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检索条件"机构=Computer Vision and Image Processing Laboratory Electrical and Computer Engineering"

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Causal pixel purity index (PPI)

Causal pixel purity index (PPI)

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XV

作者： Wu, Chao-Cheng 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 United States

ISBN: (纸本)9780819476005

Pixel Purity Index (PPI) has been widely used in endmember extraction. While it is available in ENVI software there are several interesting issues arising in its implementation. This paper re-invents the wheel by re-visiting the design rationale of the PPI and re-designing algorithms to implement PPI. More specifically, it develops the so-called Causal PPI (CPPI) which implements the PPI in a causal manner in the sense that the information used for data processing is only up to the data sample currently being visited. If the time required for computer processing is negligible, the CPPI actually becomes a real time PPI. The proposed CPPI can be implemented automatically and resolves the main issue of requiring human intervention to determine parameters.©2009 SPIE.

关键词： Pixels

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Mixed projection pursuit-based dimensionality reduction

Mixed projection pursuit-based dimensionality reduction

引用

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XV

作者： Safavi, Haleh 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 United States

ISBN: (纸本)9780819476005

Projection Pursuit (PP) is a component transform technique which seeks a component whose projection vector points to a direction of interestingness in data space which can be specified by a Projection Index (PI). Two most popular component analysis-based techniques, Principal Components Analysis (PCA), Independent Component Analysis (ICA) can be considered as special cases with their PIs specified by data variance and statistical independency *** the fact that various component analysis-based techniques have been used for Dimensionality Reduction (DR) the components are generally generated by a specific technique. Even in the case of PP, the same PI has been used to generate project components. This paper explores the utility of PP in DR where various projection indexes are used for DR in context of PP. It further lays out a general setting for PP-based DR and develops algorithms to perform one dimension reduction at a time by using different PIs. In order to substantiate our findings, experiments are conducted to demonstrate advantages of the PP with mixed PIs-based DR over traditional PCA-based, ICA-based and PP-based DR techniques.©2009 SPIE.

关键词： Principal component analysis

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Soft decision-made hyperspectral measures for target discrimination and classification

Soft decision-made hyperspectral measures for target discrim...

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Imaging Spectrometry XIV

作者： Wu, Chao-Cheng Chang, Chein-I Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819477477

Many hyperspectral measures such as Spectral Angle Mapper (SAM), Euclidean Distance (ED), Spectral Information Divergence (SID) calculate values that can be used to measure the closeness between two hyperspectral signatures in terms of spectral similarity. When a hyperspectral measure is used for target discrimination or classification, a commonly used approach assigns a data sample to a spectral signature with the maximum spectral similarity or a class whose mean is most similar to its spectral signature. As a result, such a hyperspectral measure is called a hard decision hyperspectral measure and its performance is evaluated by its confusion matrix. This paper develops a new class of hyperspectral measures, called soft-decision hyperspectral measures which use the similarity value between a data sample and a target signature or a class as an indicator of the likelihood of the data sample assigned to this particular signature or class instead of signature or class map resulting from hard decisions. In order for a soft-decision to perform target discrimination or classification, the soft-decision hyperspectral measure-generated likelihood values are normalized to probabilities so that a threshold can be used to make hard decisions via a recently developed 3D ROC analysis. Experimental study demonstrates that these two approaches yield different results. © 2009 SPIE.

关键词： Classification (of information)

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Design and analysis of real-time endmember extraction algorithms for hyperspectral imagery

Design and analysis of real-time endmember extraction algori...

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Satellite Data Compression, Communication, and processing V

作者： Chang, Chein-I Wu, Chao-Cheng Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819477453

Endmember extraction has recently received considerable attention in hyperspectral data exploitation since they represent crucial and vital information for hyperspectral data analysis. So far, no work has been reported on how to implement endmember extraction algorithms in real-time. An endmember is defined as an idealized signature and may or may not exist as a data sample or an image pixel. The interest of endmember extraction arises in the use of hundreds of contiguous spectral channels that allows a hyperspectral imaging sensor to uncover many subtle substances in diagnostic bands. However, finding such substances also presents a great challenge to hyperspectral data analysts and becomes imperative when it comes to satellite communication if a hyperspectral imaging sensor is operated in space platform where bandwidths used by satellite links may be very limited and downloading all the data may not be realistic in many practical applications. In order to address this need many endmember extraction algorithms have been developed and designed in the past, but no work has been reported on how to implement endmember extraction algorithms in real-time. This paper investigates this issue in designing algorithms for real time processing of endmember extraction and developed several endmember extraction algorithms derived from the widely used N-finder algorithm (NFINDR) that can be implemented in real time. © 2009 SPIE.

关键词： Space platforms

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Progressive dimensionality reduction for hyperspectral imagery

Progressive dimensionality reduction for hyperspectral image...

