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Unsupervised seed determination for a region-based color image segmentation scheme

Unsupervised seed determination for a region-based color ima...

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IEEE International Conference on Image processing

作者： N. Ikonomakis K.N. Pataniotis A.N. Venetsanopoulos Department of Electrical and Computer Engineering Digital Signal & Image Processing Laboratory University of Toronto Toronto ONT Canada University of Toronto Toronto ON CA

ISBN: (纸本)0780362977

A color image segmentation technique is presented for use in coding and/or compression of video-conferencing sequences. The proposed technique utilizes the perceptual HSI (hue, saturation, intensity) color space and incorporates an unsupervised seed determination algorithm that finds the seed pixels that are in the spatial center of the regions in the image. The algorithm employs a hierarchy system where seeds found at the highest level produce the best results. The region growing algorithm uses these seeds to grow regions by appending to each seed pixel those neighboring pixels that satisfy a certain homogeneity criterion. The technique is found to be robust and relatively computationally inexpensive. The effectiveness of the algorithm is found to be much improved over techniques used in the past.

关键词： Color Image segmentation Pixel Image coding Image converters signal processing Image processing Laboratories Robustness Partitioning algorithms

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A target-constrained interference-minimized filter for subpixel target detection in hyperspectral imagery

A target-constrained interference-minimized filter for subpi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

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

Due to significantly improved high spatial and spectral resolution hyperspectral sensors can now uncover many material substances which cannot be resolved by multispectral sensors. However, this also comes at a price that many unknown and unidentified signal sources, referred to as interferers may also be extracted unexpectedly. Such interferers generally introduce additional noise effects on target detection and its factor must be taken into account. The problem associated with this interference is challenging because interference is generally unknown in nature and cannot be identified from an image scene. This paper presents a target-onstrained interference-minimized filter (TCIMF) which does not require to identify interferers, but can minimize the effects caused by interference. In addition, the TCIMF also separates undesired targets from the desired targets so that the TCIMF can eliminate undesired targets while detecting the desired targets and minimizing interfering effects. Most attractively, these three operations can be carried out by a single process in real time implementation.

关键词： Filters Object detection Hyperspectral sensors Layout Hyperspectral imaging Interference constraints Image resolution signal resolution Image sensors Image analysis

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Unsupervised fully constrained squares linear spectral mixture analysis method for multispectral imagery

Unsupervised fully constrained squares linear spectral mixtu...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： D.C. Heinz Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

Subpixel detection and quantification of materials in multispectral imagery presents a challenging problem due to a relatively low number of spectral bands available for analysis in which case the number of spectral bands may be less than the number of materials to be detected and quantified. The problem is even more difficult when the image scene is unknown and no prior knowledge is available. Under this circumstance, the desired information must be obtained directly from the image data. The authors present an unsupervised least squares-based linear mixture analysis method coupled with a band expansion technique for multispectral image analysis. This method allows the authors to extract necessary endmember information from an unknown image scene so that the endmembers present in the image can be detected and quantified. The band expansion technique creates additional bands from the existing multispectral bands using band-to-band nonlinear correlation. These expanded bands ease the problem of insufficient bands in multispectral imagery and can improve and enhance the performance of the proposed method. The experimental results demonstrate the advantages of the proposed approach.

关键词： Least squares methods Spectral analysis multispectral imaging Layout Image analysis Vectors Hyperspectral sensors Hyperspectral imaging Pixel Least squares approximation

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Quantum neural networks versus conventional feedforward neural networks: an experimental study

Quantum neural networks versus conventional feedforward neur...

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IEEE Workshop on Neural Networks for signal processing

作者： R. Kretzschmar R. Bueler N.B. Karayiannis F. Eggimann Signal and Information Processing Laboratory Swiss Federal Institute of Technology Zurich Switzerland Department of Electrical and Computer Engineering University of Houston Houston TX USA

This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved in the training of QNNs. This experimental study verifies that QNNs are capable of representing and quantifying the uncertainty inherent in the training data. It is also shown that simple post-processing of the QNN outputs makes QNNs an attractive alternative to conventional FFNNs for pattern classification applications.

关键词： Neural networks Feedforward neural networks Function approximation Fuzzy neural networks multi-layer neural network Uncertainty Pattern classification Joining processes signal processing Information processing

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Object segmentation based on multiple features for low bit rate video coding

Object segmentation based on multiple features for low bit r...

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International Conference on signal processing Proceedings (ICSP)

作者： D. Tancharoen S. Jitapunkul S. Triamlumlerd P. Kittipanya-ngam S. Chompun Technology Center National Electronics and Computer Center Thailand Digital Signal Processing Research Laboratory Department of Electrical Engineering Chulalongkom University Bangkok Thailand

Video object segmentation for object based video coding according to MPEG-4 should be able to combine many features of the image sequence to achieve high quality object segmentation. The significant features include color, motion vector and change information. This paper presents an object segmentation algorithm where the image features are combined for use in the segmentation process. A fast recursive shortest spanning tree (FRSST) algorithm is used to segment the image into different intensity regions because of the good image segmentation performance and the fast algorithm. Motion vectors are estimated from consecutive frames and searched by thresholding hierarchical block matching (HBM) which require low computation and give acceptable results. After that many features are integrated in the segmentation decision process. Then, the postprocessing refines the final segmentation output. The results from some test sequences especially in a video conference sequence have a good performance and show the object of interest boundary clearly.

