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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： V. Buzuloiu M. Malciu S.K. Mitra Image Processing and Analysis Laboratory University “Politehnica” of Bucharest Bucharest Romania CERN Geneva Department of Electrical and Computer Engineering University of California at Santa Barbara USA

This paper describes a method to approximate the impulse response of a linear shift-variant system by the impulse responses of a set of linear shift-invariant systems which process in parallel on various windowed versions of the input signal. The method is outlined for one-dimensional systems but the extension to multidimensional case is straightforward.

关键词： Linear approximation Large scale integration image processing image analysis Laboratories Multidimensional systems Linearity image restoration Signal restoration Transfer functions

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Radar backscattering model for multilayer mixed-species forests

Radar backscattering model for multilayer mixed-species fore...

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作者： Liang, Pan Moghaddam, Mahta Pierce, Leland E. Lucas, Richard M. IEEE Atmospheric and Environmental Research Inc. Lexington MA Department of Electrical Engineering and Computer Science Radiation Laboratpry University of Michigan Ann Arbor URSI Commission B Electromagnetics Academy Phi Kappa Phi Tau Beta Pi Eta Kappa Nu Microwave Image Processing Facility Within the Radiation Laboratory Electrical Engineering and Computer Science Department University of Michigan University of Wales Aberystwyth Aberystwyth United Kingdom Radiation Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2122 United States Institute of Geography and Earth Sciences University of Wales Aberystwyth SY23 3DB United Kingdom

A multilayer canopy scattering model is developed for mixed-species forests. The multilayer model provides a significantly enhanced representation of actual complex forest structures compared to the conventional canopy-trunk layer models. Multilayer Michigan Microwave Canopy Scattering model (Multi-MIMICS) allows overlapping layer configuration and a tapered trunk model applicable to forests of mixed species and/or mixed growth stages. The model is the first-order solution to a set of radiative transfer equations and includes layer interactions between overlapping layers. It simulates SAR backscattering coefficients based on input dimensional, geometrical, and dielectric variables of forest canopies. The Multi-MIMICS is an efficient realization of actual forest structures and can be shaped for specific interest of forest parameters. We present the model's application and validation in the paper. The model is parameterized using data collected from a 220 000-ha area of forests in central Queensland, Australia. Fifteen 50 × 50 m test sites representing the general forest diversity and growth stages are chosen as ground truth. Polarimetric backscattering Airborne SAR (AIRSAR) data of the same area are acquired to validate the model simulations. The model predicts SAR backscattering coefficients of the test areas. Simulation results show a good agreement with AIRSAR data at most frequencies and polarizations. The simulated backscattering coefficient from the multilayer model and the standard MIMICS are also compared and significant improvements are observed. © 2005 IEEE.

关键词： Mathematical models

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Using a Free-Parts Representation for Visual Speech Recognition

Using a Free-Parts Representation for Visual Speech Recognit...

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： P. Lucey S. Lucey S. Sridharan Queensland University of Technology Speech Audio Image and Video Research Laboratory Brisbane Australia Advanced Multimedia Processing Laboratory Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual speech recognition. A major problem with canonical area-based approaches in automatic visual speech recognition is the dependence these approaches have on locating and tracking the speaker’s region of interest (ROI) correctly. By employing a free-parts representation,we assume that the position/structure of patches within the mouth image can be relaxed so they can "freely" move to varying extents, hence reducing the influence of the front-end effect. In this paper, we show that by using a free-parts representation we gain some robustness against the problem of ROI localisation and tracking compared to current area-based feature extraction techniques such as the discrete cosine transform (DCT). Also in this paper, we expose the importance of representation for the task of visual speech recognition highlighted by the poor results current representations yield.

关键词： Speech recognition Mouth Hidden Markov models Laboratories Face recognition Feature extraction Discrete cosine transforms Robustness Automatic speech recognition Pixel

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A computer-aided system for mass detection and classification in digitized mammograms

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Biomedical engineering - Applications, Basis and Communications 2005年第5期17卷 215-228页

作者： Yang, Sheng-Chih Wang, Chuin-Mu Chung, Yi-Nung Hsu, Giu-Cheng Lee, San-Kan Chung, Pau-Choo Chang, Chein-I. Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Chin Yi Institute of Technology Taichung Taiwan Department of Electrical Engineering Da-Yeh University Chunghua Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Administrative Office Suao Veterans Hospital Yilan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

关键词： computer aided analysis

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Oceanic rain rates from the windsat radiometer

Oceanic rain rates from the windsat radiometer

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： K. Ahmad W.L. Jones J. Thomas-Stahle C.D. Kummerow Central Florida Remote Sensing Laboratory Department of Electrical and Computer Engineering University of Central Florida Orlando FL USA Department of Atmospheric Science Colorado State University Fort Collins CO USA

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A new application of texture unit coding to mass classification for mammograms

A new application of texture unit coding to mass classificat...

