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IEEE/SP Workshop on Statistical signal processing (SSP)

作者： M.D. Farrell R.M. Mersereau Center for Signal & Image Processing School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA USA

Adaptive detection has a rich history in the radar community, and a number of other areas have borrowed heavily from constructs developed in this field. The task of target detection in hyperspectral imaging (HSI) is one such area that has recently begun to take advantage of parallels from the field of radar array processing. While there are key differences between the physical setup and data collected by radar and hyperspectral systems, the formulation of the adaptive detection problem is remarkably similar. In this paper we apply a two-stage detection approach originally developed for STAP in airborne radar to adaptive target detection in hyperspectral imaging. Touching first on the component algorithms involved, and then on their similarities and differences, we show that this two-stage approach has an interesting conceptual interpretation for HSI that comes from the multi-dimensional Euclidean geometry of the spectral measurement space. However, due to the nature of hyper-spectral target signatures and background statistics, we propose that there are many scenarios where HSI detection may be better served by reversing the order of stages applied to the data as laid out in previous formulations. Detection results for ground targets are presented to illustrate the potential of this new two-stage approach in hyperspectral adaptive detection

关键词： Hyperspectral imaging Radar detection Radar imaging Covariance matrix Adaptive signal detection Object detection Airborne radar Statistics Testing Pixel

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MIMO transmission subspace tracking with low rate feedback

MIMO transmission subspace tracking with low rate feedback

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jingnong Yang D.B. Williams Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The paper describes a low rate feedback algorithm for conveying partial channel state information - specifically, the dominant row subspace of the channel matrix - from the receiver to the transmitter in a continuously time-varying multiple-antenna environment. Since subspaces are points on a complex Grassmann manifold, variations in subspaces are treated as a piecewise geodesic process on the manifold. The receiver feeds back one bit to indicate the preferred sign of a random velocity matrix of the geodesic. Numerical results show that the performance of the proposed algorithm is better than the Grassmannian subspace packing approach at low-to-medium Doppler frequency and always better than the previously proposed gradient sign feedback scheme.

关键词： MIMO Feedback Transmitters Frequency image coding Channel capacity Quantization Receiving antennas signal processing image processing

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Temporal partition particle filtering for multiuser detectors with mutually orthogonal sequences

Temporal partition particle filtering for multiuser detector...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： S. Ozgur D.B. Williams Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

We propose a blind multiuser detector based on Monte Carlo Markov chain (MCMC) techniques. The detector exploits mutually orthogonal complementary sequences to distinguish between transmitting users and space-time codes to take advantage of the available spatial diversity. We propose a partitioning scheme for the symbol draws in the MCMC algorithm that reduces the complexity without any degradation in performance. The detector's performance is simulated in an iterative receiver that utilizes an outer coder. The simulations display some loss in coding gain because of the blind nature of the system; however, diversity gain is preserved.

关键词： Filtering Detectors Transmitters Monte Carlo methods Space time codes Partitioning algorithms Iterative algorithms Displays Sliding mode control signal processing

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A statistical method for binary classification of images

A statistical method for binary classification of images

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2005 ACM Symposium on Document engineering

作者： Simske, Steven J. Li, Dalong Aronoff, Jason S. Hewlett-Packard Labs. Mailstop 85 3404 E. Harmony Road Fort Collins CO 80528 United States Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)1595932402

The classification of documents with sparse text, and video analysis, relies on accurate image classification. We herein present a method for binary classification that accommodates any number of individual classifiers. Each individual classifier is defined by the critical point between its two means, and its relative weighting is inversely proportional to its expected error rate. Using 10 simple image analysis metrics, we distinguish a set of "natural" and "city" scenes, providing a "semantically meaningful" classification. The optimal combination of 5 of these 10 classifiers provides 85.8% accuracy on a small (120 image) feasibility corpus. When this feasibility corpus is then split into half training and half testing images, the mean accuracy of the optimum set of classifiers was 81.7%. Accuracy as high as 90% was obtained for the test set when training percentage was increased. These results demonstrate that an accurate classifier can be constructed from a large pool of simple classifiers through the use of the statistical ("Normal") classification method described herein. Copyright 2005 ACM.

