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multiresolution segmentation OF RESPIRATORY ELECTROMYOGRAPHIC SIGNALS

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1994年第3期41卷 257-266页

作者： CHOI, HG PRINCIPE, JC HUTCHINSON, AA WOZNIAK, JA UNIV FLORIDA COLL MEDDEPT PEDIATGAINESVILLEFL 32610

Analysis of respiratory electromyographic (EMG) signals in the study of respiratory control requires the detection of burst activity from background(signal segmentation), and focuses upon the determination of onset and cessation points of the burst activity (boundary estimation). This paper describes a new automated multiresolution technique for signal segmentation and boundary estimation;During signal segmentation;a new transitional segment is defined which contains the boundary between background and burst activity. Boundary, estimation is then performed within this transitional segment. Boundary candidates are selected and a probability is attributed to each candidate, using an artificial neural network. The final boundary for a given transitional segment is the boundary estimate with the maximum a posteriori probability. This new method has proved accurate when compared to boundaries chosen by two investigatiors.

关键词： bioelectric potentials medical signal processing muscle artificial neural network automated multiresolution technique boundary candidates boundary estimation burst activity detection medical signal analysis multiresolution segmentation respiratory electr muscles medical signal processing Boundary Signal analysis Maximum a posteriori estimation signals Bioelectric potentials respiration control

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multiresolution segmentation adapted for object-based change detection

Multiresolution segmentation adapted for object-based change...

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Conference on Image and Signal Processing for Remote Sensing XVI

作者： Listner, Clemens Niemeyer, Irmgard Forschungszentrum Juelich GmbH Inst Energy Res IEF Safety Res & Reactor Technol 6 D-52425 Julich Germany

ISBN: (纸本)9780819483478

In object-based change detection approaches using specified object features as change measures, segmentation is the crucial step, especially when also shape changes are considered. In this paper we present an enhanced segmentation procedure based on the multiresolution segmentation. The procedure segments the first image using the multiresolution segmentation. The segmentation is then applied to the second image and checked for its consistency. If a segment is found to be inconsistent with the second image, it is split up. The performance of the proposed procedure is demonstrated based on simulated and real image data.

关键词： Change detection Multivariate Alteration Detection (MAD) object-based image analysis multiresolution segmentation

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multiresolution segmentation FOR EXTRACTING PLASTIC GREENHOUSES FROM DEIMOS-2 IMAGERY 24

MULTIRESOLUTION SEGMENTATION FOR EXTRACTING PLASTIC GREENHOU...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Jimenez-Lao, Rafael A Aguilar, Manuel Ladisa, Claudio J Aguilar, Fernando Nemmaoui, Abderrahim Univ Almeria Dept Engn Almeria 04120 Spain Univ Almeria Res Ctr CIAIMBITAL Almeria 04120 Spain Politecn Bari DICATECh Via Orabona 4 I-70125 Bari Italy

Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almeria). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results.

关键词： Plastic Covered Greenhouse multiresolution segmentation VHR Satellite OBIA Deimos-2

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Land use/cover classification through multiresolution segmentation and object oriented neural networks classification

Land use/cover classification through multiresolution segmen...

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Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI

作者： Rocha, Jorge Tenedorio, Jose A. Encarnacao, Sara Morgado, Paulo Ctr Estudos Geog Alameda Univ P-1600214 Lisbon Portugal Ctr Estudos Geog & Planeamento Reg E GEO P-1069061 Lisbon Portugal

ISBN: (纸本)0819464619

In this paper is presented a land use/cover classification methodology of the rural/urban fringe, by means of the application of a neuronal network, with resource to the multiresolution image segmentation, construction of complex elements through object oriented analysis and integration of not spectral (ancillary) information. The study area is the municipality of Almada, located in the south bank of Tagus River and corresponding to one of the core regions of the Lisbon Metropolitan Area (Portugal). The data used was 2004 HRVIR SPOT images fused with supermode panchromatic image and the Portuguese urban quarter statistical data. The developed procedure is based in five steps: 1) Legend creation;2) deriving statistical ancillary data;3) deriving object (texture) data;4) deriving spectral data and 5) neural network classification.

关键词： land use/cover object oriented neural networks classification multiresolution segmentation ancillary data

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INFORMATION EXTRACTION OF HIGH-RESOLUTION REMOTELY SENSED IMAGE BASED ON multiresolution segmentation

INFORMATION EXTRACTION OF HIGH-RESOLUTION REMOTELY SENSED IM...

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ISPRS/IGU/ICA Joint International Workshop on Borderlands Modelling and Understanding for Global Sustainability

作者： Shao Peng Yang Guodong Niu Xuefeng Zhang Xuqing Zhan Fulei Tang Tianqi Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130026 Jilin Province Peoples R China

ISBN: (纸本)9781629935201

The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Further, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicates that edge-detection make a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% through confusion matrix.

关键词： Edge-detection Object-oriented multiresolution segmentation Spectral features Geometrical features Confusion matrix

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A region-based image fusion algorithm using multiresolution segmentation

A region-based image fusion algorithm using multiresolution ...

