7,705 research outputs found
End-to-End Learned Random Walker for Seeded Image Segmentation
We present an end-to-end learned algorithm for seeded segmentation. Our
method is based on the Random Walker algorithm, where we predict the edge
weights of the underlying graph using a convolutional neural network. This can
be interpreted as learning context-dependent diffusivities for a linear
diffusion process. Besides calculating the exact gradient for optimizing these
diffusivities, we also propose simplifications that sparsely sample the
gradient and still yield competitive results. The proposed method achieves the
currently best results on a seeded version of the CREMI neuron segmentation
challenge
Seeking multi-thresholds for image segmentation with Learning Automata
This paper explores the use of the Learning Automata (LA) algorithm to
compute threshold selection for image segmentation as it is a critical
preprocessing step for image analysis, pattern recognition and computer vision.
LA is a heuristic method which is able to solve complex optimization problems
with interesting results in parameter estimation. Despite other techniques
commonly seek through the parameter map, LA explores in the probability space
providing appropriate convergence properties and robustness. The segmentation
task is therefore considered as an optimization problem and the LA is used to
generate the image multi-threshold separation. In this approach, one 1D
histogram of a given image is approximated through a Gaussian mixture model
whose parameters are calculated using the LA algorithm. Each Gaussian function
approximating the histogram represents a pixel class and therefore a threshold
point. The method shows fast convergence avoiding the typical sensitivity to
initial conditions such as the Expectation Maximization (EM) algorithm or the
complex time-consuming computations commonly found in gradient methods.
Experimental results demonstrate the algorithm ability to perform automatic
multi-threshold selection and show interesting advantages as it is compared to
other algorithms solving the same task.Comment: 22 Pages. arXiv admin note: text overlap with arXiv:1405.722
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images
This paper presents an end-to-end solution for MRI thigh quadriceps
segmentation. This is the first attempt that deep learning methods are used for
the MRI thigh segmentation task. We use the state-of-the-art Fully
Convolutional Networks with transfer learning approach for the semantic
segmentation of regions of interest in MRI thigh scans. To further improve the
performance of the segmentation, we propose a post-processing technique using
basic image processing methods. With our proposed method, we have established a
new benchmark for MRI thigh quadriceps segmentation with mean Jaccard
Similarity Index of 0.9502 and processing time of 0.117 second per image.Comment: 27 pages, 7 figures and 5 table
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
This work proposes a process for efficiently training a point-wise object
detector that enables localizing objects and computing their 6D poses in
cluttered and occluded scenes. Accurate pose estimation is typically a
requirement for robust robotic grasping and manipulation of objects placed in
cluttered, tight environments, such as a shelf with multiple objects. To
minimize the human labor required for annotation, the proposed object detector
is first trained in simulation by using automatically annotated synthetic
images. We then show that the performance of the detector can be substantially
improved by using a small set of weakly annotated real images, where a human
provides only a list of objects present in each image without indicating the
location of the objects. To close the gap between real and synthetic images, we
adopt a domain adaptation approach through adversarial training. The detector
resulting from this training process can be used to localize objects by using
its per-object activation maps. In this work, we use the activation maps to
guide the search of 6D poses of objects. Our proposed approach is evaluated on
several publicly available datasets for pose estimation. We also evaluated our
model on classification and localization in unsupervised and semi-supervised
settings. The results clearly indicate that this approach could provide an
efficient way toward fully automating the training process of computer vision
models used in robotics
A survey of non-exchangeable priors for Bayesian nonparametric models
Dependent nonparametric processes extend distributions over measures, such as
the Dirichlet process and the beta process, to give distributions over
collections of measures, typically indexed by values in some covariate space.
