39 research outputs found
Bayesian Image Restoration for Poisson Corrupted Image using a Latent Variational Method with Gaussian MRF
We treat an image restoration problem with a Poisson noise chan- nel using a
Bayesian framework. The Poisson randomness might be appeared in observation of
low contrast object in the field of imaging. The noise observation is often
hard to treat in a theo- retical analysis. In our formulation, we interpret the
observation through the Poisson noise channel as a likelihood, and evaluate the
bound of it with a Gaussian function using a latent variable method. We then
introduce a Gaussian Markov random field (GMRF) as the prior for the Bayesian
approach, and derive the posterior as a Gaussian distribution. The latent
parameters in the likelihood and the hyperparameter in the GMRF prior could be
treated as hid- den parameters, so that, we propose an algorithm to infer them
in the expectation maximization (EM) framework using loopy belief
propagation(LBP). We confirm the ability of our algorithm in the computer
simulation, and compare it with the results of other im- age restoration
frameworks.Comment: 9 pages, 6 figures, The of this manuscript is submitting to the
Information Processing Society of Japan(IPSJ), Transactions on Mathematical
Modeling and its Applications (TOM
Support Vector Machine Histogram: New Analysis and Architecture Design Method of Deep Convolutional Neural Network
Deep convolutional neural network (DCNN) is a kind of hierarchical neural network models and attracts attention in recent years since it has shown high classification performance. DCNN can acquire the feature representation which is a parameter indicating the feature of the input by learning. However, its internal analysis and the design of the network architecture have many unclear points and it cannot be said that it has been sufficiently elucidated. We propose the novel DCNN analysis method “Support vector machine (SVM) histogram” as a prescription to deal with these problems. This is a method that examines the spatial distribution of DCNN extracted feature representation by using the decision boundary of linear SVM. We show that we can interpret DCNN hierarchical processing using this method. In addition, by using the result of SVM histogram, DCNN architecture design becomes possible. In this study, we designed the architecture of the application to large scale natural image dataset. In the result, we succeeded in showing higher accuracy than the original DCNN
B-DCGAN:Evaluation of Binarized DCGAN for FPGA
We are trying to implement deep neural networks in the edge computing
environment for real-world applications such as the IoT(Internet of Things),
the FinTech etc., for the purpose of utilizing the significant achievement of
Deep Learning in recent years. Especially, we now focus algorithm
implementation on FPGA, because FPGA is one of the promising devices for
low-cost and low-power implementation of the edge computer. In this work, we
introduce Binary-DCGAN(B-DCGAN) - Deep Convolutional GAN model with binary
weights and activations, and with using integer-valued operations in forward
pass(train-time and run-time). And we show how to implement B-DCGAN on
FPGA(Xilinx Zynq). Using the B-DCGAN, we do feasibility study of FPGA's
characteristic and performance for Deep Learning. Because the binarization and
using integer-valued operation reduce the memory capacity and the number of the
circuit gates, it is very effective for FPGA implementation. On the other hand,
the quality of generated data from the model will be decreased by these
reductions. So we investigate the influence of these reductions.Comment: 10 page
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Transfer learning is a machine learning technique designed to improve
generalization performance by using pre-trained parameters obtained from other
learning tasks. For image recognition tasks, many previous studies have
reported that, when transfer learning is applied to deep neural networks,
performance improves, despite having limited training data. This paper proposes
a two-stage feature transfer learning method focusing on the recognition of
textural medical images. During the proposed method, a model is successively
trained with massive amounts of natural images, some textural images, and the
target images. We applied this method to the classification task of textural
X-ray computed tomography images of diffuse lung diseases. In our experiment,
the two-stage feature transfer achieves the best performance compared to a
from-scratch learning and a conventional single-stage feature transfer. We also
investigated the robustness of the target dataset, based on size. Two-stage
feature transfer shows better robustness than the other two learning methods.
Moreover, we analyzed the feature representations obtained from DLDs imagery
inputs for each feature transfer models using a visualization method. We showed
that the two-stage feature transfer obtains both edge and textural features of
DLDs, which does not occur in conventional single-stage feature transfer
models.Comment: Preprint of the journal article to be published in IPSJ TOM-51.
Notice for the use of this material The copyright of this material is
retained by the Information Processing Society of Japan (IPSJ). This material
is published on this web site with the agreement of the author (s) and the
IPS
A Bayesian Hyperparameter Inference for Radon-Transformed Image Reconstruction
We develop a
hyperparameter inference method for image
reconstruction from Radon transform
which often appears in the computed tomography, in the manner of
Bayesian inference. Hyperparameters are often introduced in
Bayesian inference to control the strength ratio between prior
information and the fidelity to the observation. Since the quality
of the reconstructed image is controlled by the estimation
accuracy of these hyperparameters, we apply Bayesian inference
into the filtered back-projection (FBP) reconstruction method with
hyperparameters inference and demonstrate that the estimated
hyperparameters can adapt to the noise level in the observation
automatically. In the computer simulation, at first, we show that our
algorithm works well in the model framework environment, that
is, observation noise is an additive white Gaussian noise case. Then,
we also show that our algorithm works well in the more realistic
environment, that is, observation noise is Poissonian noise case.
After that, we demonstrate an application for the real chest CT
image reconstruction under the Gaussian and Poissonian observation
noises
Bayesian inference to identify crystalline structures for XRD
Crystalline phase structure is essential for understanding the performance
and properties of a material. Therefore, this study identified and quantified
the crystalline phase structure of a sample based on the diffraction pattern
observed when the crystalline sample was irradiated with electromagnetic waves
such as X-rays. Conventional analysis necessitates experienced and
knowledgeable researchers to shorten the list from many candidate crystalline
phase structures. However, the Conventional diffraction pattern analysis is
highly analyst-dependent and not objective. Additionally, there is no
established method for discussing the confidence intervals of the analysis
results. Thus, this study aimed to establish a method for automatically
inferring crystalline phase structures from diffraction patterns using Bayesian
inference. Our method successfully identified true crystalline phase structures
with a high probability from 50 candidate crystalline phase structures.
Further, the mixing ratios of selected crystalline phase structures were
estimated with a high degree of accuracy. This study provided reasonable
results for well-crystallized samples that clearly identified the crystalline
phase structures