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6th IEEE International Conference on Intelligent Transportation Systems

作者： Li, ZH Jing, ZL Liu, G Sun, SY Leung, H Shanghai Jiao Tong Univ Sch Elect & Informat Technol Inst Aerosp Informat & Control Shanghai 200030 Peoples R China

ISBN: (纸本)0780381254

This paper describes a region-based multiresolution (MR) image fusion algorithm. The MR. segmentations of source images to be fused are obtained by fuzzy c-means clustering. From these MR region representations of all the input images, we obtain a shared MR region representation to label all the input images. The fusion process is guided by these MR region representations. Experiment results, using. real data show that the proposed algorithm works well,in multisensor image fusion.

关键词： image fusion multiresolution decomposition multiresolution reconstruction multiresolution segmentation

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APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR multiresolution segmentation: A CASE STUDY IN A RIPARIAN MIXED FOREST

APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTI...

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International Conference on Sensors and Models in Remote Sensing and Photogrammetry

作者： Dabiri, Z. Hoelbling, D. Lang, S. Bartsch, A. Salzburg Univ Interfac Dept Geoinformat Z GIS A-5020 Salzburg Austria Zent Anstalt Meteorol & Geodynam A-1190 Vienna Austria

ISBN: (纸本)9781629935201

The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.

关键词： TerraSAR-X object based image analysis (OBIA) multiresolution segmentation estimation of scale parameter (ESP)

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Optimization of multiresolution segmentation by using a genetic algorithm

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JOURNAL OF APPLIED REMOTE SENSING 2012年第1期6卷 063592-063592页

作者： Nikfar, Maryam Zoej, Mohammad Javad Valadan Mohammadzadeh, Ali Mokhtarzade, Mehdi Navabi, Afshin KNTU Tehran Iran Farand Co Tehran Iran

Most traditional pixel-based analyses are based on the digital number of each pixel. Whereas images can provide more details such as color, size, shape, and texture, object-oriented processing is more advantageous. multiresolution segmentation, which was proposed by Baatz and Schape, is one of the most powerful segmentation algorithms. On the other hand, meaningful segmentation is the most important issue in object-oriented processing. Currently, meaningful segmentation, which is recommended by Baatz's multiresolution segmentation approach, is a trial-and-error task that is very tedious and time consuming. Therefore, a genetic algorithm (GA) is used for finding optimal parameters of Baatz's multiresolution segmentation approach for three building groups' meaningful segmentation. The optimal parameters are found by GA and its generality has been evaluated on a simulated image as well as some IKONOS and GeoEye image patches. The evaluations show the efficiency of GA for finding optimal multiresolution segmentation parameters for meaningful segmentation of the simulated image and the three groups of building images. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/***.6.063592]

关键词： multiresolution segmentation building meaningful segments genetic algorithm buildings

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multiresolution Gauss-Markov random field models for texture segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1997年第2期6卷 251-267页

作者： Krishnamachari, S Chellappa, R UNIV MARYLAND DEPT ELECT ENGNCOLLEGE PKMD 20742 UNIV MARYLAND CTR AUTOMAT RESCOLLEGE PKMD 20742

This paper presents multiresolution models for Gauss-Markov random fields (GMRF's) with applications to texture segmentation, Coarser resolution sample fields are obtained by subsampling the sample field at fine resolution. Although the Markov property is lost under such resolution transformation, coarse resolution non-Markov random fields can be effectively approximated by Markov fields. We present two techniques to estimate the GMRF parameters at coarser resolutions from the fine resolution parameters, one by minimizing the Kullback-Leibler distance and another based on local conditional distribution invariance. We also allude to the fact that different GMRF parameters at the fine resolution can result in the same probability measure after subsampling and present the results for first- and second-order cases. We apply this multiresolution model to texture segmentation, Different texture regions in an image are modeled by GMRF's and the associated parameters are assumed to be known. Parameters at lower resolutions are estimated from the fine resolution parameters. The coarsest resolution data is first segmented and the segmentation results are propagated upward to the finer resolution, We use the iterated conditional mode (ICM) minimization at all resolutions. Our experiments with synthetic, Brodatz texture, and real satellite images show that the multiresolution technique results in a better segmentation and requires lesser computation than the single resolution algorithm.

关键词： .Markov random random field multiresolution segmentation

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multiresolution Anisotropic Wavelet Shrinkage Image segmentation

Multiresolution Anisotropic Wavelet Shrinkage Image Segmenta...

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7th World Congress on Intelligent Control and Automation

作者： Zhu, Jingfu Huang, Fenggang Yang, Yun Harbin Engn Coll ComputerSsci & Technol Harbin Peoples R China Heilongjiang August First Land Reclamat Univ Coll Informat Technol Daqing Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781424421138

In this paper, we introduce an image segmentation method based on multiresolution and scale-space theory. Specifically, a nonlinear scale-space stack is constructed by anisotropic wavelet shrinkage. A tree structure is derived from the stack based on the relationships between connected components of adjacent scale layers. Then, Equivalent framework of wavelet shrinkage and anisotropic diffusion is proved. Method used to extract connected component during the procedure of constructing scale-space stack is optimized. Based on these, we propose multiresolution anisotropic wavelet shrinkage segmentation algorithm. Comparative experimental results show that proposed algorithm can keep edge features very well while coping with the interior nonuniform. Furthermore, it has some robustness to noise.

关键词： anisotropic wavelet shrinkage nonlinear diffusion equivalence scale-space multiresolution segmentation

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