Such models are appropriate priors when exchangeability assumptions do not
hold, and instead we want our model to vary fluidly with some set of
covariates. Since the concept of dependent nonparametric processes was
formalized by MacEachern [1], there have been a number of models proposed and
used in the statistics and machine learning literatures. Many of these models
exhibit underlying similarities, an understanding of which, we hope, will help
in selecting an appropriate prior, developing new models, and leveraging
inference techniques
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
Prostate cancer is the most diagnosed form of cancer in Canadian men, and is
the third leading cause of cancer death. Despite these statistics, prognosis is
relatively good with a sufficiently early diagnosis, making fast and reliable
prostate cancer detection crucial. As imaging-based prostate cancer screening,
such as magnetic resonance imaging (MRI), requires an experienced medical
professional to extensively review the data and perform a diagnosis,
radiomics-driven methods help streamline the process and has the potential to
significantly improve diagnostic accuracy and efficiency, and thus improving
patient survival rates. These radiomics-driven methods currently rely on
hand-crafted sets of quantitative imaging-based features, which are selected
manually and can limit their ability to fully characterize unique prostate
cancer tumour phenotype. In this study, we propose a novel \textit{discovery
radiomics} framework for generating custom radiomic sequences tailored for
prostate cancer detection. Discovery radiomics aims to uncover abstract
imaging-based features that capture highly unique tumour traits and
characteristics beyond what can be captured using predefined feature models. In
this paper, we discover new custom radiomic sequencers for generating new
prostate radiomic sequences using multi-parametric MRI data. We evaluated the
performance of the discovered radiomic sequencer against a state-of-the-art
hand-crafted radiomic sequencer for computer-aided prostate cancer detection
with a feedforward neural network using real clinical prostate multi-parametric
MRI data. Results for the discovered radiomic sequencer demonstrate good
performance in prostate cancer detection and clinical decision support relative
to the hand-crafted radiomic sequencer. The use of discovery radiomics shows
potential for more efficient and reliable automatic prostate cancer detection.Comment: 8 page
Probabilistic tractography, Path Integrals and the Fokker Planck equation
Probabilistic tractography based on diffusion weighted MRI has become a
powerful approach for quantifying structural brain connectivities. In several
works the similarity of probabilistic tractography and path integrals was
already pointed out. This work investigates this connection more closely. For
the so called Wiener process, a Gaussian random walker, the equivalence is
worked out. We identify the source of the asymmetry of usual random walkers
approaches and show that there is a proper symmetrization, which leads to a new
symmetric connectivity measure. To compute this measure we will use the
Fokker-Planck equation, which is an equivalent representation of a Wiener
process in terms of a partial differential equation. In experiments we show
that the proposed approach leads a symmetric and robust connectivity measure
Analytical tools for single-molecule fluorescence imaging in cellulo
Recent technological advances in cutting-edge ultrasensitive fluorescence
microscopy have allowed single-molecule imaging experiments in living cells
across all three domains of life to become commonplace. Single-molecule
live-cell data is typically obtained in a low signal-to-noise ratio (SNR)
regime sometimes only marginally in excess of 1, in which a combination of
detector shot noise, sub-optimal probe photophysics, native cell
autofluorescence and intrinsically underlying stochastic of molecules result in
highly noisy datasets for which underlying true molecular behaviour is
non-trivial to discern. The ability to elucidate real molecular phenomena is
essential in relating experimental single-molecule observations to both the
biological system under study as well as offering insight into the fine details
of the physical and chemical environments of the living cell. To confront this
problem of faithful signal extraction and analysis in a noise-dominated regime,
the needle in a haystack challenge, such experiments benefit enormously from a
suite of objective, automated, high-throughput analysis tools that can home in
on the underlying molecular signature and generate meaningful statistics across
a large population of individual cells and molecules. Here, I discuss the
development and application of several analytical methods applied to real case
studies, including objective methods of segmenting cellular images from light
microscopy data, tools to robustly localize and track single
fluorescently-labelled molecules, algorithms to objectively interpret molecular
mobility, analysis protocols to reliably estimate molecular stoichiometry and
turnover, and methods to objectively render distributions of molecular
parameter
A dense subgraph based algorithm for compact salient image region detection
We present an algorithm for graph based saliency computation that utilizes
the underlying dense subgraphs in finding visually salient regions in an image.
To compute the salient regions, the model first obtains a saliency map using
random walks on a Markov chain. Next, k-dense subgraphs are detected to further
enhance the salient regions in the image. Dense subgraphs convey more
information about local graph structure than simple centrality measures. To
generate the Markov chain, intensity and color features of an image in addition
to region compactness is used. For evaluating the proposed model, we do
extensive experiments on benchmark image data sets. The proposed method
performs comparable to well-known algorithms in salient region detection.Comment: 33 pages, 18 figures, Single column manuscript pre-print, Accepted at
Computer Vision and Image Understanding, Elsevie
RANSAC: Identification of Higher-Order Geometric Features and Applications in Humanoid Robot Soccer
The ability for an autonomous agent to self-localise is directly proportional
to the accuracy and precision with which it can perceive salient features
within its local environment. The identification of such features by
recognising geometric profile allows robustness against lighting variations,
which is necessary in most industrial robotics applications. This paper details
a framework by which the random sample consensus (RANSAC) algorithm, often
applied to parameter fitting in linear models, can be extended to identify
higher-order geometric features. Goalpost identification within humanoid robot
soccer is investigated as an application, with the developed system yielding an
order-of-magnitude improvement in classification performance relative to a
traditional histogramming methodology.Comment: 8 pages, 6 figure
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