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Satellite Data Compression, Communication, and processing V

作者： Safavi, Haleh Liu, Keng-Hao 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 United States

ISBN: (纸本)9780819477453

This paper develops to a new concept, called Progressive Dimensionality Reduction (PDR) which can perform data dimensionality progressive in terms of information preservation. Two procedures can be designed to perform PDR in a forward or backward manner, referred to forward PDR (FPDR) or backward PDR (BPDR) respectively where FPDR starts with a minimum number of spectral-transformed dimensions and increases the spectral-transformed dimension progressively as opposed to BPDR begins with a maximum number of spectral-transformed dimensions and decreases the spectral-transformed dimension progressively. Both procedures are terminated when a stopping rule is satisfied. In order to carry out DR in a progressive manner, DR must be prioritized in accordance with significance of information so that the information after DR can be either increased progressively by FPDR or decreased progressively by BPDR. To accomplish this task, Projection Pursuit (PP)-based DR techniques are further developed where the Projection Index (PI) designed to find a direction of interestingness is used to prioritize directions of Projection Index Components (PICs) so that the DR can be performed by retaining PICs with high priorities via FPDR or BPDR. In the context of PDR, two well-known component analysis techniques, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) can be considered as its special cases when they are used for DR. © 2009 SPIE.

关键词： Principal component analysis

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Hyperspectral information compression

Hyperspectral information compression

引用

Satellite Data Compression, Communication, and processing V

作者： 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 United States

ISBN: (纸本)9780819477453

This paper investigates a new concept, called Hyperpsectral Information Compression (HIC) as opposed to Hyperspectral Data Compression (HDC) commonly used in the literature. A key feature that differentiates the HIC from the HDC is its focus on compression of information rather than data size. Such information compression is completely determined by a specific application in data exploitation. Accordingly, the HIC can be also referred to as exploitationbased compression. In order to substantiate the utility of the HIC, experiments are conducted to demonstrate that the HIC is indeed more effective than traditional HDC. © 2009 SPIE.

关键词： Data compression

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Automatic segmentation of brain structures using geometric moment invariants and artificial neural networks

Automatic segmentation of brain structures using geometric m...

引用

21st International Conference on Information processing in Medical Imaging, IPMI 2009

作者： Jabarouti Moghaddam, Mostafa Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI United States

ISBN: (纸本)3642024971

We propose an automatic method for the segmentation of the brain structures in three dimensional (3D) Magnetic Resonance images (MRI). The proposed method consists of two stages. In the first stage, we represent the shape of the structure using Geometric Moment Invariants (GMIs) in 8 scales. For each scale, an Artificial Neural Network (ANN) is designed to approximate the signed distance function of a desired structure. The GMIs along with the voxel intensities and coordinates are used as the input features of the ANN and the signed distance function as its output. In the second stage, we combine the outputs of the ANNs of the first stage and design another ANN to classify the image voxels into two classes, inside or outside of the structure. We introduce a fast method for moment calculations. The proposed method is applied to the segmentation of caudate, putamen, and thalamus in MRI where it has outperformed other methods in the literature. © 2009 Springer Berlin Heidelberg.

关键词： Neural networks

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Color quantization using principal components for initialization of Kohonen SOFM

Color quantization using principal components for initializa...

引用

IEEE International Conference on image processing

作者： Dimitris Mavridis Nikos Papamarkos Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Greece

A new method is proposed for initializing Kohonen's self-organizing feature maps (SOFM) of fixed zero neighborhood radius for use in color quantization. The method employs the two largest principal components of the input image so that the initial weights of a number of neurons approach the input image color distribution. The rest of the neurons are initialized using the smallest principal component of the input image. Namely, standard SOFM is applied to the projection of the input image pixels onto the plane spanned by the two largest principal components and to pixels of the original image defined by the smallest principal component. The neuron values which emerge initialize the final SOFM of fixed zero neighborhood radius that performs the color quantization of the original image. Experimental results show that the proposed method can often produce smaller quantization errors than standard SOFM and other color quantization methods.

关键词： Quantization Neurons Neural networks Pixel image color analysis image processing Clustering algorithms Fuzzy neural networks Vectors Lattices

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Comparison of basis-vector selection methods for structural modeling of hyperspectral imagery

Comparison of basis-vector selection methods for structural ...

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Imaging Spectrometry XIV

作者： Peña-Ortega, Carolina Vélez-Reyes, Miguel Computer Engineering Department Laboratory for Applied Remote Sensing and Image Processing Electrical University of Puerto Rico Mayagüez Campus P.O.Box 3535 Mayagüez PR 00681-3535 United States

ISBN: (纸本)9780819477477

This paper presents a comparison of different methods for structural modeling of hyperspectral imagery for target detection. We study structured models, based on linear subspaces and convex polyhedral cones, and their application for target detection. Different training methods are studied: Singular Value Decomposition (SVD) is used for subspace modeling, and Maximum Distance (MaxD) and Positive Matrix Factorization (PMF) for convex polyhedral modeling. We study different detectors based on orthogonal and oblique projections for subspace and convex polyhedral cones and evaluate their performance. Experimental results using HYDICE imagery are presented. © 2009 SPIE.

关键词： Singular value decomposition

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Generating optimal look-up tables to achieve complex color space transformations

Generating optimal look-up tables to achieve complex color s...

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IEEE International Conference on image processing

作者： Satyam Srivastava Thanh H. Ha Edward J. Delp Jan P. Allebach Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Color space transformations are very common in digital imaging and display systems. Their complex mathematics requires hardware friendly implementations to make them practical. In this paper, look-up tables (LUT) are described for implementation of the color transformations. We show that this is possible because an analytical model of the transform is available for training and cross-validation. We identify parameters that affect the performance of a LUT-based system and then formulate the solution as an optimization problem.

关键词： Table lookup Liquid crystal displays Color computer displays Sampling methods image processing Digital images Mathematics Hardware Analytical models

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