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Chein-i Chang - One of the best experts on this subject based on the ideXlab platform.
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An orthogonal Subspace Projection approach for face recognition
Mobile Multimedia Image Processing Security and Applications 2009, 2009Co-Authors: Zhi Zhou, Chein-i ChangAbstract:Face recognition has been widely used to automatically identify and verify a person. In this paper, we proposed a new approach based on orthogonal Subspace Projection (OSP) to identification of human faces. In linear mixture model of the face images, the OSP faces of the training images are calculated by using orthogonal Subspace Projection approach and the signal-to noise ratio maximization. And the weight parameter of the input image is obtained to do face recognition.
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orthogonal Subspace Projection osp revisited a comprehensive study and analysis
IEEE Transactions on Geoscience and Remote Sensing, 2005Co-Authors: Chein-i ChangAbstract:The orthogonal Subspace Projection (OSP) approach has received considerable interest in hyperspectral data exploitation recently. It has been shown to be a versatile technique for a wide range of applications. Unfortunately, insights into its design rationale have not been investigated and have yet to be explored. This work conducts a comprehensive study and analysis on the OSP from several signal processing perspectives and further discusses in depth how to effectively operate the OSP using different levels of a priori target knowledge for target detection and classification. Additionally, it looks into various assumptions made in the OSP and analyzes filters with different forms, some of which turn out to be well-known and popular target detectors and classifiers. It also shows how the OSP is related to the well-known least-squares-based linear spectral mixture analysis and how the OSP takes advantage of Gaussian noise to arrive at the Gaussian maximum-likelihood detector/estimator and likelihood ratio test. Extensive experiments are also included in this paper to simulate various scenarios to illustrate the utility of the OSP operating under various assumptions and different degrees of target knowledge.
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Unconstrained Mixed Pixel Classification: Least-Squares Subspace Projection
Hyperspectral Imaging, 2003Co-Authors: Chein-i ChangAbstract:The orthogonal Subspace Projection (OSP) for hyperspectral image classification was first reported in (1994) and has been successfully applied to hyperspectral data exploitation since then. Its ability in subpixel detection was also demonstrated in Chapter 3. As we recall (3.9), the OSP-derived detector was given by δ OSP(r) = T P U ⊥ (r) with the scale constant κ = 1. In Chapter 6, we have seen that this scale constant κ was actually determined by the a posteriori information that was used to estimate the unknown abundance fractions. Since OSP assumed the complete knowledge of the target signature matrix M and did not estimate the abundance vector α, the scale constant κ was absent in δ OSP(r). As long as the abundance fractions detected for α provide sufficient amounts for target detection, it did not matter if a was estimated accurately. That was why OSP worked effectively for the real hyperspectral data experiments in (1994). However, this may not be true in terms of abundance estimation. So, in this chapter, the OSP in Chapter 3 is revisited for mixed pixel classification. It is then extended by three unconstrained least-squares Subspace Projection approaches, called signature Subspace Projection (SSP), target Subspace Projection (TSP) and oblique Subspace Projection (OBSP) where the abundance fractions of target signatures are not known a priori, but are required to be estimated from the data. The three Subspace Projection methods use their estimated signature abundance fractions to achieve target classification in a mixed pixel. As a result, they can be viewed as a posteriori OSP as opposed to the OSP in Chapter 3, which can be thought of as a priori OSP. In order to evaluate these three approaches, a least-squares estimation error is cast as a signal detection problem in the framework of the Neyman-Pearson detection theory so that the detection performance can be measured by the receiver operating characteristics (ROC) analysis.
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A comparative study for orthogonal Subspace Projection and constrained energy minimization
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: Hsuan Ren, Chein-i ChangAbstract:We conduct a comparative study and investigate the relationship between two well-known techniques in hyperspectral image detection and classification: orthogonal Subspace Projection (OSP) and constrained energy minimization. It is shown that they are closely related and essentially equivalent provided that the noise is white with large SNR. Based on this relationship, the performance of OSP can be improved via data-whitening and noise-whitening processes.