关键词： Object segmentation Bit rate Video coding Image segmentation Motion analysis signal processing algorithms MPEG 4 Standard Motion estimation multimedia communication Layout

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Object Segmentation Based on multiple Features for Low Bit Rate Video Coding

Object Segmentation Based on Multiple Features for Low Bit R...

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2000年第16届世界计算机大会

作者： Datchakorn Tancharoen Somchai Jitapunkul Satien Triamlumlerd Panachit Kittipanya-ngam and Supattarachai Chompun Digital Signal Processing Research Laboratory Department of Electrical Engineering Chulalongkorn University National Electronics and Computer Technology Center

Video object segmentation for object based video coding according to MPEG-4 should be able to combine many features of image sequence to achieve high quality object *** significant features include color,motion vector and change *** paper presents the object segmentation algorithm which image features are combined to use in segmentation process.A fast recursive shortest spanning tree(FRSST) algorithm is used to segment image to be different intensity regions because of a good performance in image segmentation and a fast *** vectors are estimated from consecutive frames and searched by thresholding hierarchical block matching(HBM) which want quite low computation and give acceptable *** that many features will be integrated in segmentation decision ***,the postprocessing refines the final segmentation output. The results from some test sequences especially in video conference sequence have good performance and show interested-object boundary clearly.

关键词： object segmentation spanning tree image feature motion analysis

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Application of generalized constrained energy minimization approach to urban road detection

Application of generalized constrained energy minimization a...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chinsu Lin Chuin-Mu Wang Chein-I Chang Environmental Monitoring and Planning Laboratory Department of Forestry National Chiayi University Chiayi Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA

Classification of thematic layers have been used to update digital databases, particularly digital maps which can be accessed on the Internet by a fast growing number of users. One of the most important features in digital maps is traffic patterns and road networks. Unfortunately, due to low spectral and spatial resolutions of satellite imagery, many roads cannot be extracted at pixel level since the width of most urban and rural roads is less than the pixel size. In addition, road spectral signatures vary from pixel by pixel due to natural background. In this paper, an approach combining a subpixel detection method, called generalized constrained energy minimization with a principal component analysis-based fusion technique is proposed for urban road extraction. Experimental results show that the proposed method can effectively detect roads in Landsat TM and SPOT panchromatic images.

关键词： Roads Satellites Pixel Image databases Spatial databases Internet Telecommunication traffic Spatial resolution Minimization methods Remote sensing

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Unsupervised hyperspectral image analysis using independent component analysis

Unsupervised hyperspectral image analysis using independent ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shao-Shan Chiang Chein-I Chang I.W. Ginsberg Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Department of Energy Remote Sensing Laboratories Las Vegas NV USA

In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.

关键词： Hyperspectral imaging Image analysis Independent component analysis Hyperspectral sensors Principal component analysis Vectors Laboratories Spectral analysis Pixel Composite materials

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A linear mixture analysis-based compression for hyperspectral image analysis

A linear mixture analysis-based compression for hyperspectra...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Q. Du Chein-I Chang D.C. Heinz M.L.G. Althouse I.W. Ginsberg Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Department of Energy Remote Sensing Laboratories Las Vegas NV USA

The authors present a fully constrained least squares linear spectral mixture analysis-based compression technique for hyperspectral image analysis, particularly, target detection and classification. Unlike most compression techniques that directly deal with image gray levels, the proposed compression approach generates the abundance fractional images of potential targets present in an image scene and then encodes these fractional images so as to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in the abundance fractional images, the loss of information may have very little impact on image analysis. In some occasions, it even improves analysis performance. AVIRIS data experiments demonstrate that it can effectively detect and classify targets while achieving very high compression ratios.

关键词： Image analysis Image coding Hyperspectral imaging Hyperspectral sensors Layout Object detection Spectral analysis Performance analysis Feature extraction Pixel

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Underground imaging based on edge-preserving regularization

Underground imaging based on edge-preserving regularization

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1999 International Conference on Information Intelligence and Systems, ICIIS 1999

作者： Feng, Haihua Castanon, David A. Karl, W. Clem Multi-Dimensional Signal Processing Laboratory Department of Electrical and Computer Engineering Boston University 8 Saint Mary's Street BostonMA02215 United States

ISBN: (纸本)0769504469

Develops approaches for imaging weak-contrast buried objects using data from a ground penetrating radar array. An approximate physical model relating the collected data to the underground objects is developed. This model uses ray optics to represent the air/soil interface, and a Born approximation to model the weak contrast backscattering from buried objects. In order to address both modeling errors and ill-posedness, the proposed image reconstruction algorithms use regularization based on a total variation norm with orientation preference. The algorithms are tested on data generated by nonlinear finite difference time domain electromagnetic simulations. © 1999 IEEE.

关键词： Electromagnetic simulation

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