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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

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A hidden Markov model fingerprint matching approach

A hidden Markov model fingerprint matching approach

引用

International conference on Machine Learning and Cybernetics (ICMLC)

作者： Hao Guo Remote Sensing Technology Laboratory School of Computer Science and Technology Dalian Maritime University Dalian China

Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern, which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded hidden Markov model (HMM) that is used for modeling the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded hidden Markov model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded hidden Markov model parameters.

关键词： Hidden Markov models Fingerprint recognition image matching Stochastic processes Robustness Probability distribution Fingers Surface texture Speech recognition remote sensing

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A novel registration method for SAR and SPOT images

A novel registration method for SAR and SPOT images

引用

IEEE International conference on image processing

作者： Lixia Shu Tieniu Tan Ming Tang Chunhong Pan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we propose a novel mutual information (MI) based method to register SAR and SPOT images. The traditional MI can register SAR and SPOT images well. However, its robustness is weakened by the absence of orientation information. In our approach, we first extract orientation information at four directions by Gabor filters, then MI of each corresponding image pair is calculated and the average value of MI is used as an improved measure for MI. Experiments show that our method is more robust than the traditional MI method. Meanwhile our method maintains comparable accuracy to the traditional MI, which is much better than coarse manual registration.

关键词： image registration Mutual information Robustness Gabor filters Biomedical imaging Data mining remote sensing Entropy Histograms Laboratories

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Soft SVM and its application to video object extraction

Soft SVM and its application to video object extraction

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yi Liu Y.F. Zheng Department of Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA

Support vector machines (SVM) are state-of-the-art learning machines and have found a great deal of success in a wide range of applications. In the framework of SVM, each sample belongs to either one class or the other. This requirement, however, makes it difficult to apply SVM to the applications where the data exhibit partial or unclear class memberships. To address this problem, this paper reformulates the standard SVM to be a new learning machine that is capable of dealing with binary (or hard) as well as real-valued (or soft) class memberships. The new machine, which is named soft SVM (S-SVM), has been integrated into a classification-based video object extraction approach, and the experimental results demonstrate the effectiveness of the new approach.

关键词： Support vector machines Support vector machine classification Machine learning Application software Risk management pattern recognition image analysis pattern analysis Soil remote sensing

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Improvements in object recognition by radar and ladar data fusion

Improvements in object recognition by radar and ladar data f...

引用

conference on image and Signal processing for remote sensing X

作者： Bers, K Brehm, T Essen, H Jäger, K FGAn Res Inst Optron & Pattern Recognit D-76275 Ettlingen Germany

ISBN: (纸本)0819455202

remote sensing using unmanned aerial vehicles is gaining more and more importance during peace keeping missions for military reconnaissance. Those applications nowadays have to take into account that under civil war conditions a mix-up of sensors within sensitive urban terrain may be useful. These tasks typically have to be fulfilled also under adverse weather conditions, which mainly can be served by airborne imaging radar sensors. Advanced radar sensors are able to deliver highly resolved images with considerable information content, as polarimetry, 3d-features and robustness against changing environmental and operational conditions. Extending the knowledge base for an object by fusion of radar data with Ladar-information or IR, a safe detection and even identification of objects becomes feasible allowing an optimized signal processing by distributing the assignments between the combined sensors. The contribution describes the different sensors and gives an overview over the image data for the sample scenes. The methods of object discrimination are discussed and representative results are shown.

关键词： Radar Sensors LIDAR Data fusion image fusion Data modeling Synthetic aperture radar Distance measurement image resolution Object recognition

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Genetic algorithm for unsupervised classification of remote sensing imagery

Genetic algorithm for unsupervised classification of remote ...