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Orthogonal Subspace Projection-based approaches to classification of MR image sequences
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2001Co-Authors: Chuin-mu Wang, Chein-i Chang, Sheng-chih Yang, Pau-choo Chung, Clayton Chi-chang Chen, Chih-wei Yang, Chia-hsin WenAbstract:Abstract Orthogonal Subspace Projection (OSP) approach has shown success in hyperspectral image classification. Recently, the feasibility of applying OSP to multispectral image classification was also demonstrated via SPOT (Satellite Pour 1’Observation de la Terra) and Landsat (Land Satellite) images. Since an MR (magnetic resonance) image sequence is also acquired by multiple spectral channels (bands), this paper presents a new application of OSP in MR image classification. The idea is to model an MR image pixel in the sequence as a linear mixture of substances (such as white matter, gray matter, cerebral spinal fluid) of interest from which each of these substances can be classified by a specific Subspace Projection operator followed by a desired matched filter. The experimental results show that OSP provides a promising alternative to existing MR image classification techniques.
Moeness G. Amin - One of the best experts on this subject based on the ideXlab platform.
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a Subspace Projection approach for wall clutter mitigation in through the wall radar imaging
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Moeness G. AminAbstract:One of the main challenges in through-the-wall radar imaging (TWRI) is the strong exterior wall returns, which tend to obscure indoor stationary targets, rendering target detection and classification difficult, if not impossible. In this paper, an effective wall clutter mitigation approach is proposed for TWRI that does not require knowledge of the background scene nor does it rely on accurate modeling and estimation of wall parameters. The proposed approach is based on the relative strength of the exterior wall returns compared to behind-wall targets. It applies singular value decomposition to the data matrix constructed from the space-frequency measurements to identify the wall Subspace. Orthogonal Subspace Projection is performed to remove the wall electromagnetic signature from the radar signals. Furthermore, this paper provides an analysis of the wall and target Subspace characteristics, demonstrating that both wall and target Subspaces can be multidimensional. While the wall Subspace depends on the wall type and building material, the target Subspace depends on the location of the target, the number of targets in the scene, and the size of the target. Experimental results using simulated and real data demonstrate the effectiveness of the Subspace Projection method in mitigating wall clutter while preserving the target image. It is shown that the performance of the proposed approach, in terms of the improvement factor of the target-to-clutter ratio, is better than existing approaches and is comparable to that of background subtraction, which requires knowledge of a reference background scene.
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GPS Antijam via Subspace Projection: A Performance Analysis for FM Interference in the C/A Code
Digital Signal Processing, 2002Co-Authors: Liang Zhao, Moeness G. Amin, Alan R. LindseyAbstract:Abstract Zhao, L., Amin, M. G., and Lindsey, A. R., GPS Antijam via Subspace Projection: A Performance Analysis for FM Interference in the C/A Code, Digital Signal Processing 12 (2002) 175–192 This paper is concerned with frequency modulated (FM) interference cancelation in GPS receivers. FM signals are instantaneously narrowband and have clear time–frequency signatures that are distinct from the GPS C/A spreading codes. Interference cancelation is performed by first constructing the interference Subspace and then projecting the data onto the orthogonal Subspace. Interference Subspace estimation can be provided by the time–frequency distribution (TFD) or any other instantaneous frequency (IF) estimator. The GPS receiver signal-to-interference and noise ratio in the presence of IF or phase estimation errors is derived. The errors are assumed Gaussian and independent. From both analysis and simulations, it is shown that phase estimation errors can substantially impede interference rejection in GPS using Subspace Projection techniques.
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Performance analysis of Subspace Projection techniques for FM interference rejection in GPS receivers
Digital Wireless Communication III, 2001Co-Authors: Liang Zhao, Moeness G. Amin, Alan R. LindseyAbstract:Subspace Projection techniques are effective in excising FM interferers in GPS receivers. The FM jammers are instantaneously narrowband and have clear time-frequency (t- f) signatures that are distinct from the GPA C/A spreading codes. The instantaneous frequency (IF) estimate, provided by the time-frequency distribution, or any other estimator, is used to form the jammer Subspace. The interference rejection is implemented by projecting the received data onto the orthogonal Subspace of the jammer Subspace. Errors in IF estimations, however, perturb the Projection matrix and allow some of the jammer power to escape the Projection operation. This in turn leads to degraded receiver performance and lower SINR. This paper derives the signal- to-interference-and-noise ratio (SINR) of the GPS receivers in the presence of Gaussian IF estimation errors. It is shown that IF estimation errors can substantially impede interference rejection in GPS using Subspace Projection techniques.
