10,173 research outputs found
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly near depth discontinuities or creases. Studies have however shown
that precisely such depth edges carry critical cues for the perception of
shape, and play important roles in tasks like depth-based segmentation or
foreground selection. Unfortunately, the currently extracted channels often
carry conflicting signals, making it difficult for subsequent applications to
effectively use them. In this paper, we focus on the problem of obtaining
high-precision depth edges (i.e., depth contours and creases) by jointly
analyzing such unreliable information channels. We propose DepthCut, a
data-driven fusion of the channels using a convolutional neural network trained
on a large dataset with known depth. The resulting depth edges can be used for
segmentation, decomposing a scene into depth layers with relatively flat depth,
or improving the accuracy of the depth estimate near depth edges by
constraining its gradients to agree with these edges. Quantitatively, we
compare against 15 variants of baselines and demonstrate that our depth edges
result in an improved segmentation performance and an improved depth estimate
near depth edges compared to data-agnostic channel fusion. Qualitatively, we
demonstrate that the depth edges result in superior segmentation and depth
orderings.Comment: 12 page
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Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
Automatic Leaf Extraction from Outdoor Images
Automatic plant recognition and disease analysis may be streamlined by an
image of a complete, isolated leaf as an initial input. Segmenting leaves from
natural images is a hard problem. Cluttered and complex backgrounds: often
composed of other leaves are commonplace. Furthermore, their appearance is
highly dependent upon illumination and viewing perspective. In order to address
these issues we propose a methodology which exploits the leaves venous systems
in tandem with other low level features. Background and leaf markers are
created using colour, intensity and texture. Two approaches are investigated:
watershed and graph-cut and results compared. Primary-secondary vein detection
and a protrusion-notch removal are applied to refine the extracted leaf. The
efficacy of our approach is demonstrated against existing work.Comment: 13 pages, India-UK Advanced Technology Centre of Excellence in Next
Generation Networks, Systems and Services (IU-ATC), 201
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