Nozomu Hamada

Data-dependent weighted median filtering with robust motion information for image sequence restoration

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mined noise). In general, for the image sequence filtering, motion compensation (MC) method is required in order to obtain good filtering performance both in the still and moving regions of an image sequence. Nevertheless a heavy computation load is imposed on MC method and MC tends to get mistaken motion vector owing to additive noise. To overcome above drawbacks of MC, we have proposed a Video-Data Dependent Weighted Average (Video-DDWA) filter for image sequence restoration degraded by additive Gaussian noise. The Video-DDWA filter whose weights are controlled by some local information contain a motion information as a motion detector is shown that the motion information method is more effective tool than MC method for image sequence restoration. However Video-DDWA filter is not proper for removing the mixed noise. Therefore, we replace weighted average filters and a motion information of the Video-DDWA with weighted median filters and a mixed noise robust motion information, respectively. We propose this filter as a Video-Data Dependent Weighted Median (Video-DDWM) filter for removing mixed noise from image sequence. Through some simulations, the Video-DDWM filter is proven to be more effective both the restoration results and computation time than the 3D-DDWM filter with impulse robust MC for removing mixed noise from image sequence.

Feature edge extraction from 3D triangular meshes using a thinning algorithm

Highly detailed geometric models, which are represented as dense triangular meshes are becoming popular in computer graphics. Since such 3D meshes often have huge information, we require some methods to treat them efficiently in the 3D mesh processing such as, surface simplification, subdivision surface, curved surface approximation and morphing. In these applications, we often extract features of 3D meshes such as feature vertices and feature edges in preprocessing step. An automatic extraction method of feature edges is treated in this study. In order to realize the feature edge extraction method, we first introduce the concavity and convexity evaluation value. Then the histogram of the concavity and convexity evaluation value is used to separate the feature edge region. We apply a thinning algorithm, which is used in 2D binary image processing. It is shown that the proposed method can extract appropriate feature edges from 3D meshes.

A state-space realization for transfer functions

This paper presents a new approach to the realization of rational transfer functions via state-variable methods. The resulting structures use an RC ladder network, n voltage followers, and two summers with appropriate scalings, where n is the degree of the characteristic polynomial of the given transfer function. The nonsingular transformation matrix Q that relates two state-space realizations plays an important role in our approach.

Multiple sources' direction finding by using reliable component on phase difference manifold and kernel density estimator

This paper proposes a novel direction-of-arrival estimation method in a general 3-dimensional array configuration for multiple speech signals uttered simultaneously. The method is based on sparseness in the time-frequency representation of speech signal and is applicable to an underdetermined case where the sources outnumber sensors. At first, we introduce a parameterized closed surface to which we refer the phase difference manifold. This is defined in the space of phase difference vectors between sensors in order to provide the one-to-one correspondence between the induced phase difference on this sphere and a propagating direction vector of the source. Instead using the conventional pseudo-inverse mapping algorithm, the selection of phase difference vectors located or closely located on the phase difference manifold as a set of reliable observations. Finally, the authors method utilizing kernel density algorithm is generalized for arbitrary array sensors case. The conducted experiments demonstrate that the method utilizing the reliable cell selection and the kernel density estimator with appropriate bandwidth determination performed effectively.

Traffic sign recognition by Bags of features

Road sign recognition method has been studied for realizing drivers assisting system, and various methods have been developed. Road sign alters its shape and color depending on its relative location against camera and surrounding condition such as weather and daytime. The object detection method utilized Scale Invariant Feature Transform (SIFT) is used in this study. Unless its advantage in the robustness property, calculation costs both for detecting SIFT and matching with database are usually expensive. In this paper, region extraction is performed for reducing SIFT detection cost, and the Bags of Features method is applied for traffic sign recognition. At the recognition phase, support vector machine (SVM) approach is used. Through several experimental results, lower calculation cost and higher accuracy rate (97.5 %) are observed.