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Array processing for nonstationary interference suppression in DS/SS communications using Subspace Projection techniques
IEEE Transactions on Signal Processing, 2001Co-Authors: Yimin Zhang, Moeness G. AminAbstract:Combined spatial and time-frequency signatures of signal arrivals at a multisensor array are used for nonstationary interference suppression in direct-sequence spread-spectrum (DS/SS) communications. With random PN spreading code and deterministic nonstationary interferers, the use of antenna arrays offers increased DS/SS signal dimensionality relative to the interferers. Interference mitigation through a spatio-temporal Subspace Projection technique leads to reduced DS/SS signal distortion and improved performance over the case of a single antenna receiver. The angular separation between the interference and desired signals is shown to play a fundamental role in trading off the contribution of the spatial and time-frequency signatures to the interference mitigation process. The expressions of the receiver signal-to-interference-noise ratio (SINR) implementing Subspace Projections are derived, and numerical results are provided.
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Subspace Projection techniques for anti-FM jamming GPS receivers
Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496), 1Co-Authors: Liang Zhao, Moeness G. Amin, Alan R. LindseyAbstract:This paper applies Subspace Projection techniques as a pre-correlation signal processing method for the FM interference suppressions in GPS receivers. The FM jammers are instantaneous narrowband and have clear time-frequency (t-f) signatures that are distinct from the GPS C/A spread spectrum code. In the proposed technique, the instantaneous frequency (IF) of the jammer is estimated and used to construct a rotated signal space in which the jammer occupies one dimension. The anti-jamming system is implemented by projecting the received sequence onto the jammer-free Subspace. This paper focuses on the characteristics of the GPS C/A code and derives the signal to interference plus noise ratio (SINR) of the GPS receivers implementing the Subspace Projection techniques.
Chin-hui Lee - One of the best experts on this subject based on the ideXlab platform.
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Speech recognition using weighted HMM and Subspace Projection approaches
IEEE Transactions on Speech and Audio Processing, 1994Co-Authors: Chin-hui LeeAbstract:A weighted hidden Markov model (HMM) algorithm and a Subspace Projection algorithm are proposed to address the discrimination and robustness issues for HMM-based speech recognition. A robust two-stage classifier is also proposed to incorporate these two approaches to further improve the performance. The weighted HMM enhances its discrimination power by first jointly considering the state likelihoods of different word models, then assigning a weight to the likelihood of each state, according to its contribution in discriminating words. The robustness of this model is then improved by increasing the likelihood difference between the top and the second candidates. The Subspace Projection approach discards unreliable observations on the basis of maximizing the divergence between different word pairs. To improve robustness, the mean of each cluster is then adjusted to obtain maximum separation different clusters. The performance was evaluated with a highly confusable vocabulary consisting of the nine English E-set words. The test was conducted in a multispeaker (100 talkers), isolated-word mode. The 61.7% word accuracy for the original HMM-based system was improved to 74.9% and 76.6%, respectively, by using the weighted HMM and the Subspace Projection methods. By incorporating the weighted HMM in the first stage and the Subspace Projection in the second stage, the two-stage classifier achieved a word accuracy of 79.4%. >
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Robustness and discrimination oriented speech recognition using weighted HMM and Subspace Projection approaches
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: Keh-yin Su, Chin-hui LeeAbstract:Two algorithms, a weighted hidden Markov model (HMM) algorithm and a Subspace Projection algorithm, are proposed to address some of the discrimination and robustness issues for HMM-based speech recognition. A robust two-stage classifier is also proposed to enhance the discrimination capability of the classifiers in each of the two stages so that the overall discrimination power is improved. The proposed algorithms were evaluated using a highly confusable vocabulary consisting of the nine English E-set letters. The test was conducted in a multi-speaker, isolated-word mode. The average word accuracy for the original HMM-based system was 61.7%. When the weighted HMM and the Subspace Projection methods were incorporated, the word accuracy improved to 74.9% and 76.4%, respectively. By incorporating the weighted HMM in the first stage and the Subspace Projection in the second stage, the two-stage classifier achieved a word accuracy of 79.4%. >
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ICASSP - Robustness and discrimination oriented speech recognition using weighted HMM and Subspace Projection approaches
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: Chin-hui LeeAbstract:Two algorithms, a weighted hidden Markov model (HMM) algorithm and a Subspace Projection algorithm, are proposed to address some of the discrimination and robustness issues for HMM-based speech recognition. A robust two-stage classifier is also proposed to enhance the discrimination capability of the classifiers in each of the two stages so that the overall discrimination power is improved. The proposed algorithms were evaluated using a highly confusable vocabulary consisting of the nine English E-set letters. The test was conducted in a multi-speaker, isolated-word mode. The average word accuracy for the original HMM-based system was 61.7%. When the weighted HMM and the Subspace Projection methods were incorporated, the word accuracy improved to 74.9% and 76.4%, respectively. By incorporating the weighted HMM in the first stage and the Subspace Projection in the second stage, the two-stage classifier achieved a word accuracy of 79.4%. >
Fok Hing Chi Tivive - One of the best experts on this subject based on the ideXlab platform.
