18,637 research outputs found
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.Comment: Accepted in IEEE Transactions on Geoscience and Remote Sensing, 201
3D Shape Segmentation with Projective Convolutional Networks
This paper introduces a deep architecture for segmenting 3D objects into
their labeled semantic parts. Our architecture combines image-based Fully
Convolutional Networks (FCNs) and surface-based Conditional Random Fields
(CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are
used for efficient view-based reasoning about 3D object parts. Through a
special projection layer, FCN outputs are effectively aggregated across
multiple views and scales, then are projected onto the 3D object surfaces.
Finally, a surface-based CRF combines the projected outputs with geometric
consistency cues to yield coherent segmentations. The whole architecture
(multi-view FCNs and CRF) is trained end-to-end. Our approach significantly
outperforms the existing state-of-the-art methods in the currently largest
segmentation benchmark (ShapeNet). Finally, we demonstrate promising
segmentation results on noisy 3D shapes acquired from consumer-grade depth
cameras.Comment: This is an updated version of our CVPR 2017 paper. We incorporated
new experiments that demonstrate ShapePFCN performance under the case of
consistent *upright* orientation and an additional input channel in our
rendered images for encoding height from the ground plane (upright axis
coordinate values). Performance is improved in this settin
Dynamic spot analysis in the 2D electrophoresis gels images
Práce shrnuje faktory a parametry, které ovlivňují výsledky 2D elektroforézy, se zaměřením na zpracování obrazu jako jeden ze způsobů snížení nesprávné interpretace jejích výstupů. Proces zpracování obrazu využívá jako zdroj dat především obrazů z opakovaných provedení téhož pokusu, neboli víceplik. Pomocí analýzy obrazů víceplik je možno pozorovat nebo korigovat změny jednoho pokusu a také porovnávat je s výstupy jiných pokusů. Cílem práce je poskytnout podporu specialistovi, který má na starosti popsat vlastnosti struktur nacházejících se v elektroforetických obrazech.The text briefly describes factors and parameters which influence the results of 2D electrophoresis focusing on image processing as one manner to reduce incorrect interpretation of its outputs. As dataset, image processing performance uses images from repeated execution of one experiment also known as multiplicates. Using multiplicates analysis it is possible to observe or lower the changes of one experiment and to compare them with outputs of other experiments. The aim of this work is to provide support for specialist who takes care about describing the character patterns located in electrophoretic images.
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