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Unsupervised Kalman filter approach to signature estimation for remotely sensed imagery

Unsupervised Kalman filter approach to signature estimation ...

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Jianwei Wang 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)078037536X

The commonly used linear spectral unmixing is generally performed on a single pixel basis and does not take advantage of inter-pixel spatial correlation. The Kalman filter has been considered to extend the linear unmixing by taking into account both spectral and spatial correlation. In addition to a linear mixture model implemented as a measurement equation, it includes a state equation to keep track of changes in between pixels. However, Kalman filtering requires the complete knowledge of image endmembers present in image data, which is generally not available and very difficult to obtain a priori. In order to relax this dilemma, this paper presents an unsupervised Kalman filtering (UKF) approach to signature estimation for remotely sensed images. It first uses an anomaly detector combined with orthogonal subspace projection (OSP) to extract desired image endmember signatures directly from the image data, then further applies a discrimination measure to classify the extracted signatures into a set of distinct signatures that will be used in the measurement equation. In order for the UKF to effectively capture spatial correlation among sample image pixels, the state equation is also implemented dynamically to adjust the state transition matrix adaptively. Experimental results have shown that the proposed UKF approach provides additional advantages over the commonly used spectral-based linear unmixing methods.

关键词： Pixel Equations Kalman filters Filtering Nonlinear filters Data mining remote sensing Detectors Signal processing image processing

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Thresholding video images for text detection

Thresholding video images for text detection

引用

16th International Conference on Pattern Recognition, ICPR 2002

作者： Du, Eliza Yingzi Chang, Chein-I Thouin, Paul D. 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 Department of Defense Fort MeadeMD20755 United States

ISBN: (纸本)0769516963

Thresholdging video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detection and further develops a relative entropy-based thresholding approach that can effectively extract text from complicated video images. In order to demonstrate its performance a comparative study is conducted among the proposed thresholding method and several thresholding techniques which are widely used for document and gray scale images. The experimental results show that thresholdging video images is far more difficult than thresholding document images and simple histogrambased methods generally do not perform well. © 2002 IEEE.

关键词： Pattern recognition

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Automatic thresholding abundance fractional images for mixed pixel classification

Automatic thresholding abundance fractional images for mixed...

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Shao-Shan Chiang Chein-I Chang Department of Electrical Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Mixed pixel classification is different from spatial-based image classification in the sense that the former deals with abundance fractional images resulting from mixed pixels as opposed to classification maps produced by the latter. As a result, mixed pixel classification is generally carried out by visual inspection on the generated abundance fractional images. Consequently, it can be very subjective and vary with different human interpretations. Under such circumstance, it is difficult to substantiate an algorithm and conducting a comparative analysis is impossible. This paper presents one histogram-based approach to thresholding abundance fractional images. It thresholds an abundance fractional image into a binary image using a probability of confidence as a threshold value.

关键词： Pixel Humans image analysis Inspection image segmentation Signal processing image processing Laboratories computer science image classification

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Relative entropy-based methods for image thresholding

Relative entropy-based methods for image thresholding

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jianwei Wang Eliza Yingzi Du Chein-I Chang P.D. Thouin Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore USA Department of Defense Fort Meade USA

A relative entropic thresholding approach was recently developed by Chang et al. (see Pattern Recognition, vol. 27, no. 9, p. 1275-1289, 1994). This paper extends Chang et al.'s approach to two more relative entropy-based thresholding methods, called local relative entropy thresholding (LRE) and joint relative entropy thresholding (JRE). Since relative entropy based methods are sensitive to sparse image histograms, a histogram compression and translation is suggested to compact the histogram. In order to achieve an objective assessment, uniformity and shape measures are introduced for performance evaluation. Experimental results show that when image histograms are sparse, with the proposed histogram compression and translation, JRE and LRE generally perform better than Chang et al.'s approach.

关键词： Entropy Histograms Shape measurement image coding remote sensing Signal processing image processing computer science Hardware Inspection

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Thresholding video images for text detection

Thresholding video images for text detection

引用

International Conference on Pattern Recognition

作者： E.Y. Du Chang C P.D. Thouin Remote Sensing Signal and Image Processing Laboratory. Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Defense Fort Meade MD USA

Thresholding video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detection and further develops a relative entropy-based thresholding approach that can effectively extract text from complicated video images. In order to demonstrate its performance a comparative study is conducted among the proposed thresholding method and several thresholding techniques which are widely used for document and gray scale images. The experimental results show that thresholding video images is far more difficult than thresholding document images and simple histogram-based methods generally do not perform well.

