Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

This thesis treats analysis and design of piecewise linear control systems. Piecewise linear systems capture many of the most common nonlinearities in engineering systems, and they can also be used for approximation of other nonlinear systems. Several aspects of linear systems with quadratic constraints are generalized to piecewise linear systems with piecewise quadratic constraints. It is shown how uncertainty models for linear systems can be extended to piecewise linear systems, and how these extensions give insight into the classical trade-offs between fidelity and complexity of a model. Stability of piecewise linear systems is investigated using piecewise quadratic Lyapunov functions. Piecewise quadratic Lyapunov functions are much more powerful than the commonly used quadratic Lyapunov functions. It is shown how piecewise quadratic Lyapunov functions can be computed via convex optimization in terms of linear matrix inequalities. The computations are based on a compact parameterization of continuous piecewise quadratic functions and conditional analysis using the S-procedure. A unifying framework for computation of a variety of Lyapunov functions via convex optimization is established based on this parameterization. Systems with attractive sliding modes and systems with bounded regions of attraction are also treated. Dissipativity analysis and optimal control problems with piecewise quadratic cost functions are solved via convex optimization. The basic results are extended to fuzzy systems, hybrid systems and smooth nonlinear systems. It is shown how Lyapunov functions with a discontinuous dependence on the discrete state can be computed via convex optimization. An automated procedure for increasing the flexibility of the Lyapunov function candidate is suggested based on linear programming duality. A Matlab toolbox that implements several of the results derived in the thesis is presented.

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Piecewise Linear Control Systems

[서평]「Digital Video Processing」

This paper proposes a novel system for the automatic detection and recognition of traffic signs. The proposed system detects candidate regions as maximally stable extremal regions (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on a cascade of support vector machine (SVM) classifiers that were trained using histogram of oriented gradient (HOG) features. The training data are generated from synthetic template images that are freely available from an online database; thus, real footage road signs are not required as training data. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 20 frames per second, and recognizes all classes of ideogram-based (nontext) traffic symbols from an online road sign database. Comprehensive comparative results to illustrate the performance of the system are presented.

Real-Time Detection and Recognition of Road Traffic Signs

In this paper, we propose a segmentation method of color images with being rid of oversegmentation. Many conventional segmentation methods assign one region to each gathering of pixels which have similar property. But, these methods tend to oversegment by the optical influences. Besides, they do not use the edge information, therefore the edge is not always selected to the boundary. In this study, we propose a segmentation method of color images with being rid of oversegmentation. Our method uses not only the pixel's property but also the information of regions' contour. It consists of the first segmentation process and the region combining process. First, the given image is segmented in consideration of the edge information. Next, the obtained regions are combined according to pixels' property and regions' contour.

Color image segmentation method using watershed algorithm and contour information

A method of system reduction using the continued fraction expansion about arbitrary points on the real frequency axis is proposed. It gives a good approximation and the procedure is suitable for digital computation.

System reduction by continued-fraction expansion about s=jωi

In many conventional voice activity detection (VAD) methods, speech signal is assumed to be acquired in high quality. However, human-machine interface based on speech is usually employed in indoor environment where various interferences exist, therefore, the VAD performance is seriously deteriorated. In this paper, we propose a novel VAD method with array signal processing on wavelet domain, in which we utilize the time, frequency and space information in the speech signal to separate interferences. In the proposed method, speech signal acquired by microphone array is at first decomposed into appropriate subbands with wavelet packet, and then array signal processing is executed on each subbands to realize VAD system for speech signal arriving from particular direction.

Voice Activity Detection with Array Signal Processing in the Wavelet Domain

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.

Nozomu Hamada

Papers

Color image segmentation method using watershed algorithm and contour information

System reduction by continued-fraction expansion about s=jωi

Voice Activity Detection with Array Signal Processing in the Wavelet Domain

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

Improvement of DOA Estimation Using Virtually Generated Multichannel Data from Two-Channel Microphone Array