This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

We propose a novel strategy to obtain a high spatio-temporal resolution video. To this end, we introduce a dual sensor camera that can capture two video sequences with the same field of view simultaneously. These sequences record high resolution with low frame rate and low resolution with high frame rate. This paper presents an algorithm to synthesize a high spatio-temporal resolution video from these two video sequences by using motion compensation and spectral fusion. We confirm that the proposed method improves the resolution and frame rate of the synthesized video.

Kernel-based learning algorithms have been successfully applied in various problem domains, given appropriate kernel functions. In this paper, we discuss the problem of designing kernel functions for binary regression and show that using a bell-shaped cosine function as a kernel function is optimal in some sense. The rationale of this result is based on the Karhunen-Loeve expansion, i.e., the optimal approximation to a set of functions is given by the principal component of the correlation operator of the functions.

Point pattern matching (PPM) arises in areas such as pattern recognition, digital video processing and computer vision. In this study, a novel Genetic Algorithm (GA) based method for matching affine-related point sets is described. Most common techniques for solving the PPM problem, consist in determining the correspondence between points localized spatially within two sets and then find the proper transformation parameters, using a set of equations. In this paper, we use this fact that the correspondence and transformation matrices are two unitary polar factors of Grammian matrices. We estimate one of these factors by the GA's population and then evaluate this estimation by computing an error function using another factor. This approach is an easily implemented one and because of using the GA in it, its computational complexity is lower than other known methods. Simulation results on synthetic and real point patterns with varying amount of noise, confirm that the algorithm is very effective.

The purpose of the study is to obtain the automatic and optimal matching between a motion-measurement device such as a data glove and an output device such as a dexterous robot hand, where there are many differences in the numbers of degree of freedom, sensor and actuator positions, and data format, by means of motion imitation by the humans. Through the algorithm proposed here, a system engineer or user need no labor of determining the values of gains and parameters to be used. In the system, a subject with data glove imitated the same motion with a dexterous robot hand which was moving according to a certain mathematical function. Autoregressive models were adapted to the matching, where each joint angle in the robot and data glove data of the human were used as object and explanatory variables respectively. The partial regression coefficients were estimated by means of singular value decomposition with a system-noise reduction algorithm utilizing statistical properties. The experimental results showed that the robot hand was controlled with high accuracy with small delay, suggesting that the method proposed in this study is proper and easy way and is adaptive to many other systems between a pair of motion-measurement device and output device.

This paper presents a new statistical model-based voice activity detection (VAD) algorithm in the wavelet domain to improve the performance in non-stationary environments. Due to the efficient time-frequency localization and the multi-resolution characteristics of the wavelet representations, the wavelet transforms are quite suitable for processing non-stationary signals such as speech. To utilize the fact that the wavelet packet is very efficient approximation of discrete Fourier transform and has built-in de-noising capability, we first apply wavelet packet decomposition to effectively localize the energy in frequency space, use spectral subtraction, and employ matched filtering to enhance the SNR. Since the conventional wavelet-based spectral subtraction eliminates the low-power speech signal in onset and offset regions and generates musical noise, we derive an improved multi-band spectral subtraction. On the other hand, noticing that fixed threshold cannot follow fluctuations of time varying noise power and the inability to adapt to a time-varying environment severely limits the VAD performance, we propose a statistical model-based VAD algorithm in wavelet domain with an adaptive threshold. We perform extensive computer simulations and compare with the conventional algorithms to demonstrate performance improvement of the proposed algorithm under various noise environments.

An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.

We show that the necessary and sufficient condition for the existence of a balanced C4-trefoil decomposition of the complete multi-graph λKn is λ(n-1) ≡ 0 (mod 24) and n ≤ 10. Decomposition algorithms are also given.

To perform a comparative study, we experimented on two differential epitaxial structures, the conventional metamorphic high-electron-mobility-transistor (MHEMT) using the InAlAs/InGaAs/InAlAs structure and the InP-composite-channel MHEMT adopting the InAlAs/InGaAs/InP structure. Compared with the conventional MHEMT, the InP-composite-channel MHEMT shows improved breakdown performance; more than approximately 3.8 V. This increased breakdown voltage can be explained by the lower impact ionization coefficient of the InP-composite-channel MHEMT than that of the conventional MHEMT. The InP-composite-channel MHEMT also shows improved Radio Frequency characteristics of S21 gain of approximately 4.35 dB at 50 GHz, and a cutoff frequency (fT) and a maximum frequency of oscillation (fmax) of approximately 124 GHz and 240 GHz, respectively, were obtained. These are due to decreases in go and gm.