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conference on image processing - Algorithms and Systems iiI

作者： Yang, MD Yang, YF Natl Chung Hsing Univ Dept Civil Engn Taichung 40227 Taiwan

ISBN: (纸本)0819452017

Traditionally, an unsupervised classification divides all pixels within an image into a corresponding class pixel by pixel. The number of classes must be selected, but seldom is ascertainable with little information in advance. Moreover, spectral properties of specific informational classes change seasonally for satellite imagery. The relationships between informational classes and spectral classes are not always constant, and relationships defined for one image cannot be extended to others. Thus, the analyst has very limited or no control over the menu of classes and their specific identities. In this study, a Generic Algorithm is adopted to interpret the cluster centers of an image and to reveal a suitable number of classes to overcome the disadvantage of unsupervised classification. A Genetic Algorithm is capable of dealing with a set of numerous data such as satellite imagery pixels. An optimization consequence of the image classification is introduced and carried out. Through an image process program developed in Mathlab, the GA unsupervised classifier was processed on several test images for validity and on SPOT satellite imagery. The classified SPOT image was compared with finer aerial photographs as a ground truth for the estimation of classification accuracy.

关键词： Genetic Algorithm unsupervised classification remote sensing imagery pattern recognition

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Survey and assessment of advanced feature extraction techniques and tools for EO applications

Survey and assessment of advanced feature extraction techniq...

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conference on image and Signal processing for remote sensing IX

作者： Sorensen, MK Rasmussen, MS Skriver, H Johansen, P Pece, A Thygesen, JH Univ Copenhagen Inst Geog GRAS DK-1350 Copenhagen Denmark

ISBN: (纸本)0819451215

In addition to the substantial amounts of available Earth Observation (EO) data, there is currently an increasing trend towards the acquisition of larger and larger EO data and image quantities from single satellites or missions, with multiple, higher resolution sensors and with more frequent revisiting. More sophisticated algorithms and techniques than those largely in use today are required to exploit this rapidly growing wealth of data and images to a fuller extent. The project "Survey and Assessment of Advanced Feature Extraction Techniques and Tools for EO Applications" (SURF) funded by the European Space Agency (ESA) will address these issues. The objective of SURF is to provide an overview of the current state-of-the-art Methods within feature extraction and manipulation for EO applications and to identify scenarios and related architectures for exploitation of the most promising EO feature extraction Methods. The task is to identify the most promising Methods to extract pertinent information from EO data on environment, natural resources and security issues. SURF aims at listing existing Methods with the final goal of identifying the three most promising Methods to be implemented in prototype solutions. The work includes the development of the concept for the evaluation and rating of Methods relative to the users needs for information, the maturity and novelty of the Methods, the potential for fusing data and the operational feasibility. Special emphasis will be made regarding the exploitation of state-of-the art image processing, pattern recognition and classification techniques.

关键词： automatic feature extraction classification survey methods EO remote sensing data fusion

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Application of high resolution imagery for oil-fields ecological monitoring

Application of high resolution imagery for oil-fields ecolog...

引用

conference on remote sensing for Environmental Monitoring, GIS Applications and Geology IV

作者： Napryushkin, A Vertinskaya, E Gavilanes, D Tomsk Polytech Univ Tomsk 634034 Russia

ISBN: (纸本)0819455210

In the paper a methodology of RS-based thematic mapping is introduced which uses an original RS imagery interpretation approach. The implementation of the methodology is based on application of GIS MapInfo Professional and original imagery processing and interpretation system "LandMapper" developed in Tomsk Polytechnic University (TPU). The paper considers the basic principles of imagery interpretation approach adopted in the "LandMapper" system as well as gives the results of its application for Tomsk region oil-fields pollution mapping with use of high resolution images acquired by QuickBird satellite.

关键词： ecological monitoring digital image analysis pattern recognition texture analysis

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Classification of high spatial resolution images by means of a Gabor wavelet decomposition and a support vector machine

Classification of high spatial resolution images by means of...