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a Subspace Projection approach for wall clutter mitigation in through the wall radar imaging
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Moeness G. AminAbstract:One of the main challenges in through-the-wall radar imaging (TWRI) is the strong exterior wall returns, which tend to obscure indoor stationary targets, rendering target detection and classification difficult, if not impossible. In this paper, an effective wall clutter mitigation approach is proposed for TWRI that does not require knowledge of the background scene nor does it rely on accurate modeling and estimation of wall parameters. The proposed approach is based on the relative strength of the exterior wall returns compared to behind-wall targets. It applies singular value decomposition to the data matrix constructed from the space-frequency measurements to identify the wall Subspace. Orthogonal Subspace Projection is performed to remove the wall electromagnetic signature from the radar signals. Furthermore, this paper provides an analysis of the wall and target Subspace characteristics, demonstrating that both wall and target Subspaces can be multidimensional. While the wall Subspace depends on the wall type and building material, the target Subspace depends on the location of the target, the number of targets in the scene, and the size of the target. Experimental results using simulated and real data demonstrate the effectiveness of the Subspace Projection method in mitigating wall clutter while preserving the target image. It is shown that the performance of the proposed approach, in terms of the improvement factor of the target-to-clutter ratio, is better than existing approaches and is comparable to that of background subtraction, which requires knowledge of a reference background scene.
Marcos Eduardo Valle - One of the best experts on this subject based on the ideXlab platform.
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On Subspace Projection autoassociative memories based on linear support vector regression
2015 Latin America Congress on Computational Intelligence (LA-CCI), 2015Co-Authors: Marcos Eduardo Valle, Emely Pujolli Da SilvaAbstract:Autossociative memories (AMs) are models inspired by the human brain ability to store and recall information. They should be able to retrieve a stored information upon presentation of a partial or corrupted item. An AM that projects the input onto a linear Subspace is called Subspace Projection autoassociative memory (SPAM). The recall phase of a SPAM model is equivalent to a multi-linear regression problem. In particular, the optimal linear autoassociative memory (OLAM) corresponds to the SPAM model obtained by considering traditional least squares regression in the recall phase. In this paper, we present a novel class of SPAM models obtained by considering linear support vector regression (SVR). Precisely, we introduce three SPAM models based on primal, dual, and bi-level formulations of the linear e-support vector regression. A simple example is used throughout the paper to illustrate the noise tolerance of the proposed memory models.
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A Robust Subspace Projection Autoassociative Memory Based on the M-Estimation Method
IEEE Transactions on Neural Networks and Learning Systems, 2014Co-Authors: Marcos Eduardo ValleAbstract:An autoassociative memory (AM) that projects an input pattern onto a linear Subspace is referred to as a Subspace Projection AM (SPAM). The optimal linear AM (OLAM), which can be used for the storage and recall of real-valued patterns, is an example of SPAM. In this brief we introduce a novel SPAM model based on the robust M-estimation method. In contrast to the OLAM and many other associative memory models, the robust SPAM represents a neural network in which the synaptic weights are iteratively adjusted during the retrieval phase. Computational experiments concerning the reconstruction of corrupted gray-scale images reveal that the novel memories exhibit an excellent tolerance with respect to salt and pepper noise as well as some tolerance with respect to Gaussian noise and blurred input images.
