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Two-layer hidden Markov models for multi-class motor imagery classification

Two-layer hidden Markov models for multi-class motor imagery...

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Workshop on brain Decoding: Pattern Recognition Challenges in Neuroimaging

作者： Suk, Heung-Il Lee, Seong-Whan Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9780769541334

Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we design a framework of two-layer hidden Markov models (HMMs) for probabilistic classification of EEG signals. We first independently model the characteristics of EEG signals embedded in each channel for different motor imagery tasks in the lower-layer, and then represent the holistic task-related dynamic EEG patterns in the upper-layer by considering the relationships among channels. From the experimental results based on the dataset II-a of BCI Competition IV (2008), we demonstrated that our method achieved high session-to-session transfer results and was superior to previous methods. © 2010 IEEE.

关键词： brain computer interface

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View invariant body pose estimation based on biased manifold learning

View invariant body pose estimation based on biased manifold...

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International Conference on Pattern Recognition

作者： Hur, Dongcheol Wallraven, Christian Lee, Seong-Whan Dept. of Computer Science and Engineering Korea University Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Korea Republic of

ISBN: (纸本)9780769541099

In human body pose estimation, manifold learning is a popular technique for reducing the dimension of 2D images and 3D body configuration data. This technique, however, is especially vulnerable to silhouette variation such as caused by viewpoint changes. In this paper, we propose a novel approach that combines three separate manifolds for representing variations in viewpoint, pose and 3D body configuration. We use biased manifold learning to learn these manifolds with appropriately weighted distances. A set of four mapping functions are then learned by a generalized regression neural network for added robustness. Despite using only three manifolds, we show that this method can reliably estimate 3D body poses from 2D images with all learned viewpoints. © 2010 IEEE.

关键词： Neural networks

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Structured local edge pattern moment for pedestrian detection

Structured local edge pattern moment for pedestrian detectio...

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2nd International Conference on Image Analysis and Signal Processing, IASP'2010

作者： Su, Song-Zhi Chen, Shu-Yuan Li, Shao-Zi Duh, Der-Jyh Cognitive Science Department Xiamen University Xiamen China Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen China Department of Computer Science and Engineering Yuanze University Taiwan Department of Computer Science and Information Engineering Ching Yun University Taiwan

ISBN: (纸本)9781424455553

Local feature based approaches have gotten great success in object detection and recognition in recent years. In this paper, a novel local based feature, Structured Local Edge Pattern Moment (SLEPm), is proposed for pedestrian detection in the sliding window framework. SLEPm encodes not only the statistical information but also the structure and spatial information of object for pedestrian detection. Linear Support Vector Machine (SVM) is used as a binary classifier to determine whether a sub-window contains pedestrian. Experimental results in INRIA pedestrian database show that performance of SLEPm is better than that of Histogram of Oriented Gradient (HOG). © 2010 IEEE.

关键词： Object detection

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Accelerating generalized iterative scaling using componentwise extrapolations for on-line conditional random fields

Accelerating generalized iterative scaling using componentwi...

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International Conference on Machine Learning and Cybernetics

作者： Yang, Hee-Deok Lee, Seong-Whan School of Computer Engineering Chosun University Korea Republic of Department of Computer Science and Engineering Korea University Korea Republic of Department of Brain and Cognitive Engineering Korea University Korea Republic of

ISBN: (纸本)9781424465262

In this paper, a simple and globally convergent method based on penalized generalized iterative scaling (GIS) with staggered Aitken acceleration is proposed to efficiently estimate the parameters for an on-line conditional random field (CRF). The staggered Aitken acceleration method, which alternates between an acceleration step and a non-acceleration step, provides numerical stability and computational simplicity in analyzing the incompleteness of data. The proposed method is based on stochastic gradient descent (SGD) and it has the following advantages: (1) it can approximate parameters close to the empirical optimum in a single pass through the training examples;(2) it can reduce the computing time by eliminating computation of the inverse of the objective function's Hessian matrix with staggered Aitken acceleration. We show the convergence of penalized GIS based on the staggered Aitken acceleration method, compare its speed of convergence with that of other stochastic optimization methods, and also illustrate experimental results with public data sets. © 2010 IEEE.

关键词： Acceleration

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PTZ camera assisted face acquisition, tracking & recognition

PTZ camera assisted face acquisition, tracking & recognition

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IEEE International Conference on Biometrics: Theory, Applications and Systems

作者： Choi, Hyun-Cheol Park, Unsang Jain, Anil K. Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Department of Computer Science and Engineering Michigan State University E. Lansing MI 48824 United States

ISBN: (纸本)9781424475803

Face recognition systems typically have a rather short operating distance with standoff (distance between the camera and the subject) limited to 1∼2 meters. When these systems are used to capture face images at a larger distance (5∼10 m), the resulting images contain only a small number of pixels on the face region, resulting in a degradation in face recognition performance. To address this problem, we propose a camera system consisting of one PTZ camera and two static cameras to acquire high resolution face images up to a distance of 10 meters. We propose a novel camera calibration method based on the coaxial configuration between the static and PTZ cameras. We also use a linear prediction model and camera control to mitigate delays in image processing and mechanical camera motion. The proposed system has a larger standoff in face image acquisition and effectiveness in face recognition test. Experimental results on video data collected at a distance ranging from 5 to 10 meters of 20 different subjects as probe and 10,020 subjects as gallery shows 96.4% rank-1 identification accuracy of the proposed method compared to 0.1% rank-1 accuracy of the conventional camera system using a state-of-the-art matcher. © 2010 IEEE.

