2,528 research outputs found
Shadow Optimization from Structured Deep Edge Detection
Local structures of shadow boundaries as well as complex interactions of
image regions remain largely unexploited by previous shadow detection
approaches. In this paper, we present a novel learning-based framework for
shadow region recovery from a single image. We exploit the local structures of
shadow edges by using a structured CNN learning framework. We show that using
the structured label information in the classification can improve the local
consistency of the results and avoid spurious labelling. We further propose and
formulate a shadow/bright measure to model the complex interactions among image
regions. The shadow and bright measures of each patch are computed from the
shadow edges detected in the image. Using the global interaction constraints on
patches, we formulate a least-square optimization problem for shadow recovery
that can be solved efficiently. Our shadow recovery method achieves
state-of-the-art results on the major shadow benchmark databases collected
under various conditions.Comment: 8 pages. CVPR 201
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation
The development of powerful 3D scanning hardware and reconstruction
algorithms has strongly promoted the generation of 3D surface reconstructions
in different domains. An area of special interest for such 3D reconstructions
is the cultural heritage domain, where surface reconstructions are generated to
digitally preserve historical artifacts. While reconstruction quality nowadays
is sufficient in many cases, the robust analysis (e.g. segmentation, matching,
and classification) of reconstructed 3D data is still an open topic. In this
paper, we target the automatic and interactive segmentation of high-resolution
3D surface reconstructions from the archaeological domain. To foster research
in this field, we introduce a fully annotated and publicly available
large-scale 3D surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset to the community. We provide baseline
results for our existing random forest-based approach and for the first time
investigate segmentation with convolutional neural networks (CNNs) on the data.
Results show that both approaches have complementary strengths and weaknesses
and that the provided dataset represents a challenge for future research.Comment: CBMI Submission; Dataset and more information can be found at
http://lrs.icg.tugraz.at/research/petroglyphsegmentation
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a central
role in image understanding. Recent approaches have attempted to harness the
capabilities of deep learning techniques for image recognition to tackle
pixel-level labelling tasks. One central issue in this methodology is the
limited capacity of deep learning techniques to delineate visual objects. To
solve this problem, we introduce a new form of convolutional neural network
that combines the strengths of Convolutional Neural Networks (CNNs) and
Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To
this end, we formulate mean-field approximate inference for the Conditional
Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.
This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a
deep network that has desirable properties of both CNNs and CRFs. Importantly,
our system fully integrates CRF modelling with CNNs, making it possible to
train the whole deep network end-to-end with the usual back-propagation
algorithm, avoiding offline post-processing methods for object delineation. We
apply the proposed method to the problem of semantic image segmentation,
obtaining top results on the challenging Pascal VOC 2012 segmentation
benchmark.Comment: This paper is published in IEEE ICCV 201
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