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Image segmentation is one of the most challenging tasks in the field of image processing. Even the best automatic segmentation approaches cannot yet provide accurate segmentation in all situations. Hence, there is a persistent need for interactive editing tools to correct the automatic segmentation results such that they match what would be clinically accepted by an expert. We present an editing approach...
Discriminative classifiers are often the computational bottleneck in medical imaging applications such as foreground/background classification, 3D pose detection, and boundary delineation. To overcome this bottleneck, we propose a fast technique based on boosting tree classifiers adapted for GPU computation. Unlike standard tree-based algorithms, our method does not have any recursive calls which...
The expanding role of complex object detection algorithms introduces a need for flexible architectures that simplify interfacing with machine learning techniques and offer easy-to-use training and detection procedures. To address this need, the Integrated Detection Network (IDN) proposes a conceptual design for rapid prototyping of object and boundary detection systems. The IDN uses a strong spatial...
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