2,971 research outputs found
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
Image registration, the process of aligning two or more images, is the core
technique of many (semi-)automatic medical image analysis tasks. Recent studies
have shown that deep learning methods, notably convolutional neural networks
(ConvNets), can be used for image registration. Thus far training of ConvNets
for registration was supervised using predefined example registrations.
However, obtaining example registrations is not trivial. To circumvent the need
for predefined examples, and thereby to increase convenience of training
ConvNets for image registration, we propose the Deep Learning Image
Registration (DLIR) framework for \textit{unsupervised} affine and deformable
image registration. In the DLIR framework ConvNets are trained for image
registration by exploiting image similarity analogous to conventional
intensity-based image registration. After a ConvNet has been trained with the
DLIR framework, it can be used to register pairs of unseen images in one shot.
We propose flexible ConvNets designs for affine image registration and for
deformable image registration. By stacking multiple of these ConvNets into a
larger architecture, we are able to perform coarse-to-fine image registration.
We show for registration of cardiac cine MRI and registration of chest CT that
performance of the DLIR framework is comparable to conventional image
registration while being several orders of magnitude faster.Comment: Accepted: Medical Image Analysis - Elsevie
Convolutional Kernel Networks
An important goal in visual recognition is to devise image representations
that are invariant to particular transformations. In this paper, we address
this goal with a new type of convolutional neural network (CNN) whose
invariance is encoded by a reproducing kernel. Unlike traditional approaches
where neural networks are learned either to represent data or for solving a
classification task, our network learns to approximate the kernel feature map
on training data. Such an approach enjoys several benefits over classical ones.
First, by teaching CNNs to be invariant, we obtain simple network architectures
that achieve a similar accuracy to more complex ones, while being easy to train
and robust to overfitting. Second, we bridge a gap between the neural network
literature and kernels, which are natural tools to model invariance. We
evaluate our methodology on visual recognition tasks where CNNs have proven to
perform well, e.g., digit recognition with the MNIST dataset, and the more
challenging CIFAR-10 and STL-10 datasets, where our accuracy is competitive
with the state of the art.Comment: appears in Advances in Neural Information Processing Systems (NIPS),
Dec 2014, Montreal, Canada, http://nips.c
Comparisons of the execution times and memory requirements for high-speed discrete fourier transforms and fast fourier transforms, for the measurement of AC power harmonics
Conventional wisdom dictates that a Fast Fourier Transform (FFT) will be a more computationally effective method for measuring multiple harmonics than a Discrete Fourier Transform (DFT) approach. However, in this paper it is shown that carefully coded discrete transforms which distribute their computational load over many frames can be made to produce results in shorter execution times than the FFT approach, even for large number of harmonic measurement frequencies. This is because the execution time of the presented DFT actually rises with N and not the classical N2 value, while the execution time of the FFT rises with Nlog2N
Gabor Barcodes for Medical Image Retrieval
In recent years, advances in medical imaging have led to the emergence of
massive databases, containing images from a diverse range of modalities. This
has significantly heightened the need for automated annotation of the images on
one side, and fast and memory-efficient content-based image retrieval systems
on the other side. Binary descriptors have recently gained more attention as a
potential vehicle to achieve these goals. One of the recently introduced binary
descriptors for tagging of medical images are Radon barcodes (RBCs) that are
driven from Radon transform via local thresholding. Gabor transform is also a
powerful transform to extract texture-based information. Gabor features have
exhibited robustness against rotation, scale, and also photometric
disturbances, such as illumination changes and image noise in many
applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework
for the image annotation. To find the most discriminative GBC for a given query
image, the effects of employing Gabor filters with different parameters, i.e.,
different sets of scales and orientations, are investigated, resulting in
different barcode lengths and retrieval performances. The proposed method has
been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray
images for indexing, and 1,733 x-rays images for testing. A total error score
as low as ( accuracy for the first hit) was achieved.Comment: To appear in proceedings of The 2016 IEEE International Conference on
Image Processing (ICIP 2016), Sep 25-28, 2016, Phoenix, Arizona, US
Advanced Algorithms for Satellite Communication Signal Processing
Dizertační práce je zaměřena na softwarově definované přijímače určené k úzkopásmové družicové komunikaci. Komunikační kanály družicových spojů zahrnujících komunikaci s hlubokým vesmírem jsou zatíženy vysokými úrovněmi šumu, typicky modelovaného AWGN, a silným Dopplerovým posuvem signálu způsobeným mimořádnou rychlostí pohybu objektu. Dizertační práce představuje možné postupy řešení výpočetně efektivní digitální downkonverze úzkopásmových signálů a systému odhadu kmitočtu nosné úzkopásmových signálů zatížených Dopplerovým posuvem v řádu násobků šířky pásma signálu. Popis navrhovaných algoritmů zahrnuje analytický postup jejich vývoje a tam, kde je to možné, i analytické hodnocení jejich chování. Algoritmy jsou modelovány v prostředí MATLAB Simulink a tyto modely jsou využity pro ověření vlastností simulacemi. Modely byly také využity k experimentálním testům na reálném signálu přijatém z družice PSAT v laboratoři experimentálních družic na ústavu radioelektroniky.The dissertation is focused on software defined receivers intended for narrowband satellite communication. The satellite communication channel including deep space communication suffers from a high level of noise, typically modeled by AWGN, and from a strong Doppler shift of a signal caused by the unprecedented speed of an object in motion. The dissertation shows possible approaches to the issues of computationally efficient digital downconversion of narrowband signals and the carrier frequency estimation of narrowband signals distorted by the Doppler shift in the order of multiples of the signal bandwidth. The description of the proposed algorithms includes an analytical approach of its development and, if possible, the analytical performance assessment. The algorithms are modeled in MATLAB Simulink and the models are used for validating the performance by the simulation. The models were also used for experimental tests on the real signal received from the PSAT satellite at the laboratory of experimental satellites at the department of radio electronics.
- …