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

共 707 条记录，以下是301-310 订阅

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Connectivity constraint-based sequential pattern extraction from Satellite image Time Series

Connectivity constraint-based sequential pattern extraction ...

引用

conference on image and Signal processing for remote sensing XIX

作者： Julea, Andreea Meger, Nicolas Inst Space Sci POB MG 23 RO-077125 Bucharest Romania Univ Savoie Polytech Annecy Chambery LISTIC Lab F-74944 Annecy Le Vieux France

ISBN: (纸本)9780819497611

The temporal evolution of pixel values in Satellite image Time Series (SITS) is considered as criterion for the characterization, discrimination and identification of terrestrial objects and phenomena. Due to the exponential behavior of sequences number with specialization, Sequential Data Mining (SDM) techniques need to be applied. The huge search and solution spaces imply the use of constraints according to the user's knowledge, interest and expectation. The spatial aspect of the data was taken into account by the introduction of connectivity measures that characterize the pixels tendency to form objects. These measures can highlight stratifications in data structure, can be useful for shape recognition and offer a base for post-processing operations similar to those from mathematical morphology (dilation, erosion etc.). The conjunction of corresponding Connectivity Constraints (CC) with the Support Constraint (SC) leads to the extraction of Grouped Frequent Sequential patterns (GFSP), a concept with proved capability for preliminary description and localization of terrestrial events. This work is focused on efficient SITS extraction of evolutions that fulfill SC and CC. Different types of extractions using anti-monotone constraints are analyzed. Experiments performed on two interferometric SITS are used to illustrate the potential of the approach to find interesting evolution patterns.

关键词： Sequential Data Mining Satellite images Time Series (SITS) Grouped Frequent Sequential patterns anti-monotone constraints shape recognition

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image FUSION USING SPATIAL UNMIXING

IMAGE FUSION USING SPATIAL UNMIXING

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International conference on Signal processing, image processing and pattern recognition (ICSIPR)

作者： Arivazhagan, S. Anisha, J. Praislin Mepco Schlenk Engn Coll Sivakasi India

ISBN: (纸本)9781467348621;9781467348614

In remote sensing applications, image acquired from space borne satellites are of diverse spatial, spectral and temporal resolutions. Several situations in image interpretation require high spatial information and high spectral information in a single image. But the existing sensors do not have the ability to provide such information either by design or because of observational constraints. Thus the complementary information from different sensors are integrated using the technique called image fusion to get a resultant fused image which is more informative than any of the given input images. The objective of this work is to perform image fusion on a high spatial resolution LISS IV image (with low spectral resolution) and a high spectral resolution LISS In image (with low spatial resolution) to obtain a fused image with high spatial resolution and high spectral resolution. To find the endmember proportions of the mixed pixels, the land cover map is obtained by performing unsupervised soft classification on the high spatial resolution LISS IV image. The land cover class proportions thus obtained is downscaled to get the land cover class proportions at LISS iiI scale. The unmixing algorithm used here is spatial unmixing wherein the high spectral resolution LISS In image is processed using a sliding window approach to obtain the endmember spectra. Thus the pixels of the fused image are obtained as the linear combination of endmembers derived from the LISS iiI weighted by the land cover class proportions of LISS IV.

关键词： image Fusion LISS iiI (Linear Imaging and Self Scanning Sensor) LISS IV spatial unmixing endmember

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Building Detection in Aerial images Based on Watershed and Visual Attention Feature Descriptors

Building Detection in Aerial Images Based on Watershed and V...

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10th International conference on Computer and Robot Vision (CRV)

作者： Cretu, Ana-Maria Payeur, Pierre Univ Quebec Outaouais Dept Comp Sci & Engn Gatineau PQ Canada Univ Ottawa Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9780769549835

This paper investigates a novel solution for the recognition of objects of interest in aerial images. The solution builds on a combination of algorithms inspired from the human visual system with classical and modern algorithms. The goal is to achieve intelligent and powerful approaches that allow for fast and automatic treatment of complex images. The methodology that is proposed innovatively combines a variation of the classical watershed segmentation algorithm with a series of feature descriptors derived from a computational model of visual attention. The feature descriptors are tuned with a machine learning approach for the task of detecting buildings in aerial images. The experimental evaluation that is conducted demonstrates that objects recognition with features derived from human visual attention performs better than when only traditional features, such as statistical texture descriptors and shape descriptors, are used. As well, the proposed solution obtains better classification rates than those reported on image processing-based recognition of buildings in the remote sensing literature.