关键词： Classification (of information)

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Complexity scalable motion compensated wavelet video encoding

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IEEE Transactions on Circuits and Systems for Video Technology 2005年第8期15卷 982-992页

作者： Turaga, Deepak S. Van Der Schaar, Mihaela Pesquet-Popescu, Beatrice Wireless Communications and Networking Philips Research USA Briarcliff Manor NY 10510 United States IBM T.J. Watson Research Center Hawthorne NY 10532 United States Department of Electrical and Computer Engineering University of California Davis Davis CA 95616-5294 United States Signal and Image Processing Department Télécom Paris 75634 Paris Cedex 13 France Media Delivery Architectures Department IBM T.J. Watson Research Center Hawthorne NY Electrical and Computer Engineering Department University of California Davis

We present a framework for the systematic analysis of video encoding complexity, measured in terms of the number of motion estimation (ME) computations, that we illustrate on motion compensated wavelet video coding schemes. We demonstrate the graceful complexity scalability of these schemes through the modification of the spatiotemporal decomposition structure and the ME parameters, and the use of spatiotemporal prediction. We generate a wide range of rate-distortion-complexity (R-D-C) operating points for different sequences, by modifying these options. Using our analytical framework we derive closed form expressions for the number of ME computations for these different coding modes and show that they accurately capture the computational complexity independent of the underlying content characteristics. Our frame-work for complexity analysis can be combined with rate-distortion modeling to determine the encoding structure and parameters for optimal R-D-C tradeoffs. © 2005 IEEE.

关键词： image coding

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Efficient FPGA implementation of a knowledge-based automatic speech classifier 1

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2nd International Conference on Embedded Software and Systems, ICESS 2005

作者： Siniscalchi, Sabato M. Gennaro, Fulvio Vitabile, Salvatore Gentile, Antonio Sorbello, Filippo 90128 Palermo Italy Dipartimento di Biotecnologie Mediche e Medicina Legale Università di Palermo Via del Vespro 90127 Palermo Italy Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States 90128 Palermo Italy

ISBN: (数字)9783540322979

ISBN: (纸本)3540308814

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of Automatic Speech Recognition (ASR) systems are comparable to Human Speech Recognition (HSR) only under very strict working conditions, and in general far lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper an optimized digital Knowledge-based Automatic Speech Classifier for real-time applications is implemented on FPGA using six attribute scoring Multi-Layer Perceptrons (MLP). Digital MLP key features are a virtual neuron architecture and use of sinusoidal activation functions for the hidden layer. Implementation results on FPGA show that use of sinusoidal activation functions decrease hardware resource usage of more than 50% for slices, FFs, LUTs and more than 35% for FPGA RAM blocks when compared with the standard sigmoid-based neuron implementation. Furthermore, neuron virtualization allows for a significant decrease of concurrent memory access, resulting in improved performance for the entire attribute scoring module. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Speech recognition

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3D ROC analysis for detection software used in water monitoring

3D ROC analysis for detection software used in water monitor...

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Chemical and Biological Standoff Detection III

作者： Liu, Wei-Min Wang, Su Chang, Chein-I. Jensen, Janet L. Jensen, James O. Knapp, Harlan Daniel, Robert Yin, Ray Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 US Army Edgewood Chemical and Biological Center Aberdeen Proving Ground MD 21010 ANP Technologies Inc. 824 Interchange Boulevard Newark DE 19711

