2010 Western New York Image Processing Workshop最新文献_第2页

Applying computer vision techniques to perform semi-automated analytical photogrammetry 应用计算机视觉技术进行半自动分析摄影测量

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649777

David Nilosek, C. Salvaggio

引用次数: 3

A framework for object class recognition with no visual examples 一个对象类识别的框架，没有可视化的例子

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649768

Grigorios Tsagkatakis, A. Savakis

引用次数: 1

Propagating multi-class pixel labels throughout video frames 在整个视频帧中传播多类像素标签

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649773

Albert Y. C. Chen, Jason J. Corso

引用次数: 87

Jab to RGB multidimensional lookup tables 转而使用RGB多维查找表

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649757

M. Maltz, Iris Zhao

引用次数: 0

Anchor point selection by KL-divergence 通过kl散度选择锚点

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649752

Charles Parker

引用次数: 2

Calibration of a multi-projector system for display on a cylindrical surface 校正在圆柱形表面上显示的多投影仪系统

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649778

Brandon B. May, N. Cahill, M. Rosen

引用次数: 3

Utilizing the graph modularity to blind cluster multispectral satellite imagery 利用图模块性对多光谱卫星图像进行盲聚类

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649737

Ryan A. Mercovich, A. Harkin, D. Messinger

{"title":"Utilizing the graph modularity to blind cluster multispectral satellite imagery","authors":"Ryan A. Mercovich, A. Harkin, D. Messinger","doi":"10.1109/WNYIPW.2010.5649737","DOIUrl":"https://doi.org/10.1109/WNYIPW.2010.5649737","url":null,"abstract":"The fully automatic separation of spectral image data into clusters is a problem with a wide variety of desired and potential solutions. Contrary to the typical approaches of utilizing the first order statistics and Gaussian modeling of spectral image data, the method described in this paper utilizes the spectral data structure to generate a graph representation of the image and then clusters the data by applying the method of optimal modularity for finding communities within the graph. After defining and identifying pixel adjacencies to represent an image as an adjacency matrix, a quantity known as the graph modularity is maximized to split the data into groups of spectrally similar pixels. Recursion with the subgroups created in each split creates the data clustering. The groups where the maximal modularity is not above a given threshold are not split, resulting in a stopping condition and an estimation of the number of clusters necessary to fully describe the data. By ignoring any reliance on assumptions of the shape of the data, this method excels in regions where typical clustering methods fail, such as high resolution urban scenes with very high clutter and regions with subtle variability like coastal bodies of water.","PeriodicalId":210139,"journal":{"name":"2010 Western New York Image Processing Workshop","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114447900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A euclidean distance transformation for improved anomaly detection in spectral imagery 一种改进光谱图像异常检测的欧氏距离变换

2010 Western New York Image Processing Workshop Pub Date : 2010-12-03 DOI: 10.1109/WNYIPW.2010.5649762

A. Schlamm, D. Messinger

{"title":"A euclidean distance transformation for improved anomaly detection in spectral imagery","authors":"A. Schlamm, D. Messinger","doi":"10.1109/WNYIPW.2010.5649762","DOIUrl":"https://doi.org/10.1109/WNYIPW.2010.5649762","url":null,"abstract":"Remotely sensed spectral imagery is used in many disciplines, including environmental monitoring, agricultural health, defense and security applications, astronomy, medical imaging, and food quality assessment. The basic tasks performed within any of these fields are target or anomaly detection, classification or clustering, change detection, and physical parameter estimation. Hyperspectral image (HSI) analysis in any field often involves mathematically transforming the raw data into a new space using Principal Components Analysis (PCA) or similar techniques. The dimensionality of this new space is usually smaller than the collected space and as a result reduces computation time of subsequent algorithms. Additionally and more importantly, the results of standard algorithms may perform better in this new, uncor-related space. Many of the currently used transformations in HSI analysis are statistical in nature and therefore place Gaussian or similar assumptions on the data distribution. These assumptions work well with remote sensing imagery with low spectral and/or spatial resolution. In low resolution imagery, each pixel is a mixture of many materials and the data distribution is often sufficiently represented by statistical distributions. However, as the current generation sensors typically have higher spatial and/or spectral resolution, the complexity of the data collected is increasing and these assumptions are no longer adequate. As a result, algorithms based on these statistical transformations do not necessarily provide improved results on modern datasets. A new, data driven, mathematical transformation is presented as a preprocessing step for HSI analysis. Termed the Nearest Neighbor Transformation, this new transformation does not rely on placing assumptions on the data and may improve analytical results from standard HSI anomaly detection algorithms.","PeriodicalId":210139,"journal":{"name":"2010 Western New York Image Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3