19 research outputs found
Deformable Registration through Learning of Context-Specific Metric Aggregation
We propose a novel weakly supervised discriminative algorithm for learning
context specific registration metrics as a linear combination of conventional
similarity measures. Conventional metrics have been extensively used over the
past two decades and therefore both their strengths and limitations are known.
The challenge is to find the optimal relative weighting (or parameters) of
different metrics forming the similarity measure of the registration algorithm.
Hand-tuning these parameters would result in sub optimal solutions and quickly
become infeasible as the number of metrics increases. Furthermore, such
hand-crafted combination can only happen at global scale (entire volume) and
therefore will not be able to account for the different tissue properties. We
propose a learning algorithm for estimating these parameters locally,
conditioned to the data semantic classes. The objective function of our
formulation is a special case of non-convex function, difference of convex
function, which we optimize using the concave convex procedure. As a proof of
concept, we show the impact of our approach on three challenging datasets for
different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine
Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201
Geometric Multi-Model Fitting with a Convex Relaxation Algorithm
We propose a novel method to fit and segment multi-structural data via convex
relaxation. Unlike greedy methods --which maximise the number of inliers-- this
approach efficiently searches for a soft assignment of points to models by
minimising the energy of the overall classification. Our approach is similar to
state-of-the-art energy minimisation techniques which use a global energy.
However, we deal with the scaling factor (as the number of models increases) of
the original combinatorial problem by relaxing the solution. This relaxation
brings two advantages: first, by operating in the continuous domain we can
parallelize the calculations. Second, it allows for the use of different
metrics which results in a more general formulation.
We demonstrate the versatility of our technique on two different problems of
estimating structure from images: plane extraction from RGB-D data and
homography estimation from pairs of images. In both cases, we report accurate
results on publicly available datasets, in most of the cases outperforming the
state-of-the-art
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
In this paper, we propose DeepCut, a method to obtain pixelwise object
segmentations given an image dataset labelled with bounding box annotations. It
extends the approach of the well-known GrabCut method to include machine
learning by training a neural network classifier from bounding box annotations.
We formulate the problem as an energy minimisation problem over a
densely-connected conditional random field and iteratively update the training
targets to obtain pixelwise object segmentations. Additionally, we propose
variants of the DeepCut method and compare those to a naive approach to CNN
training under weak supervision. We test its applicability to solve brain and
lung segmentation problems on a challenging fetal magnetic resonance dataset
and obtain encouraging results in terms of accuracy
RSGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems
Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but
power efficiency is hard to achieve with conventional hardware, making the use
of embedded devices such as FPGAs attractive for low-power applications.
However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so
most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA
context, the accuracy of SGM has been improved by More Global Matching (MGM),
which also helps tackle the streaking artifacts that afflict SGM. In this
paper, we propose a novel, resource-efficient method that is inspired by MGM's
techniques for improving depth quality, but which can be implemented to run in
real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI
and Middlebury), we show that in comparison to other real-time capable stereo
approaches, we can achieve a state-of-the-art balance between accuracy, power
efficiency and speed, making our approach highly desirable for use in real-time
systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4
table
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers
We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The
proposed approach, refereed as Inference by Learning or in short as IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by
use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework
constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing
the MRF. We make our code available on-line [4]
Three-dimensional X-ray imaging and analysis of fungi on and in wood
As wood is prone to fungal degradation, fundamental research is necessary to increase our knowledge aiming at product improvement. Several imaging modalities are capable of visualizing fungi, but the X-ray equipment presented in this paper can envisage fungal mycelium in wood non-destructively in three dimensions with sub-micron resolution. Four types of wood subjected to the action of the white rot fungus Coriolus versicolor (Linnaeus) Quélet (CTB 863 A) were scanned using an X-ray based approach. Comparison of wood volumes before and after fungal exposure, segmented manually or semi-automatically, showed the presence of the fungal mass on and in the wood samples and therefore demonstrated the usefulness of computed X-ray tomography for mycological and wood research. Further improvements to the experimental set-up are necessary in order to resolve individual hyphae and enhance segmentation