关键词： Entropy Shape measurement image resolution Information retrieval image retrieval Particle measurements remote sensing Signal processing image processing Laboratories

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3-D Bayesian ultrasound breast image segmentation using the EM/MPM algorithm

3-D Bayesian ultrasound breast image segmentation using the ...

引用

IEEE International Symposium on Biomedical Imaging, ISBI 2002

作者： Christopher, Lauren A. Delp, Edward J. Meyer, Charles R. Carson, Paul L. Purdue University Video and Image Processing Laboratory Schooi of Electrical and Computer Engineering West LafayetteIN47907 United States University of Michigan Department of Radiology Ann ArborMI48109 United States

ISBN: (纸本)078037584X

In this paper, ultrasound breast image segmentation is improved by using the volumetric data available in neighboring slices. The new algorithm extends the EM/MPM framework to 3D by including pixels from neighboring frames in the Markov Random Field (MRF) clique. In addition, this paper describes a unique linear cost factor introduced in the optimization loop to compensate for the attenuation common to ultrasound images. © 2002 IEEE.

关键词： image segmentation

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A study between orthogonal subspace projection and generalized likelihood ratio test in hyperspectral image analysis

A study between orthogonal subspace projection and generaliz...

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Qian Du Hsuan Ren Chein-I Chang Department of Electrical Engineering and Computer Science Texas A and M University Kingsville TX USA Edgewood Chemical and Biological Center US Army MD USA Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Orthogonal subspace projection (OSP) and generalized likelihood ratio test (GLRT) have shown success in hyperspectral image classification. The OSP is derived by maximizing signal-to-noise ratio (SNR) resulting from a linear mixture model in which the noise is assumed to be white. On the other hand, the GLRT is formulated based on a signal detection model that can be described by a binary hypothesis testing problem. In order for the GLRT to derive an analytical form, the noise in the signal detection model is generally assumed to be white Gaussian noise. However, Gaussianity is generally not true in remotely sensed imagery. Interestingly, such assumption has not been investigated. This paper presents a comparative study between OSP and GLRT based on their assumptions. In particular, a detailed analysis of assumptions made on these two approaches is conducted through a series of computer simulations. Experimental results show that the OSP does not depend on Gaussian noise. By the contrast, the GLRT is affected by the Gaussian noise assumption. If it is violated, its performance is degraded.

关键词： Testing Hyperspectral imaging image analysis Gaussian noise Signal to noise ratio Hyperspectral sensors Signal detection image classification Signal analysis Gaussian processes

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Multi-aspect pattern classification using predictive networks and error mapping

Multi-aspect pattern classification using predictive network...

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International Joint Conference on Neural Networks (IJCNN)

作者： J. Salazar M. Robinson M.R. Azimi-Sadjadi Signal/Image Processing Laboratory Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA

A classification scheme is developed for classifying underwater mine-like and non-mine-like objects from acoustic backscattered signals. This scheme uses a predictive network along with a neural network classifier. Th... 详细信息

关键词： Pattern classification Underwater acoustics Fusion power generation Signal processing Neural networks Acoustic signal detection Probability computer errors Laboratories Acoustical engineering

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Oversampled filter banks as error correcting codes

Oversampled filter banks as error correcting codes

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： F. Labeau L. Vandendorpe B. Macq Electrical and Computer Engineering Department McGill University Montreal Canada Communications and Remote Sensing Laboratory Universite catholique de Louvain Belgium

In this paper, we outline the similarities between oversampled filter banks and classical error-correcting codes. We recall the geometric interpretation of oversampling in terms of subspaces and that of the subband synthesis in terms of projection on this subspace. We derive generic expressions both in the time and polyphase domain for projections on this subspace along noise subspaces and give an example of potential application for subband image transmission.

关键词： Channel bank filters Error correction codes Filter bank Decoding Information filtering Information filters Genetic expression Time domain analysis image communication Ear

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Error resilience of video transmission by rate-distortion optimization and adaptive packetization

Error resilience of video transmission by rate-distortion op...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuxin Liu P. Salama E. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indianapolis IN USA

We propose new schemes to introduce error resilience into the compressed video bitstreams for transmission over packet networks. First, we develop an adaptive packetization scheme that prohibits any dependency across packets, for error resilience purposes, while exploiting the dependency within each packet to improve the source coding performance. Secondly, we address a two-layer rate-distortion optimization scheme to serve our packetization method. We also use forward error correction (FEC) coding across packets to provide further error protection. Finally, we present a simplified version of our schemes to make it fully compliant with the current ITU video coding standard - H.263+.

关键词： Resilience Rate-distortion image coding Video compression computer errors Propagation losses Motion estimation Forward error correction Video coding Streaming media

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