ICA-Based Separation and DOA Estimation of Analog Modulated Signals in Multipath Environment

The blind separation problem of analog modulated radio signals and their DOA (Direction Of Arrival) estimation problem are considered. These problems are very important in radio surveillance. Based on the idea of Carlos and Takada [1], the following two methods are proposed for an Independent Component Analysis (ICA) based radio surveillance system. The first method is concerned with the improvement of DOA estimation accuracy after signal separation by ICA. Another method treats separation and DOA estimation in multipath environment. The effectiveness of the proposed methods is proved by computer simulation.

Detection of moving objects using observer motion‐based optical flow estimation

A scheme for detecting a moving object in a three-dimensional environment from observed dynamic images by optical flow, based on the state of the motion of the observing system, is proposed in this paper. The usual optical flow constraint equations defined in an image coordinate system do not sufficiently satisfy the assumptions made in deriving them when the observing system is in motion. In this paper, optical flow constraint equations considering the motion of the observing system are first derived. In order to do this, a mapping converting the motion of a stationary environment image to linear trajectory signals is derived. The uniform velocity property of motion and the isotropic property of motion within a proximal area, which are basic assumptions of the block gradient method, can be satisfied by these. Next, a method of expressing the optical flow constraint equations after mapping by the gradient in the time dimension before mapping is presented. Finally, the residuals of the optical flow constraint equations are proposed as the evaluation quantity for the extraction of a moving object and their efficacy is shown.

Adaptive weighted median filter utilizing impulsive noise detection

The removal of noise in image is one of the current important issues. It is also useful as a preprocessing for edge detection, motion estimation and so on. In this paper, an adaptive weighted median filter utilizing impulsive noise detection is proposed for the removal of impulsive noise in digital images. The aim of our proposed method is to eliminate impulsive noise effectively preserving original fine detail in images. This aim is same for another median-type nonlinear filters try to realized. In our method, we use weighted median filter whose weights should be determined by balancing between the signal preserving ability and noise reduction performance. The trade off between these two inconsistent properties is realized using the noise detection mechanism and optimized adaptation process. In the previous work, threshold value between the signal and the output of the median filter have to be decided for the noise detection. Adaptive algorithm for optimizing WM filters uses the teacher image for training process. In our method, following two new approaches are introduced in the filtering. (1) The noise detection process uses the discriminant method to the histogram distribution of the derivation from median filter output. (2) Filter weights which have been learned by uncorrupted pixels and their neighborhood without the original image are used for the restoration filtering for noise corrupted pixels. The validity of the proposed method is shown through some experimental results.

Asymmetric half-plane lattice modeling based on 2-D Levinson algorithm

This brief proposes a two-dimensional (2-D) Levinson algorithm and a lattice filter for the general case of the Autoregressive (AR) model with an asymmetric half-plane (AHP) support. The resulting Levinson algorithm and corresponding lattice filter solve the 2-D normal equation recursively, Although the 2-D signals of the model support are ordered into a one-dimensional (1-D) array, the ordering of the support signal is assigned voluntarily. The effects on the resulting model caused by different choices of support signal ordering are discussed. Finally, the validity of the proposed theory is confirmed through various simulations.

Mouth motion analysis with space-time interest points

Speech recognition and speaker detection technique from audio visual fusion information attract much attention. In the visual side information, namely lip reading area, most of recent studies are based on analyzing shape of mouth, whereas few studies are based on analyzing lip motion. However, analysis associated with mouth motion gives essential cues for obtaining utterance mechanics. Thus, as a tool to analyze mouth motion, we focus attention on space-time interest points (STIP) that have been effectively applied for analyzing gait and for recognizing human action. This study stems from the idea that the STIP must be also useful for mouth motion analysis. The proposed mouth motion analysis system using STIP needs neither contour estimation nor feature tracking. Then, several image processings are proposed in order to appropriately apply STIP to mouth motion. Additionally, to evaluate the detected STIP as a tool for mouth motion analysis, we classified Japanese vowels utterances into three motion types by using detected STIPs.