This paper proposes a low complexity composite CDMA system based on MIMO (Multiple-Input-Multiple-Output) processing and LDPC (low-density parity-check) codec based a CDMA system, which works well even if the complicated case of multipath, multiusers and short length LDPC codes. To explore the practical application, the mobile user in the composite CDMA systems is with only two antennas and adopts short length irregular LDPC codec, each user's data has been divided two LDPC encoded substreams being sent to two transmitter antennas at base station. Since the LDPC encoded substreams of reaching the mobile user are orthogonal to each other in space and time, the CDMA system performances (BER and SINR) can be improved much, but the multipath may ruin the orthogonalilty. To solve the problems, the paper provides the algorithms of main function modules of transmitter and receivers, gives a simple method to test the girth of LDPC codes, and analyzes the performance of MIMO-LDPC CDMA systems theoretically and experimentally. The simulation results show that the hybrid CDMA systems can have better performance than the conventional CDMA systems based on single transmitted antenna at a base station.

This paper presents a design method of multi-stage, multi-way microstrip power dividers with the aim of constructing a compact low-loss power divider with numbers of outputs. First, an integration design technique of power dividers composed of multi-step, multi-furcation and mitered bends is described. Since the analytical technique is founded on the planar circuit approach combined with the segmentation method, the optimization of the circuit patterns can be performed in a reasonable short computation time. Next, the present method is applied to the design of broadband Nn-way power dividers such as 32-way power divider consisting of 3-way dividers in two-stage structures, respectively. In addition, a 12-way power divider constructed from a series connection of a 3-way and three 4-way dividers is designed. The dividers equivalently contain a 3-section Chebyshev transformer to realize broadband properties. As a result, the fractional bandwidths of nearly 85% and 66.7% for the power-split imbalance less than 0.2 dB and the return loss better than -20 dB are obtained for the 9- and 12-way power dividers, respectively. The validity of these design results is confirmed by a commercial em-simulator (Ansoft HFSS) and experiments.

The release of image processing techniques make image modification and fakery easier. Image fakery, here, is defined as a process to copy a region of source image and paste it onto the destination image, with some post processing methods applied, such as boundary smoothing, blurring, etc. to make it natural. The most important characteristic of image fakery is object copy and paste. In order to detect fake images, this letter introduces a blind detection scheme based on singular value decomposition (SVD). Experimental results also show the effectiveness of the proposed scheme.

We consider the classification problem to construct a classifier c:{0,1}n{0,1} from a given set of examples (training set), which (approximately) realizes the hidden oracle y:{0,1}n{0,1} describing the phenomenon under consideration. For this problem, a number of approaches are already known in computational learning theory; e.g., decision trees, support vector machines (SVM), and iteratively composed features (ICF). The last one, ICF, was proposed in our previous work (Haraguchi et al., (2004)). A feature, composed of a nonempty subset S of other features (including the original data attributes), is a Boolean function fS:{0,1}S{0,1} and is constructed according to the proposed rule. The ICF algorithm iterates generation and selection processes of features, and finally adopts one of the generated features as the classifier, where the generation process may be considered as embodying the idea of boosting, since new features are generated from the available features. In this paper, we generalize a feature to an extended Boolean function fS:{0,1,*}S{0,1,*} to allow partial knowledge, where * denotes the state of uncertainty. We then propose the algorithm ICF* to generate such generalized features. The selection process of ICF* is also different from that of ICF, in that features are selected so as to cover the entire training set. Our computational experiments indicate that ICF* is better than ICF in terms of both classification performance and computation time. Also, it is competitive with other representative learning algorithms such as decision trees and SVM.