关键词： aerial images feature descriptors visual attention object recognition segmentation remote sensing

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Multiple Classifiers Systems with Granular Neural Networks

Multiple Classifiers Systems with Granular Neural Networks

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IEEE International conference on Signal processing, Computing and Control (ISPCC)

作者： Kumar, D. Arun Meher, Saroj K. Indian Stat Inst Syst Sci & Informat Unit Bangalore 560059 Karnataka India

ISBN: (纸本)9781467361880;9781467361903

Hybridization of neural networks and fuzzy sets has proved its efficiency in solving different pattern classification tasks, which led to the development of granular neural networks (GNNs). GNN works with the principles of granular computing and basically operates on granules of information. The present paper proposes an efficient multiple classifier system (MCS) framework with different guiding rules based GNNs. The performance of the proposed MCS is demonstrated and its superiority over individual GNNs is justified with remote sensing data for five land use/cover classes. Conventional back propagation algorithm is used to train the networks.

关键词： pattern recognition neural network granular neural network remote sensing image land cover classification

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Implementation of image Fusion algorithm using MATLAB (LAPLACIAN PYRAMID)

Implementation of Image Fusion algorithm using MATLAB (LAPLA...

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IEEE International Multi conference on Automation, Computing, Control, Communication and Compressed sensing (iMac4s)

作者： Pradeep, M. Shri Vishnu Engn Coll Women ECE Dept Bhimavaram India

ISBN: (纸本)9781467350907;9781467350891

This paper represents a approach to implement image fusion algorithm ie LAPLACIAN PYRAMID. In this technique implements a pattern selective approach to image fusion. The basic idea is to perform a pyramid decomposition on each source image and finally reconstruct the fused image by performing an inverse pyramid transform. It offers benefits like resolution, S/N ratio and pixel size. The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging Based on this technique finally it reconstructs the fused image from the fused pyramid.

关键词： image fusion Laplacian Pyramid image fusion system

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Proceedings of the IASTED International conference on Signal processing, pattern recognition and Applications, SPPRA 2013

Proceedings of the IASTED International Conference on Signal...

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IASTED International conference on Signal processing, pattern recognition and Applications, SPPRA 2013

The proceedings contain 59 papers. The topics discussed include: on the sampling theorem in Hilbert spaces;quantization for maximal preservation of information;precise yarn segmentation of fabric images via active grid;blur invariants and projection operators;optimal sampling of PSK signals in mutual time delay estimation problem;entropy constrained dictionary learning for remote sensing image compression;object recognition in cluttered environments;a novel and automated circle pattern recognition technique for infra-red stereo camera calibration;design of quasi-equiripple iiR filters with prescribed flatness and approximately linear phase;construction of perfect space-time codes;rapid visual tracking with modified on-line boosting and template matching;diagnosis and classification of systolic murmur in newborns;and a sign-logo image search & combination system by analyzing color and shape features.

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remote respiratory sensing with an infrared camera using the Kinect™ infrared projector

Remote respiratory sensing with an infrared camera using the...

