13,163 research outputs found
Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs
In this work we propose a structured prediction technique that combines the
virtues of Gaussian Conditional Random Fields (G-CRF) with Deep Learning: (a)
our structured prediction task has a unique global optimum that is obtained
exactly from the solution of a linear system (b) the gradients of our model
parameters are analytically computed using closed form expressions, in contrast
to the memory-demanding contemporary deep structured prediction approaches that
rely on back-propagation-through-time, (c) our pairwise terms do not have to be
simple hand-crafted expressions, as in the line of works building on the
DenseCRF, but can rather be `discovered' from data through deep architectures,
and (d) out system can trained in an end-to-end manner. Building on standard
tools from numerical analysis we develop very efficient algorithms for
inference and learning, as well as a customized technique adapted to the
semantic segmentation task. This efficiency allows us to explore more
sophisticated architectures for structured prediction in deep learning: we
introduce multi-resolution architectures to couple information across scales in
a joint optimization framework, yielding systematic improvements. We
demonstrate the utility of our approach on the challenging VOC PASCAL 2012
image segmentation benchmark, showing substantial improvements over strong
baselines. We make all of our code and experiments available at
{https://github.com/siddharthachandra/gcrf}Comment: Our code is available at https://github.com/siddharthachandra/gcr
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
Brain Tumor Vascular Network Segmentation from Micro-Tomography
Micro-tomography produces high resolution images of bio- logical structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a condi- tional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus character- ized by their respective distribution of watershed region size interpreted as local vascular territories
A spatially distributed model for foreground segmentation
Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models.
Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications
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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