12,828 research outputs found
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks
We present a fast algorithm for training MaxPooling Convolutional Networks to
segment images. This type of network yields record-breaking performance in a
variety of tasks, but is normally trained on a computationally expensive
patch-by-patch basis. Our new method processes each training image in a single
pass, which is vastly more efficient.
We validate the approach in different scenarios and report a 1500-fold
speed-up. In an application to automated steel defect detection and
segmentation, we obtain excellent performance with short training times
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are
two widely used tools for visual recognition. They are typically viewed as
distinct approaches: DPMs are graphical models (Markov random fields), while
CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM
can be formulated as a CNN, thus providing a novel synthesis of the two ideas.
Our construction involves unrolling the DPM inference algorithm and mapping
each step to an equivalent (and at times novel) CNN layer. From this
perspective, it becomes natural to replace the standard image features used in
DPM with a learned feature extractor. We call the resulting model DeepPyramid
DPM and experimentally validate it on PASCAL VOC. DeepPyramid DPM significantly
outperforms DPMs based on histograms of oriented gradients features (HOG) and
slightly outperforms a comparable version of the recently introduced R-CNN
detection system, while running an order of magnitude faster
Multi-directional Geodesic Neural Networks via Equivariant Convolution
We propose a novel approach for performing convolution of signals on curved
surfaces and show its utility in a variety of geometric deep learning
applications. Key to our construction is the notion of directional functions
defined on the surface, which extend the classic real-valued signals and which
can be naturally convolved with with real-valued template functions. As a
result, rather than trying to fix a canonical orientation or only keeping the
maximal response across all alignments of a 2D template at every point of the
surface, as done in previous works, we show how information across all
rotations can be kept across different layers of the neural network. Our
construction, which we call multi-directional geodesic convolution, or
directional convolution for short, allows, in particular, to propagate and
relate directional information across layers and thus different regions on the
shape. We first define directional convolution in the continuous setting, prove
its key properties and then show how it can be implemented in practice, for
shapes represented as triangle meshes. We evaluate directional convolution in a
wide variety of learning scenarios ranging from classification of signals on
surfaces, to shape segmentation and shape matching, where we show a significant
improvement over several baselines
Simplification of the generalized adaptive neural filter and comparative studies with other nonlinear filters
Recently, a new class of adaptive filters called Generalized Adaptive Neural Filters (GANFs) has emerged. They share many characteristics in common with stack filters, include all stack filters as a subset. The GANFs allow a very efficient hardware implementation once they are trained. However, there are some problems associated with GANFs. Three of these arc slow training speeds and the difficulty in choosing a filter structure and neural operator.
This thesis begins with a tutorial on filtering and traces the GANF development up through its origin -- the stack filter. After the GANF is covered in reasonable depth, its use as an image processing filter is examined. Its usefulness is determined based on simulation comparisons with other common filters. Also, some problems of GANFs are looked into. A brief study which investigates different types of neural networks and their applicability to GANFs is presented. Finally, some ideas on increasing the speed of the GANF are discussed. While these improvements do not completely solve the GANF\u27s problems, they make a measurable difference and bring the filter closer to reality
Optimum non linear binary image restoration through linear grey-scale operations
Non-linear image processing operators give excellent results in a number of image processing tasks such as restoration and object recognition. However they are frequently excluded from use in solutions because the system designer does not wish to introduce additional hardware or algorithms and because their design can appear to be ad hoc. In practice the median filter is often used though it is rarely optimal. This paper explains how various non-linear image processing operators may be implemented on a basic linear image processing system using only convolution and thresholding operations. The paper is aimed at image processing system developers wishing to include some non-linear processing operators without introducing additional system capabilities such as extra hardware components or software toolboxes. It may also be of benefit to the interested reader wishing to learn more about non-linear operators and alternative methods of design and implementation. The non-linear tools include various components of mathematical morphology, median and weighted median operators and various order statistic filters. As well as describing novel algorithms for implementation within a linear system the paper also explains how the optimum filter parameters may be estimated for a given image processing task. This novel approach is based on the weight monotonic property and is a direct rather than iterated method
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on
dictio-nary matching to map the temporal MRF signals to quantitative tissue
parameters. Such approaches suffer from inherent discretization errors, as well
as high computational complexity as the dictionary size grows. To alleviate
these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting
approach, referred to as HYDRA.
Methods: HYDRA involves two stages: a model-based signature restoration phase
and a learning-based parameter restoration phase. Signal restoration is
implemented using low-rank based de-aliasing techniques while parameter
restoration is performed using a deep nonlocal residual convolutional neural
network. The designed network is trained on synthesized MRF data simulated with
the Bloch equations and fast imaging with steady state precession (FISP)
sequences. In test mode, it takes a temporal MRF signal as input and produces
the corresponding tissue parameters.
Results: We validated our approach on both synthetic data and anatomical data
generated from a healthy subject. The results demonstrate that, in contrast to
conventional dictionary-matching based MRF techniques, our approach
significantly improves inference speed by eliminating the time-consuming
dictionary matching operation, and alleviates discretization errors by
outputting continuous-valued parameters. We further avoid the need to store a
large dictionary, thus reducing memory requirements.
Conclusions: Our approach demonstrates advantages in terms of inference
speed, accuracy and storage requirements over competing MRF method
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