI
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.
Wenlu Zhang,Rongjian Li,Houtao Deng,Li Wang,Weili Lin,Shuiwang Ji,Dinggang Shen,Dinggang Shen +7 more
TL;DR: This paper proposes to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images, and compared the performance of the approach with that of the commonly used segmentation methods on a set of manually segmented isointENSE stage brain images.
Proceedings ArticleDOI
Fast and accurate image upscaling with super-resolution forests
TL;DR: This paper shows the close relation of previous work on single image super-resolution to locally linear regression and demonstrates how random forests nicely fit into this framework, and proposes to directly map from low to high-resolution patches using random forests.
Journal ArticleDOI
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
TL;DR: This paper presents the culmination of the research in designing a system for computer-aided detection of polyps in colonoscopy videos based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps.
Proceedings ArticleDOI
Monocular Depth Estimation Using Neural Regression Forest
Anirban Roy,Sinisa Todorovic +1 more
TL;DR: This paper presents a novel deep architecture, called neural regression forest (NRF), for depth estimation from a single image, which combines random forests and convolutional neural networks (CNNs).