Recently, researches and developments for measuring and modeling of the human body have been receiving much attention. Our aim is to reconstruct an accurate shape of a human foot from multiple camera images, which can capture dynamic behavior of the object. In this paper, a foot-shape database is used for accurate reconstruction of human foot. By using Principal Component Analysis, the foot shape can be represented with new meaningful variables. The dimensionality of the data is also reduced. Thus, the shape of object can be recovered efficiently, even though the object is partially occluded in some input views. To demonstrate the proposed method, two kinds of experiments are presented: reconstruction of human foot in a virtual reality environment with CG multi-camera images, and in real world with eight CCD cameras. In the experiments, the reconstructed shape error with our method is around 2 mm in average, while the error is more than 4 mm with conventional volume intersection method.

Affine projection algorithms perform well for acoustic echo cancellation and adaptive equalization. Although these algorithms typically provide fast convergence, they are unduly complex when updating the weights of the associated adaptive filter. In this paper, we propose a new subband affine projection (SAP) algorithm and a facile method for its implementation. The SAP algorithm is derived by combining the affine projection algorithm and the subband adaptive structure with the maximal decimation. In the proposed SAP algorithm, the derived weight-updating formula for the subband adaptive filter has a simple form as compared with the normalized least mean square (NLMS) algorithm. The algorithm gives improved convergence and reduced computational complexity. The efficiency of the proposed algorithm for a colored input signal is evaluated experimentally.

Fingerprints are useful for biometric purposes because of their well known properties of distinctiveness and persistence over time. However, owing to skin conditions or incorrect finger pressure, original fingerprint images always contain noise. Especially, some of them contain useless components, which are often mistaken for the terminations that are an essential minutia of a fingerprint. Mathematical Morphology (MM) is a powerful tool in image processing. In this paper, we propose a linear time algorithm to eliminate impulsive noise and useless components, which employs generalized and ordinary morphological operators based on Euclidean distance transform. There are two contributions. The first is the simple and efficient MM method to eliminate impulsive noise, which can be restricted to a minimum number of pixels. We know the performance of MM is heavily dependent on structuring elements (SEs), but finding an optimal SE is a difficult and nontrivial task. So the second contribution is providing an automatic approach without any experiential parameter for choosing appropriate SEs to eliminate useless components. We have developed a novel algorithm for the binarization of fingerprint images [1]. The information of distance transform values can be obtained directly from the binarization phase. The results show that using this method on fingerprint images with impulsive noise and useless components is faster than existing denoising methods and achieves better quality than earlier methods.

A component connection enables a component to use the functionality of other components directly, without generating adapters or other mechanisms at run-time. In conventional component connection models, the connection between components, particularly third-party components, is very costly for code reuse because the component source code must be modified if the types of requester-side and provider-side are different. This paper proposes a new component model, built upon an existing component architecture, which abandons a component service type and connects components based on a method type collection of the provider and requester components. Our model enables flexible connections owing to relaxed component matching, in which the system that implements our model automatically converts values of parameters, return values, and exceptions between required methods and provided ones within a well-defined range. As a result of experimental evaluations, it is found that our model is superior to conventional models in terms of the component-use cost and the capability of changing connections.

This paper analyzes automation surprises in human-machine systems with time information. Automation surprises are phenomena such that the underlying machine's behavior diverges from user's intention and may lead to critical situations. Thus, designing human-machine systems without automation surprises is one of fundamental issues to achieve reliable user interaction with the machines. In this paper, we focus on timed human-machine interaction and address their formal aspects. The presented framework is essentially an extension of untimed human-machine interaction and will cover the previously proposed methodologies. We employ timed automata as a model of human-machine systems with time information. Modeling the human-machine systems as timed automata enables one to deal with not only discrete behavior but also time constraints. Then, by introducing the concept of timed simulation of the machine model and the user model, conditions which guarantee the nonexistence of automation surprises are derived. Finally, we construct a composite model in which a machine model and a user model evolve concurrently and show that automation surprises can be detected by solving a reachability problem in the composite model.

A planar composite right/left-handed leaky wave antenna which operates at W-band is fabricated and its backward to forward beam scanning operation including broadside direction is confirmed experimentally. The scanning angle from 61 to 114 degrees with a frequency scanning range of 76 to 79 GHz is achieved.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.