5,715 research outputs found
Superpixel Convolutional Networks using Bilateral Inceptions
In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and other parameters of the module are learned end-to-end
using standard backpropagation techniques. The bilateral inception module
addresses two issues that arise with general CNN segmentation architectures.
First, this module propagates information between (super) pixels while
respecting image edges, thus using the structured information of the problem
for improved results. Second, the layer recovers a full resolution segmentation
result from the lower resolution solution of a CNN. In the experiments, we
modify several existing CNN architectures by inserting our inception module
between the last CNN (1x1 convolution) layers. Empirical results on three
different datasets show reliable improvements not only in comparison to the
baseline networks, but also in comparison to several dense-pixel prediction
techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
In 3D image/video acquisition, different views are often captured with
varying noise levels across the views. In this paper, we propose a graph-based
image enhancement technique that uses a higher quality view to enhance a
degraded view. A depth map is utilized as auxiliary information to match the
perspectives of the two views. Our method performs graph-based filtering of the
noisy image by directly computing a projection of the image to be filtered onto
a lower dimensional Krylov subspace of the graph Laplacian. We discuss two
graph spectral denoising methods: first using Chebyshev polynomials, and second
using iterations of the conjugate gradient algorithm. Our framework generalizes
previously known polynomial graph filters, and we demonstrate through numerical
simulations that our proposed technique produces subjectively cleaner images
with about 1-3 dB improvement in PSNR over existing polynomial graph filters.Comment: 6 pages, 6 figures, accepted to 2014 IEEE International Conference on
Multimedia and Expo Workshops (ICMEW
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