• Communications for Statistical Applications and Methods
• /
• v.4 no.3
• /
• pp.817-825
• /
• 1997

Images reconstructed with EM algorithm have been observed to have checkerboard effects and have large distortions near edges as iterations proceed. We suggest a aimple algorithm of applying line process to the EM and Bayesian EM to reduce the distortions near edges. We show by simulation that this algorithm improves the clarity of the reconstructed image and has good properties based on root mean square error.

• The Journal of the Acoustical Society of Korea
• /
• v.25 no.2
• /
• pp.78-87
• /
• 2006

In this paper. we propose an effective mask estimation scheme for missing-feature reconstruction in order to achieve robust speech recognition under unknown noise environments. In the previous work. colored noise is used for training the mask classifer, which is generated from the entire frequency Partitioned signals. However it gives a limited performance under the restricted number of training database. To reflect the spectral events of more various background noise and improve the performance simultaneously. a new Bayesian classifier for mask estimation is proposed, which works independent of other frequency bands. In the proposed method, we employ the colored noise which is obtained by combining colored noises generated from each frequency band in order to reflect more various noise environments and mitigate the 'sparse' database problem. Combined with the cluster-based missing-feature reconstruction. the performance of the proposed method is evaluated on a task of noisy speech recognition. The results show that the proposed method has improved performance compared to the Previous method under white noise. car noise and background music conditions.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.180-187
• /
• 2021

In this paper, we present a computational volumetric reconstruction method for three-dimensional (3D) photon counting imaging with enhanced visual quality when low-resolution elemental images are used under photon-starved conditions. In conventional photon counting imaging with low-resolution elemental images, it may be difficult to estimate the 3D scene correctly because of a lack of scene information. In addition, the reconstructed 3D images may be blurred because volumetric computational reconstruction has an averaging effect. In contrast, with our method, the pixels of the elemental image rearrangement technique and a Bayesian approach are used as the reconstruction and estimation methods, respectively. Therefore, our method can enhance the visual quality and estimation accuracy of the reconstructed 3D images because it does not have an averaging effect and uses prior information about the 3D scene. To validate our technique, we performed optical experiments and demonstrated the reconstruction results.

• IEIE Transactions on Smart Processing and Computing
• /
• v.2 no.2
• /
• pp.97-101
• /
• 2013

Bit-depth expansion is a process of enhancing the image quality by increasing the number of intensity levels. To solve this problem, a hybrid method is proposed, where the pixels are categorized into smooth and complex regions, and are processed using different methods. The pixels in the smooth region are reconstructed with a smooth prior, and a Bayesian estimator is used for the pixels in the complex region. The proposed method effectively removes the false contour artifacts while requiring less computation than conventional methods. In addition, the method shows good quantitative performance, and the PSNR gains over the best existing method are 1.45 dB and 0.26 dB for 4 bits and 3 bits expansion cases, respectively.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.361-364
• /
• 1997

Bayesian MAP-EM approaches have been quite useful or tomographic reconstruction in that they can stabilize the instability of well-known ML-EM approaches, and can incorporate a priori information on the underlying emission object. However, MAP reconstruction algorithms with expressive priors often suffer from the optimization problem when their objective unctions are nonquadratic. In our previous work [1], we showed that the use of deterministic annealing method greatly reduces computational burden or optimization and provides a good solution or nonquadratic objective unctions. Here, we further investigate the convergence of the deterministic annealing algorithm; our experimental results show that, while the solutions obtained by a simple quenching algorithm depend on the initial conditions, the estimates converged via deterministic annealing algorithm are consistent under various initial conditions.

• The Korean Journal of Applied Statistics
• /
• v.35 no.5
• /
• pp.603-617
• /
• 2022

Following the September 11, 2001 terrorist attacks, the United States declared war on terror and invaded Afghanistan and Iraq, winning quickly. However, interest in analyzing terrorist activities has developed as a result of a significant amount of time being spent on the post-war stabilization effort, which failed to minimize the number of terrorist activities that occurred later. Based on terrorist data from 2003 to 2010, this study utilized a Bayesian hierarchical model to forecast the terrorist threat in 2011. The model depicts spatiotemporal dependence with predictors such as population and religion by autonomous district. The military commander in charge of the region can utilize the forecast value based on the our model to prevent terrorism by deploying forces efficiently.

• Korean Journal of Remote Sensing
• /
• v.24 no.1
• /
• pp.79-89
• /
• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• Molecules and Cells
• /
• v.23 no.1
• /
• pp.64-71
• /
• 2007

To investigate the phylogeny of Patellogastropoda, the complete 18S rDNA sequences of nine patellogastropod limpets Cymbula canescens (Gmelin, 1791), Helcion dunkeri (Krauss, 1848), Patella rustica Linnaeus, 1758, Cellana toreuma (Reeve, 1855), Cellana nigrolineata (Reeve, 1854), Nacella magellanica Gmelin, 1791, Nipponacmea concinna (Lischke, 1870), Niveotectura pallida (Gould, 1859), and Lottia dorsuosa Gould, 1859 were determined. These sequences were then analyzed along with the published 18S rDNA sequences of 35 gastropods, one bivalve, and one chiton species. Phylogenetic trees were constructed by maximum parsimony, maximum likelihood, and Bayesian inference. The results of our 18S rDNA sequence analysis strongly support the monophyly of Patellogastropoda and the existence of three subgroups. Of these, two subgroups, the Patelloidea and Acmaeoidea, are closely related, with branching patterns that can be summarized as [(Cymbula + Helcion) + Patella] and [(Nipponacmea + Lottia) + Niveotectura]. The remaining subgroup, Nacelloidea, emerges as basal and paraphyletic, while its genus Cellana is monophyletic. Our analysis also indicates that the Patellogastropoda have a sister relationship with the order Cocculiniformia within the Gastropoda.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.2
• /
• pp.294-302
• /
• 2008

In this paper, we present a new image up-sampling method which registers low resolution images to the high resolution grid when Bayesian super-resolution image processing is performed. The proposed up-sampling method interpolates high-resolution pixels using high-frequency data lying in all the low resolution images, instead of up-sampling each low resolution image separately. The interpolation is based on B-spline non-uniform re-sampling, adjusted for the super-resolution image processing. The experimental results demonstrate the effects when different up-sampling methods generally used such as zero-padding or bilinear interpolation are applied to the super-resolution image reconstruction. Then, we show that the proposed hybird up-sampling method generates high-resolution images more accurately than conventional methods with quantitative and qualitative assess measures.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.270-273
• /
• 2007

In this paper, a SAR-despeck1ing approach of adaptive iteration based a Bayesian model using the lognormal distribution for image intensity and a Gibbs random field (GRF) for image texture is proposed for noise removal of the images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. The iterative approach based on MRF is very effective for the inner areas of regions in the observed scene, but may result in yielding false reconstruction around the boundaries due to using wrong information of adjacent regions with different characteristics. The proposed method suggests an adaptive approach using variable parameters depending on the location of reconstructed area, that is, how near to the boundary. The proximity of boundary is estimated by the statistics based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution.