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

共 708 条记录，以下是381-390 订阅

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Contour-based feature extraction for image potential energy theory

Contour-based feature extraction for image potential energy ...

引用

iiTA International conference on Geoscience and remote sensing (iiTA-GRS)

作者： Xuesong Chen Xuejun Xu Cheng Wang Hongbo Zhu Shaohua Jiang Digital Engineering Simulation Center Huazhong University of Science and Technology Wuhan China

A good feature extraction method can improve the performance of pattern recognition system or classification system. The contour can better to retain the original features of the object. In target recognition system , using potential energy of contour-point projection into the plane coordinate system. The method can be better to show a contour in the structural feature. In addition, it's better avoid the matrix storage redundancy. In all energy projection method, potential energy projection is better show its superiority in the structure information, the time of consumption and the storage space. Using potential energy theory into binary image feature extraction and feature store is a new method for image processing. Potential energy is to compute the energy of each pixel the first, and store the energy in order, which is better to retain the image features, and facilitate storage and classification. Contour is one of the most important features for distinguish between two objects. The contour potential energy can be used in target recognition and target classification field and so on.

关键词： image resolution Energy resolution Support vector machines image recognition Biology

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An image segmentation method based on Type-2 fuzzy Gaussian Mixture Models

An image segmentation method based on Type-2 fuzzy Gaussian ...

引用

International conference on image Analysis and Signal processing, IASP

作者： Xu Kai Wu Fangfang Qin Kun School of Remote Sensing Information Engineering Wuhan University of China Wuhan China

This paper proposes a new image segmentation method based on Type-2 fuzzy Gaussian Mixture Models (T2 FGMMs). First, the core-region and the open-region of image are extracted according to spatial information of pixels. Then, the GMMs parameters are estimated by EM algorithm. The interval in which T2 FGMMs parameters vary is constrained by the GMMs parameters of the core-region and the open-region of image. Finally, Bayesian decision is used to realize image segmentation. In the end, the method is compared with image segmentation using Otsu's method, FCM and GMM. Experiments demonstrate the effectiveness of this method.

关键词： image segmentation Pixel Uncertainty Parameter estimation Bayesian methods Gaussian distribution Data mining Covariance matrix remote sensing pattern recognition

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Modeling multi-source remote sensing image classifier based on the MDL principle: Experimental studies

Modeling multi-source remote sensing image classifier based ...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Huaiying Xia Rukun Hu Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

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Contour-Based Feature Extraction for image Potential Energy Theory

Contour-Based Feature Extraction for Image Potential Energy ...

引用

2010 Second iiTA International conference on Geoscience and remote sensing (iiTA-GRS 2010)

作者： Xuesong Chen,Xuejun Xu,Cheng Wang,Hongbo Zhu,Shaohua Jiang Digital Engineering Simulation Center Huazhong University of Science & Technology Wuhan,China

A good feature extraction method can improve the performance of pattern recognition system or classification *** contour can better to retain the original features of the *** target recognition system,using potential energy of contour-point projection into the plane coordinate *** method can be better to show a contour in the structural *** addition,it's better avoid the matrix storage *** all energy projection method,potential energy projection is better show its superiority in the structure information,the time of consumption and the storage *** potential energy theory into binary image feature extraction and feature store is a new method for image *** energy is to compute the energy of each pixel the first,and store the energy in order,which is better to retain the image features,and facilitate storage and *** is one of the most important features for distinguish between two *** contour potential energy can be used in target recognition and target classification field and so on.

关键词： image processing contour potential energy feature extraction target recognition

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A CO-GAUSSIAN PROCESS BASED FRAMEWORK FOR remote sensing image CHANGE DETECTION

A CO-GAUSSIAN PROCESS BASED FRAMEWORK FOR REMOTE SENSING IMA...

引用

IEEE International conference on Acoustics, Speech, and Signal processing

作者： Keming Chen Zhenglong Li Jian Cheng Zhixin Zhou Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China 100190 Beijing Institute of Remote Sensing Beijing China 100854

ISBN: (纸本)9781424442959

Inspired by the idea of co-training algorithm, in this paper we propose a novel semi-supervised learning algorithm, co-Gaussian Process (co-GP), under a Bayesian framework. image data are characterized in two distinct views, i.e. two disjoint feature sets. A latent function with a GP prior is employed for each view. In learning process of co-GP, knowledge acquired in each view is transferred by probabilistic labels to the other in turns to enhance learning effect. In this manner, proper parameters are estimated in a bootstrap mode and a satisfying performance can be maintained with only small amount of labeled data. The experiments carried out on multitemporal images validate the proposed algorithm.