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conference on image and Signal processing for remote sensing X

作者： Baraldi, A Bruzzone, L ISSIA CNR Bari Italy

ISBN: (纸本)0819455202

Very high spatial resolution satellite images, acquired by third-generation commercial remote sensing (RS) satellites (like Ikonos and QuickBird), are characterized by a tremendous spatial complexity, i.e. surface objects are described by a combination of spectral, textural and shape information. Potentially capable of dealing with the spatial complexity of such images, context-sensitive data mapping systems, e.g. employing filter sets designed for texture feature analysis/synthesis, have been extensively studied in pattern recognition literature in recent years. In this work, four implementations of a two-stage classification scheme for the analysis of high spatial resolution images are compared. Competing first stage (feature extraction) implementations of increasing complexity are: 1) a standard multi-scale dyadic Gaussian pyramid image decomposition, and 2) an original almost complete (near-orthogonal) basis for the Gabor wavelet transform of an input image at selected spatial frequencies (i.e. band-pass filter central frequency and filter orientation pairs). The second stage of the classification scheme consists of. a) an ensemble of pixel-based two-class support vector machines (SVMs) applied to the multi-class classification problem according to the one-against-one strategy, exploiting the well-known SVM's capability of dealing with high dimensional mapping problems;and b) a traditional two-phase supervised learning pixel-based Radial Basis Function (RBF) network. In a badly-posed Ikonos image classification experiment, SVM combined with the two filter sets provide an interesting compromise between ease of use (i.e. easy free parameter selection), classification accuracy, robustness to changes in surface properties, capability of detecting genuine, but small, image details as well as linear structures. Qualitatively and quantitatively, the multi-scale multi-orientation almost complete Gabor wavelet transform appears superior to the dyadic multi-scale Gaussian

关键词： Gabor filter bank Gaussian/Laplacian image decomposition high spatial resolution satellite imagery Hughes phenomenon image classification wavelet

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Comparison of the binary phase only filter design by DBS algorithm with the classical SDF filter

Comparison of the binary phase only filter design by DBS alg...

引用

conference on Military remote sensing

作者： Butt, JA Wilkinson, TD Univ Cambridge Dept Engn Photon & Sensors Grp Cambridge CB2 1PZ England

ISBN: (纸本)0819455660

The joint transform correlator (JTC) is one of two main optical image processing architectures which provide us with a highly effective way of comparing images in a wide range of applications. Traditionally an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. There is a new class of application for the JTC where they are used as image comparators, not having a known reference image, rather frames from a video sequence form both the input and reference. The JTC input plane is formed by combining the current frame with the previous frame in a video sequence and if the frames match, then there will be a correlation peak. If the objects move then the peaks will move (tracking) and if something has changed dramatically in the scene, then the correlation between the two frames is lost. This forms the basis of a very powerful application for the JTC in Defense and Security. Any change in the scene can be recorded and with the inherent shift invariance property of the correlator, any movement of the objects in the scene can also be detected. A major limitation of the JTC is its intolerance to rotation and scale changes in images. The strength of the Correlation signal decreases as the input object rotates or varies in scale relative to the reference object. We have designed a binary phase only filter using the direct binary search algorithm for rotation invariant pattern recognition to be implemented on a JTC and compared to a classical synthetic discriminant function (SDF) filter. Results show that the performance of the DBS filter is better than the SDF filter.

关键词： image filtering Optical correlators Binary data Spatial light modulators Optical filters Phase only filters Virtual colonoscopy Fourier transforms image processing Video

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Subsurface interface detection using bispectral features and ground penetrating radar

Subsurface interface detection using bispectral features and...

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Proceedings of the International conference on Antennas, Radar, and Wave Propagation

作者： Strange, Andrew D. Chandran, Vinod Ralston, Jonathon C. Image and Video Research Lab Queensland University of Technology 2 George St Brisbane QLD 4001 Australia CSIRO Exploration and Mining Technology Court Pullenvale QLD 4069 Australia

ISBN: (纸本)0889864160

The detection of coal/shale interfaces close to the surface using ground penetrating radar (GPR) is difficult because the echo is drowned in unwanted signal components. These components are susceptible to changes in amplitude and cannot be removed well by standard pre-processing techniques. Second order correlation based methods such as matched filtering fail to locate the echo reliably under these conditions. Features that extract signal shape information based on the bispectrum can provide desirable immunity from clutter and noise. These features are investigated for this task using signals obtained from finite difference time domain simulations of a typical coal seam. It is shown that a pattern recognition approach using features derived from the bispectrum allows the detection of layer interfaces using GPR to be extended reliably to the near surface region.

关键词： remote sensing

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