Under the U.S. Army sponsored Joint Service Agent Water Monitor (JSAWM) program, developing hand-held assays using tickets for chemical/biological agent detection has been of major interest. One of keys to success is to develop detection algorithms that not only can effectively detect the presence of various agents, but also can quantify the detected agents. This paper presents a recent development of detection software that can perform 3-dimensional (3D) receiver operating characteristics (ROC) analysis which is based on quantified agent concentration. The ROC curves have been widely used in communications, signal processing and medical communities to evaluate the effectiveness of a detection technique. It generally formulates a signal detection problem as a binary composite hypothesis testing problem with the null hypothesis and the alternative hypothesis represents the case of no signal and the case of signal presence respectively. The ROC curve is then plotted based on the detection probability (power) PD versus the false alarm probability, P F. Unfortunately, such a two-dimensional (2D) (PD,P F)-based ROC curve does not factor in the concentration detected in an agent signal which is a crucial parameter in chemical/biological agent detection. The proposed 3D ROC analysis is developed from such a need. It includes an additional parameter, referred to as threshold t, which is used to threshold the detected agent signal concentration. Consequently, a different value of t results in a different 2D ROC curve. In order to take into account the thresholding factor t, a 3D ROC curve is derived and plotted based on three parameters, (PD,PF,t). As a result of the 3D ROC curve, three 2D ROC curves can be also derived. One is the conventional 2D (P D,PF)-ROC curve. Another is a 2D (PD,t)-ROC curve which describes the relationship between PD and the threshold value t. A third one is a 2D (PF,t)-ROC curve which shows the effect of the threshold value t on PF. The utility of

关键词： computer software

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An embedded system developed for hand held assay used in water monitoring

An embedded system developed for hand held assay used in wat...

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Chemical and Biological Standoff Detection III

作者： Wu, Lin Wang, Jianwei Ramakrishna, Bharath Hsueh, Mingkai Liu, Jonathan Wu, Qufei Wu, Chao-Cheng Cao, Mang Chang, Chein-I. Jensen, Janet L. Jensen, James O. Knapp, Harlan Daniel, Robert Yin, Ray Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 US Army Edgewood Chemical and Biological Center Aberdeen Proving Ground MD 21010 ANP Technologies Inc. 824 Interchange Boulevard Newark DE 19711

The US Army Joint Service Agent Water Monitor (JSAWM) program is currently interested in an approach that can implement a hardware-designed device in ticket-based hand-held assay (currently being developed) used for chemical/biological agent detection. This paper presents a preliminary investigation of the proof of concept. Three components are envisioned to accomplish the task. One is the ticket development which has been undertaken by the ANP, Inc. Another component is the software development which has been carried out by the Remote Sensing signal and image processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County (UMBC). A third component is an embedded system development which can be used to drive the UMBC-developed software to analyze the ANP-developed HHA tickets on a small pocket-size device like a PDA. The main focus of this paper is to investigate the third component that is viable and is yet to be explored. In order to facilitate to prove the concept, a flatbed scanner is used to replace a ticket reader to serve as an input device. The Stargate processor board is used as the embedded System with Embedded Linux installed. It is connected to an input device such as scanner as well as output devices such as LCD display or laptop etc. It executes the C-Coded processing program developed for this embedded system and outputs its findings on a display device. The embedded system to be developed and investigated in this paper is the core of a future hardware device. Several issues arising in such an embedded system will be addressed. Finally, the proof-of-concept pilot embedded system will be demonstrated.

关键词： Embedded systems

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3D ROC analysis for medical imaging diagnosis

3D ROC analysis for medical imaging diagnosis

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2005 27th Annual International Conference of the engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Wang, Su Chang, Chein-I Yang, Sheng-Chih Hsu, Giu-Cheng Hsu, Hsian-He Chung, Pau-Choo Guo, Shu-Mei Lee, San-Kan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 Department of Electrical Engineering National Cheng Kung University Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei 114 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Administrative Office Suao Veterans Hospital Yuan County 270 Taiwan

ISBN: (纸本)0780387406

Receiver operating characteristics (ROC) has been widely used as a performance evaluation tool to measure effectiveness of medical modalities. It is derived from a standard detection theory with false alarm and detection power interpreted as false positive (FP) and true positive (TP) respectively in terms of medical diagnosis. The ROC curve is plotted based on TP versus FP via hard decisions. This paper presents a three dimensional (3D) ROC analysis which extends the traditional two-dimensional (2D) ROC analysis by including a threshold parameter in a third dimension resulting from soft decisions, (SD). As a result, a 3D ROC curve can be plotted based on three parameters, TP, FP and SD. By virtue of such a 3D ROC curve three two-dimensional (2D) ROC curves can be derived, one of which is the traditional 2D ROC curve of TP versus FP with SD reduced to hard decision. In order to illustrate its utility in medical diagnosis, its application to magnetic resonance (MR) image classification is demonstrated. © 2005 IEEE.

关键词： Medical imaging

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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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