1,295 research outputs found
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse
depth data, guided by high-resolution imagery. Our approach goes beyond the use
of intensity cues only and additionally exploits object boundary cues through
structured edge detection and semantic scene labeling for guidance. Both cues
are combined within a geodesic distance measure that allows for
boundary-preserving depth in- terpolation while utilizing local context. We
model the observed scene structure by locally planar elements and formulate the
upsampling task as a global energy minimization problem. Our method determines
glob- ally consistent solutions and preserves fine details and sharp depth
bound- aries. In our experiments on several public datasets at different levels
of application, we demonstrate superior performance of our approach over the
state-of-the-art, even for very sparse measurements.Comment: German Conference on Pattern Recognition 2016 (Oral
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
Recent trends in image understanding have pushed for holistic scene
understanding models that jointly reason about various tasks such as object
detection, scene recognition, shape analysis, contextual reasoning, and local
appearance based classifiers. In this work, we are interested in understanding
the roles of these different tasks in improved scene understanding, in
particular semantic segmentation, object detection and scene recognition.
Towards this goal, we "plug-in" human subjects for each of the various
components in a state-of-the-art conditional random field model. Comparisons
among various hybrid human-machine CRFs give us indications of how much "head
room" there is to improve scene understanding by focusing research efforts on
various individual tasks
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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