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Robust Technique for Segmentation and Counting of Trees from Remotely Sensed Data

Robust Technique for Segmentation and Counting of Trees from...

引用

IEEE international Advance computing conference

作者： Vibha, L. Shenoy, P. Deepa Venugopal, K. R. Patnaik, L. M. Dr MGR Educ & Res Inst Dept Comp Sci & Engn Madras Tamil Nadu India Bangalore Univ Univ Visvesvaraya Coll Engn Dept Comp Sci & Engn Bangalore 560001 Karnataka India Def Inst Adv Technol Pune Maharashtra India

ISBN: (纸本)9781424429271

Advanced data mining technologies along with the large quantities of Remotely Sensed Imagery, provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data are typically used in order to detect the distribution of vegetation, soil classes, built-up areas, roads and water bodies such as rivers, lakes etc. The availability of new high spatial resolution satellite sensors permits people having large amounts of detailed digital imaging of rural environment. In this paper an approach towards the automatic segmentation of the satellite image into distinct regions and further to extract tree count from the vegetative area is presented. Counting trees in specific geographical areas is a very complicated process. Now a days manual counting is done by the forest department, both in agricultural as well as forest regions. Image segmentation is a very important technique in image processing. However, it is a very difficult task and there is no single unified approach for all types of images In this paper, image processing techniques have been employed for automatic segmentation of the satellite image and extraction of the trees from the segmented image.

关键词： Blob Extraction Image Processing pattern recognition Remotely Sensed Imagery Segmentation

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Gyral Folding pattern Analysis via Surface Profiling

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12th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Li, Kaiming Guo, Lei Li, Gang Nie, Jingxin Faraco, Carlos Zhao, Qun Miller, Stephen Liu, Tianming Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Dept Psychol Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Dept Phys & Astron Athens GA 30602 USA

ISBN: (纸本)9783642042676

Human cortical folding pattern has been Studied for decades. This paper proposes a gyrus scale folding pattern analysis technique via cortical surface profiling. Firstly, we sample the cortical Surface into 2D profiles and model them using power function. This step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. Secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical Surface and apply the affinity propagation Clustering algorithm to parcellate the cortex into regions with different shape patterns. Finally, a second-round surface profiling is performed on the parcellated cortical regions, and the number of hinges is detected to describe the gyral folding pattern. Experiments demonstrate that our method could successfully classify human gyri into 2-hinge, 3-hinge and 4-hinge gyri. The proposed method has the potential to significantly contribute to automatic segmentation and recognition of cortical gyri.

关键词： Clustering algorithms

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HISI: A soft framework for building recognition

HISI: A soft framework for building recognition

引用

international conference on soft computing, computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW

作者： Mariette Awad Ayman El Mobacher American University of Beirut Beirut Lebanon

In an effort to develop an interaction mechanism based on visual sensing to trigger and improve delivery of context based services and information pertinent to location in a solicited and near real time manner, we present in this paper HISI, a soft recognition approach to the processing and identification of buildings. Using a coarse joint histogram technique, an image captured by a mobile user with a cell phone is pre-processed to reduce the search space to an adaptive list of potential buildings, after which a weighted fusion of different SIFT maps identifies the building in question. Experimental results showed HISI's effectiveness with respect to other published results in the literature and motivates further extensions to HISI, as it carries expectations for commercial values to the delivery of context based services initiated by users.

关键词： Buildings Context-aware services Mobile handsets Information retrieval Histograms Cellular phones Earth Costs IEEE members Global Positioning System

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Attribute Grid Computer based on Qualitative Mapping and its application in pattern recognition

Attribute Grid Computer based on Qualitative Mapping and its...

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IEEE international conference on Granular computing (GRC)

作者： Jiali Feng College of Information Engineering Shanghai Maritime University Shanghai China

A new kind of computer, called attribute grid computer based on qualitative mapping is presented in this paper, It is shown that a series of intelligent methods, such as production system, artificial neural network, and support vector machine can be fused in the framework of qualitative criterion transformation of qualitative mapping and can be implemented by attribute grid computer. And some examples of application in pattern recognition are given too.

关键词： Grid computing Application software pattern recognition Computer networks Artificial intelligence Intelligent networks Machine intelligence Production systems Artificial neural networks Support vector machines

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Design method of morphological structural function for pattern recognition of EEG signals during motor imagery and cognition

Design method of morphological structural function for patte...

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2009 4th international conference on Innovative computing, Information and Control, ICICIC 2009

作者： Yamaguchi, Tomonari Fujio, Mitsuhiko Inoue, Katsuhiro Pfurtscheller, Gert Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka 820-8502 Japan Laboratory of Brain-Computer Interface Graz University of Technology Graz Austria

ISBN: (纸本)9780769538730

Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen (such system is called BCI). Recently, we have proposed the detection method of Error Potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from the EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function and this multi-resolution analysis can be constructed by repeating this filtering to signal while changing structural function. This method is a kind of discrete wavelet analysis with non-linear characteristics and is effective to extract specific shapes. The structural function which decides the filter characteristic is designed to obtain optimal separation based on mutual information algorithm or spectrum dividing algorithm. © 2009 IEEE.

