Anisotropic Guided Filtering.

The guided filter and its subsequent derivatives have been widely employed in many image processing and computer vision applications primarily brought about by their low complexity and good edge-preservation properties. Despite this success, the different variants of the guided filter are unable to handle more aggressive filtering strengths leading to the manifestation of "detail halos". At the same time, these existing filters perform poorly when the input and guide images have structural inconsistencies. In this paper, we demonstrate that these limitations are due to the guided filter operating as a variable-strength locally-isotropic filter that, in effect, acts as a weak anisotropic filter on the image. Our analysis shows that this behaviour stems from the use of unweighted averaging in the final steps of guided filter variants including the adaptive guided filter (AGF), weighted guided image filter (WGIF), and gradient-domain guided image filter (GGIF). We propose a novel filter, the Anisotropic Guided Filter (AnisGF), that utilises weighted averaging to achieve maximum diffusion while preserving strong edges in the image. The proposed weights are optimised based on the local neighbourhood variances to achieve strong anisotropic filtering while preserving the low computational cost of the original guided filter. Synthetic tests show that the proposed method addresses the presence of detail halos and the handling of inconsistent structures found in previous variants of the guided filter. Furthermore, experiments in scale-aware filtering, detail enhancement, texture removal, and chroma upsampling demonstrate the improvements brought about by the technique.

Organic π-type thermoelectric module supported by photolithographic mold: a working hypothesis of sticky thermoelectric materials.

To examine the potential of organic thermoelectrics (TEs) for energy harvesting, we fabricated an organic TE module to achieve 250 mV in the open-circuit voltage which is sufficient to drive a commercially available booster circuit designed for energy harvesting usage. We chose the π-type module structure to maintain the temperature differences in organic TE legs, and then optimized the p- and n-type TE materials' properties. After injecting the p- and n-type TE materials into photolithographic mold, we eventually achieved 250 mV in the open-circuit voltage by a method to form the upper electrodes. However, we faced a difficulty to reduce the contact resistance in this material system. We conclude that TE materials must be inversely designed from the viewpoints of the expected module structures and mass-production processes, especially for the purpose of energy harvesting.

A quadratically constrained MAP classifier using the mixture of Gaussians models as a weight function.

In this paper, we propose classifiers derived from quadratically constrained maximum a posteriori (QCMAP) estimation. The QCMAP consists of the maximization of the expectation of a cost function, which is derived from the maximum a posteriori probability and a quadratic constraint. This criterion is highly general since its forms include least squares regressions and a support vector machine. Furthermore, the criterion provides a novel classifier, the "Gaussian QCMAP." The QCMAP procedure still has large theoretical interest and its full extensibility has yet to be explored. In this paper, we propose using the mixture of Gaussian distributions as the QCMAP weight function. The mixture of Gaussian distributions has wide-ranging applicability, and encompasses forms, such as a normal distribution model and a kernel density model. We propose four types of mixture of Gaussian functions for QCMAP classifiers, and conduct experiments to demonstrate their advantages.

A new approach to a maximum à posteriori-based kernel classification method.

This paper presents a new approach to a maximum a posteriori (MAP)-based classification, specifically, MAP-based kernel classification trained by linear programming (MAPLP). Unlike traditional MAP-based classifiers, MAPLP does not directly estimate a posterior probability for classification. Instead, it introduces a kernelized function to an objective function that behaves similarly to a MAP-based classifier. To evaluate the performance of MAPLP, a binary classification experiment was performed with 13 datasets. The results of this experiment are compared with those coming from conventional MAP-based kernel classifiers and also from other state-of-the-art classification methods. It shows that MAPLP performs promisingly against the other classification methods. It is argued that the proposed approach makes a significant contribution to MAP-based classification research; the approach widens the freedom to choose an objective function, it is not constrained to the strict sense Bayesian, and can be solved by linear programming. A substantial advantage of our proposed approach is that the objective function is undemanding, having only a single parameter. This simplicity, thus, allows for further research development in the future.

Kernel Wiener filter and its application to pattern recognition.

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.

Blind extraction of global signal from multi-channel noisy observations.

We propose a novel efficient method of blind signal extraction from multi-sensor networks when each observed signal consists of one global signal and local uncorrelated signals. Most of existing blind signal separation and extraction methods such as independent component analysis have constraints such as statistical independence, non-Gaussianity, and underdetermination, and they are not suitable for global signal extraction problem from noisy observations. We developed an estimation algorithm based on alternating iteration and the smart weighted averaging. The proposed method does not have strong assumptions such as independence or non-Gaussianity. Experimental results using a musical signal and a real electroencephalogram demonstrate the advantage of the proposed method.

Extraction of steady state visually evoked potential signal and estimation of distribution map from EEG data.

We propose a signal extraction method from multi-channel EEG signals and apply to extract Steady State Visually Evoked Potential (SSVEP) signal. SSVEP is a response to visual stimuli presented in the form of flushing patterns. By using several flushing patterns with different frequency, brain machine (computer) interface (BMI/BCI) can be realized. Therefore it is important to extract SSVEP signals from multi-channel EEG signals. At first, we estimate the power of the objective signal in each electrode. Estimation of the power is helpful in not only extraction of the signal but also drawing a distribution map of the signal, finding electrodes which have large SNR, and ranking electrodes in sort of information with respect to the power of the signal. Experimental results show that the proposed method 1) estimates more accurate power than existing methods, 2) estimates the global signal which has larger SNR than existing methods, and 3) allows us to draw a distribution map of the signal, and it conforms the biological theory.

Variable-length lapped transforms with a combination of multiple synthesis filter banks for image coding.

A class of lapped transforms for image coding, which are characterized by variable-length synthesis filters, is introduced. In this class, the synthesis filter bank (FB) is first defined with an arbitrary combination of finite impulse response synthesis filters of perfect reconstruction FBs. An analysis FB is then obtained using direct matrix inversion or iterative implementation of Neumann series expansion. Moreover, to improve compression, we introduce a unitary transform that follows the analysis FB. This class enables a greater freedom of design than previously presented variable-length lapped transforms. We illustrate several design examples and present experimental results for image coding, which indicate that the proposed transforms are promising and comparable with conventional subband transforms including wavelets.