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2004 International Conference on image processing, ICIP 2004

作者： Chen, Yuan Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250

Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as Texture Unit Coding (TUC) has shown promise in texture classification. This paper presents a new application of the TUC to mass classification in mammograms. The TUC generates a texture spectrum for a texture image that can be used to describe the characteristics of masses. It also develops an information divergence (ID)-based discrimination criterion to measure the discrepancy between two texture spectra, a concept yet to explore in texture analysis. The TUC along with ID are further applied to mass classification in mammograms where the MiniMammographic Database provided by the Mammographie image Analysis Society (MIAS) is used for experiments. ©2004 IEEE.

关键词： Mammography

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How to effectively utilize information to design hyperspectral target detection and classification algorithms

How to effectively utilize information to design hyperspectr...

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2003 IEEE Workshop on Advances in Techniques for Analysis of remotely Sensed Data

作者： Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States

ISBN: (纸本)0780383508

Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis algorithms are designed. In this paper, we take up this issue and focus on algorithms designed and developed for target detection and classification in hyperspectral imagery. In order to effectively characterize the information available before and after the data are processed, the a priori information and a posteriori information are used in accordance with how the information is obtained. A piece of information is referred to as a priori information if it is provided by known knowledge before data are processed. On the other hand, a piece of information is referred to as a posteriori information if it is unknown a priori, but can be obtained directly from the data in an unsupervised fashion during the course of data processing. Since a priori information is known beforehand, it can be further decomposed into two types of information, desired and undesired a priori information. The desired a priori information is the knowledge that will assist, improve and enhance data analysis, whereas the undesired a priori information is the knowledge that hinders, interferes or destructs analysis during data processing. This paper investigates how these three types of information play their roles in design and development of several hyperspectral target detection and classification algorithms. Experiments are also conducted to validate their utility. © 2004 IEEE.

关键词： Classification (of information)

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Discrimination and identification for subpixel targets in hyperspectral imagery

Discrimination and identification for subpixel targets in hy...

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2004 International Conference on image processing, ICIP 2004

作者： Chang, Chein-I. Liu, Weimin Chang, Chein-Chi Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 Department of Civil and Environmental Engineering University of Maryland Baltimore County Baltimore MD 21250

Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets. ©2004 IEEE.

关键词： Imaging techniques

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Adaptive causal anomaly detection for hyperspectral imagery

Adaptive causal anomaly detection for hyperspectral imagery

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Mingkai Hsueh Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780387422

Anomaly detection finds target pixels whose signatures are spectrally distinct from their surrounding pixels. It is generally performed without prior knowledge. This paper presents an adaptive causal anomaly detector (ACAD) which implements a causal anomaly detector in such a fashion that a target pixel will be removed from the data correlation matrix once it is detected as an anomaly. As a result, it improves the commonly used RX algorithm as well as a recently developed causal RX filter

关键词： Hyperspectral imaging Filters Detectors Covariance matrix Adaptive signal detection Hyperspectral sensors remote sensing Signal processing image processing Laboratories

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A nested spatial window-based approach to target detection for hyperspectral imagery

A nested spatial window-based approach to target detection f...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Weimin Liu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780387422

A great challenge of hyperspectral target detection is to detect subtle targets without prior knowledge, particularly, when the targets of interest are insignificant and occur with low probabilities. This work provides a promising alternative to adaptive hyperspectral target detection. It considers a nested spatial window-based target detection (NSWTD) approach for hyperspectral imagery where a set of different spatial windows are nested and implemented to extract targets whose signatures are spectrally and spatially distinct. The use of nested spatial windows is determined by the image pixel resolution and applications. In order to demonstrate the performance of the proposed NSWTD approach, dual nested windows and three nested windows are implemented for computer simulations and real hyperspectral image experiments. The experimental results demonstrate that our proposed NSWTD approach performs effectively and improves a recent adaptive anomaly detector developed by Kwon et al. and the commonly used anomaly detector developed by Reed and Yu, referred to as RX algorithm. Its computation complexity is also very simple.

关键词： Object detection Hyperspectral imaging Hyperspectral sensors Detectors Spatial resolution remote sensing Signal processing image processing Laboratories computer science

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