关键词： Multiresolution analysis

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Discrete Geometry for Computer Imagery - 15th IAPR international conference, DGCI 2009, Proceedings

Discrete Geometry for Computer Imagery - 15th IAPR Internati...

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15th IAPR international conference on Discrete Geometry for Computer Imagery, DGCI 2009

ISBN: (纸本)3642043968

The proceedings contain 45 papers. The topics discussed include: arithmetic discrete planes are quasicrystals;affine connections, and midpoint formation;mathematics in atmospheric sciences: an overview;on three constrained versions of the digital circular arc recognition problem;efficient lattice width computation in arbitrary dimension;Christoffel and Fibonacci Tiles;optimal partial tiling of Manhattan polyominoes;an improved coordinate system for point correspondences of 2D articulated shapes;two linear-time algorithms for computing the minimum length polygon of a digital contour;multiscale discrete geometry;vanishing point detection with an intersection point neighborhood;ellipse detection with elemental subsets;multi-label simple points definition for 3D images digital deformable model;and marching triangle polygonization for efficient surface reconstruction from its distance transform.

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An efficient algorithm for fingerprint reference-point detection

An efficient algorithm for fingerprint reference-point detec...

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2009 IEEE-RIVF international conference on computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2009

作者： Van, Thien Hoang Le, Hoang Thai Department of Computer Sciencies Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Department of Computer Sciencies Ho Chi Minh City University of Natural Science Ho Chi Minh City Viet Nam

ISBN: (纸本)9781424445684

The reference-point played important roles in most algorithms of fingerprint recognition. It is widely used in fingerprint classification, and fingerprint matching. There are many methods proposed for reference point detection as Poincare index technique, Geometry of region technique, Direction curvature technique and Orientation-consistency-based technique. However, the experimental results show that the present methods are less accurate on the fingerprint database which contains many poor-quality fingerprint images (the most accuracy of the present methods is 93% on the fingerprint database FVC2004, DB2, set A). This paper proposes a novel algorithm to locate fingerprint reference-point consistently and accurately for all types of fingerprints. The method computes the convex orientation consistency which describes both direction of curvature and how well the orientations over a neighborhood are consistent with the dominant orientation. Experimental results show the effectiveness and superiority of our proposed method (96.9 % on the FVC2004, DB2 set A). © 2009 IEEE.

关键词： pattern matching

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Unsupervised Change Detection of Remotely Sensed Images Using Fuzzy Clustering

Unsupervised Change Detection of Remotely Sensed Images Usin...

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international conference on Advances in pattern recognition (ICAPR)

作者： Susmita Ghosh Niladri Shekhar Mishra Ashish Ghosh Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata India Machine Intelligence Unit and Center of Soft Computing Research Indian Statistical Institute Kolkata India

In this paper two fuzzy clustering algorithms, namely fuzzy C-means (FCM) and Gustafson Kessel clustering (GKC), have been used for detecting changes in multitemporal remote sensing images. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. To show the effectiveness of the proposed technique, experiments are conducted on three multispectral and multitemporal images. Results are compared with those of existing Markov random field (MRF) & neural network based algorithms and found to be superior. The proposed technique is less time-consuming and unlike MRF do not need any a priori knowledge of distribution of changed and unchanged pixels (as required by MRF).

关键词： Clustering algorithms Pixel Change detection algorithms Remote sensing Neural networks Object detection Remote monitoring Statistical distributions Context modeling Arithmetic

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Weak metric learning for feature fusion towards perception-inspired object recognition

Weak metric learning for feature fusion towards perception-i...

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16th international Multimedia Modeling conference on Advances in Multimedia Modeling, MMM 2010

作者： Li, Xiong Zhao, Xu Fu, Yun Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China NY 14260 United States

ISBN: (纸本)3642113001

With extracted local features of a given image, computing its global feature under perceptual framework has shown promising performance in object recognition. However, under some tough applications with large intra-class variance, using only one kind of local feature is inadequate to build a robust classification system. To integrate the discriminability of complementary local features, in this paper, we extend the efficacy of perceptual framework to adapt to heterogeneous features. Given multiple raw global features, we propose a fusion strategy through metric learning, which is called weak metric learning in this work, for fusing high dimensional features. The fusion model is solved with the maximal kernel canonical correlation formulation with the multiple global features as outputs. Experimental results show that our method achieves significant improvements about 5% to 11% than the benchmark perceptual framework system, HMAX, on several difficult categories of object recognition with much less training samples and feature elements. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Object recognition

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Research on Bayesian Decision Theory in pattern recognition

Research on Bayesian Decision Theory in Pattern Recognition

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international conference on Genetic and Evolutionary computing (WGEC)

作者： Hai-ming Li Ting-lei Huang Xin Wang School of Computer and Control Guilin University of Electronic and Technology China

In this paper, Bayesian decision theory is discussed. Bayesian decision related to the basic elements and the principles as well as the Bayes optimal decision criteria is introduced briefly. Its characteristics, advantages and disavantages as well as the applicable targets are analysed in this paper, in the end, the new application situation is introduced.

关键词： Bayesian methods Decision theory pattern recognition Error analysis Probability Genetics Testing Data analysis pattern analysis pattern classification

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