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2013 International conference on image processing, Computer Vision, and pattern recognition, IPCV 2013, at WORLDCOMP 2013

作者： Loblaw, Andrew Nielsen, John Okoniewski, Michal Ali Lakhani, Mazhar Department of Electrical and Computer Engineering University of Calgary CalgaryAB Canada

ISBN: (纸本)1601322526

The need for an inexpensive and portable remote respiratory monitor is in particular demand in a hospital setting as the respiratory rate provides early warning for cardiorespiratory arrest. This paper proposes an inexpensive infrared (IR) camera combined with the Microsoft Kinect™ IR projector as a low-cost module for accurate measurement of respiratory rate. The IR camera utilizes a background subtraction algorithm to obtain the respiratory information of a patient in a bed. Issues with ill-defined feature points for the background subtraction algorithm are overcome by using the Kinect™ IR projector. The IR camera can easily detect the subtle respiratory motion of a prone or side-sleeping patient, even under covers. The IR camera system is experimentally validated in a home scenario as well as with a respiratory mannequin at the Foothills hospital in Calgary. © 2013 CSREA Press. All rights reserved.

关键词： Hospitals

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SURVEY OF image SEGMENTATION ALGORITHMS ON ULTRASOUND MEDICAL imageS

SURVEY OF IMAGE SEGMENTATION ALGORITHMS ON ULTRASOUND MEDICA...

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International conference on pattern recognition, Informatics and Mobile Engineering (PRIME)

作者： Sridevi, S. Sundaresan, M. Bharathiar Univ Dept Informat Technol Coimbatore 641046 Tamil Nadu India

ISBN: (纸本)9781467358453;9781467358439

Visual information plays an important role in almost all areas our life. Nowadays, much of this information is represented and processed digitally. image processing is ubiquitous, with the applications ranging from Television ( TV) to Computed Tomography, from Photography to Printing, from Robotics to remote sensing and medical applications. Ultrasound is one of the techniques for medical application. It is used for clinical usage. The most important technologies include transducers, beam forming, contrast agents, pulse compression techniques for measuring blood flow and three-dimensional imaging. Ultrasound imaging provides valuable imaging modality to provide the following functionalities as ease of use, low cost, safe, non invasive and fast examination. Ultrasound imaging system provides effective way for examining the various tissues of human body including thyroid, breast, abdominal organs, heart, muscles, tendons, arteries and veins. This paper aims to present on current segmentation algorithms used for medical images. Each and every type of algorithm is discussed as well as their main application fields identified. Experiments that apply the algorithms to segment ultrasound images are presented to further evaluate their behaviour.

关键词： Ultrasound image processing Segmentation Ultra-Sonography Similarity B-mode images Thresholding Radiofrequency

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A Survey on Texture Classification Techniques

A Survey on Texture Classification Techniques

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International conference on Information Communication and Embedded Systems (ICICES)

作者： Raju, Jisy Durai, C. Anand Deva Karunya Univ Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781467357869;9781467357876

In image processing, regular repetition of an element is known as texture. Texture classification is a process of assigning an unknown texture to a known set of texture class. The real applications of texture classification are remote sensing, medical imaging, industrial inspection and pattern recognition. In most of the developed texture classification, the resulting classification accuracy is highly affected by random noise. And also most of the presented approaches use either local texture features or both local and global features. Extracting texture features that is rotation-invariant, insensitive to noise and classification accuracy is still a challenge. This survey comprised with a brief overview of the most common classification techniques, and a comparison between them. It discusses the various feature extraction methods.

关键词： Local binary pattern (LBP) dominant neighborhood structure (DNS) Gray Level Aura Matrix (GLAM) Completed local binary pattern (CLBP) and rotation invariance

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Dynamic monitoring technique for remote-sensing image of invasive alien plant species

Dynamic monitoring technique for remote-sensing image of inv...

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2013 International conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2013

作者： Hesen, Rabigul Jia, Zhenhong Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

ISBN: (纸本)9783642419072

Invasive Alien Plant Species (IAPS) could seriously affect the local ecosystem balance, and pose a threat to the ecological security. In order to effectively monitor and control invasive alien plants, it needs to monitor the spatial distribution and dynamic changes of IAPS. It will cite the method of nonsubsampled contourlet transform (NSCT) combination with fuzzy C-means clustering algorithm in this paper, which is used in remote sensing image change detection, for monitoring the dynamic changes of biological invasive species in this paper, to get a better detection result. © Springer-Verlag Berlin Heidelberg 2013.

关键词： image enhancement

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