关键词： Gaussian Process co-training change detection remote sensing remote sensing Gaussian processes images Frameworks Learning processes Turning View

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Road Change Detection from Multi-Spectral Aerial Data

Road Change Detection from Multi-Spectral Aerial Data

引用

International conference on pattern recognition

作者： Adriano Mancini Emanuele Frontoni Primo Zingaretti Università Politecnica delle Marche Ancona Italy

The paper presents a novel approach to automate the Change Detection (CD) problem for the specific task of road extraction. Manual approaches to CD fail in terms of the time for releasing updated maps; in the contrary, automatic approaches, based on machine learning and image processing techniques, allow to update large areas in a short time with an accuracy and precision comparable to those obtained by human operators. This work is focused on the road-graph update starting from aerial, multi-spectral data. Georeferenced, ground data, acquired by a GPS and an inertial sensor, are integrated with aerial data to speed up the change detector. After roads extraction by means of a binary AdaBoost classifier, the old road-graph is updated exploiting a particle filter. In particular this filter results very useful to link (track) parts of roads not extracted by the classifier due to the presence of occlusions (e.g., shadows, trees).

关键词： Roads Data mining Training Satellites image resolution remote sensing Global Positioning System

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Cluster-based training data preselection and classification for remote sensing images

Cluster-based training data preselection and classification ...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Xiaoyong Bian Tianxu Zhang Zheng Fang Yuxia Sheng Xiaolong Zhang Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

In classical image classification approaches, it assumes that there are a number of labeled training data per class. In real applications, labeled data generally are difficult to obtain while unlabeled data are sufficient and helpful to improve the accuracy of classifier. Bipartition based clustering method is to generate better initial cluster centers and to preselect representative data samples from each cluster region with given area under clustering model. To attack the quantity and quality problems of training samples, we propose a Cluster-based Classification Algorithm (CCA) for remote sensing images, and different data samples selection methods are evaluated. Using this approach, the confident unlabeled data both cluster centroid and the ones nearest to the centroid are labeled as training data and extracted. SVM can subsequently be trained with the labeled dataset. The conducted experiments by clustering and classification on real remote sensing images have validated the proposed approach.

关键词： Classification algorithms Clustering algorithms remote sensing Training data Support vector machines Accuracy Data mining

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Sub-manifold distance based object recognition in clutter

Sub-manifold distance based object recognition in clutter

引用

International conference on Natural Computation (ICNC)

作者： Hua Zhou Chao Cai Mingyue Ding Institute of Pattern Recognition and Artificial Intelligence National Laboratory of Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan China School of Life Science and Technology Imaging Processing and Intelligence Control Key Laboratory of Education Ministry Huazhong University of Science and Technology Wuhan China

Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manifold learning algorithms are developed for discovering intrinsical features. Selective visual attention mechanism provides tools to reduce computation cost and avoid the influence of clutter background. In this paper, we described a new object recognition method, the submanifold distance (SMD) algorithm, which is induced by the visual attention mechanism to provide complex object recognition. Experiments with airport remote sensing images illustrated that our proposed algorithm can recognize complex objects accurately, robustly and quickly.

关键词： Manifolds Visualization Airports Clutter Target recognition image recognition

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Human Visual System based processing for High Resolution remote sensing image Segmentation

Human Visual System based Processing for High Resolution Rem...

引用

2010 2nd International conference on Signal processing System(2010年信号处理系统国际会议 ICSPS 2010)

作者： Guizhou Wang Guojin He Center for Earth Observation and Digital Earth Chinese Academy of Sciences Graduate University of Ch Center for Earth Observation and Digital Earth Chinese Academy of Sciences Beijing China

image segmentation is very essential and critical to image processing and pattern recognition. Watershed is the most popular one among all the proposed image segmentation algoritbms, but it suffers from over-segmentation. To resolve the over-segmentation problem and obtain a concise region representation has been the focus of many researchers. There are many ways to reduce the over-segmentation However, Human visual system (HVS) is often not incorporated into the consideration and process of *** paper presents a new approach to high resolution remote sensing image segmentation taking into consideration human visual system (HVS) model. A Contrast Sensitivity Function (CSF) based filtering is applied to the image before watershed trAnsform. Multi-scale spatial frequency filtering images are derived from setting multi-scale viewing distance. Then the region merging based on Just Noticeable Difference (JND) is applied to the segmentation results. Finally, the efffect of reducing over segmentation based on CSF and JND is analyzed and significant improvement is reported in the experimental results.

关键词： human visual system CSF muki-scale filtering JND watershed transform

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VARIATIONAL MODEL-BASED 3D BUILDING EXTRACTION FROM remote sensing DATA

VARIATIONAL MODEL-BASED 3D BUILDING EXTRACTION FROM REMOTE S...

引用

16th IEEE International conference on image processing

作者： Karantzalos, Konstantinos Paragios, Nikos Ecole Cent Paris Lab Math Appl Syst Chatenay Malabry France

ISBN: (纸本)9781424456536

In this paper, we introduce a variational framework towards automatic 3D building reconstruction from optical and Lidar data. Multiple 3D competing building priors are considered under a recognition-driven way. These models, under a certain hierarchical representation, describe the space of solutions and under a fruitful synergy with an inferential procedure recover the observed scene's geometry. Our formulation allows the cue with the higher spatial resolution to constrain properly the boundaries detection procedure ensuring, in this way, optimal results in terms of accuracy. Such an integrated approach is defined in a variational context, solves segmentation in both spaces, addresses fusion in a natural manner and allows multiple competing priors to determine the pose and 3D geometry from the observed data. Very promising experimental results demonstrate the potentials of our approach.

关键词： pattern recognition Variational Methods Object Detection Segmentation Competing Priors

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