关键词： Face recognition

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PILL-ID: Matching and retrieval of drug pill imprint images

PILL-ID: Matching and retrieval of drug pill imprint images

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International Conference on Pattern Recognition

作者： Lee, Young-Beom Park, Unsang Jain, Anil K. Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Dept. of Computer Science and Engineering Michigan State University E. Lansing MI 48824 United States

ISBN: (纸本)9780769541099

Automatic illicit drug pill matching and retrieval is becoming an important problem due to an increase in the number of tablet type illicit drugs being circulated in our society. We propose an automatic method to match drug pill images based on the imprints appearing on the tablet. This will help identify the source and manufacturer of the illicit drugs. The feature vector extracted from tablet images is based on edge localization and invariant moments. Instead of storing a single template for each pill type, we generate multiple templates during the edge detection process. This circumvents the difficulties during matching due to variations in illumination and viewpoint. Experimental results using a set of real drug pill images (822 illicit drug pill images and 1,294 legal drug pill images) showed 76.74% (93.02%) rank-one (rank-20) matching accuracy. © 2010 IEEE.

关键词： Edge detection

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Function over form: An identity-free dynamic facial avatar 10

Function over form: An identity-free dynamic facial avatar

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Proceedings of the SSPNET 2nd International Symposium on Facial Analysis and Animation

作者： Griffin, Harry J. McOwan, Peter W. Johnston, Alan Cognitive Perceptual and Brain Sciences University College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom CoMPLEX University College London United Kingdom

ISBN: (纸本)9781450305228

The degree to which facial motion is used for facial categorisation and identification is an ongoing question of great interest to psychologists. The development and choice of appropriate tools for displaying facial motion to investigate this question is a major challenge. A facial avatar in which static cues to category and identity have been removed is required. Previous solutions include point light displays or projection of motion onto a pre-formed computer-generated head or mask. These solutions may offer only sparse sampling of the rich field of facial motion or be perceived as synthetic due to unrealistic skin tone or unusual avatar shape.

关键词： Interactive computer graphics

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View Invariant Body Pose Estimation Based on Biased Manifold Learning

View Invariant Body Pose Estimation Based on Biased Manifold...

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International Conference on Pattern Recognition

作者： Dongcheol Hur Christian Wallraven Seong-Whan Lee Dept. of Computer Science and Engineering Korea University Korea Dept. of Brain and Cognitive Engineering Korea University Korea

ISBN: (纸本)9781424475421

关键词： Manifolds Three dimensional displays Estimation Image reconstruction Joints Parameter estimation Artificial neural networks

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Two-Layer Hidden Markov Models for Multi-class Motor Imagery Classification

Two-Layer Hidden Markov Models for Multi-class Motor Imagery...

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Workshop on brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD)

作者： Heung-Il Suk Seong-Whan Lee Department of Computer Science and Engineering Korea University Seoul South Korea Department of Brain and Cognitive Engineering Korea University Seoul South Korea

关键词： Hidden Markov models Electroencephalography Feature extraction brain modeling Training Time domain analysis Principal component analysis

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Sparse dictionary methods for EEG signal classification in face perception

Sparse dictionary methods for EEG signal classification in f...

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2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010

作者： Shariat, Shahriar Pavlovic, Vladimir Papathomas, Thomas Braun, Ainsley Sinha, Pawan Dept. Computer Science Rutgers University Piscataway NJ United States Dept. of Biomedical Engineering RuCCS/Lab. of Vision Research Rutgers University Piscataway NJ United States Dept. of Brain and Cognitive Sciences MIT Cambridge MA United States

ISBN: (纸本)9781424478774

This paper presents a systematic application of machine learning techniques for classifying high-density EEG signals elicited by face and non-face stimuli. The two stimuli used here are derived from the vase-faces illusion and share the same defining contours, differing only slightly in stimulus space. This emphasizes activity differences related to high-level percepts rather than low-level attributes. This design decision results in a difficult classification task for the ensuing EEG signals. Traditionally, EEG analyses are done on the basis of signal processing techniques involving multiple instance averaging and then a manual examination to detect differentiating components. The present study constitutes an agnostic effort based on purely statistical estimates of three major classifiers: L1-norm logistic regression, group lasso and k Nearest Neighbors (kNN);kNN produced the worst results. L1 regression and group lasso show significantly better performance, while being abl e to identify distinct spatio-temporal signatures. Both L1 regression and group lasso assert the saliency of samples in 170ms, 250ms, 400ms and 600ms after stimulus onset, congruent with the previously reported ERP components associated with face perception. Similarly, spatial locations of salient markers point to the occipital and temporal brain regions, previously implicated in visual object perception. The overall approach presented here can provide a principled way of identifying EEG correlates of other perceptual/cognitive tasks. ©2010 IEEE.

关键词： brain

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