2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献_第3页

Vessel detection in video with dynamic maritime background 动态海事背景视频中的船舶检测

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528222

Michael T. Chan, C. Weed

引用次数: 3

Template matching localization for GPS denied environments GPS拒绝环境的模板匹配定位

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528208

Steven C. Rowe, Pritpaul Mahal, Lucas Burkowski, G. Beach, C. Cohen

引用次数: 0

Obtaining accurate change detection results from high-resolution satellite sensors 从高分辨率卫星传感器获得准确的变化检测结果

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528199

N. Bryant, W. Bunch, R. Fretz, P. Kim, T. Logan, M. Smyth, A. Zobrist

{"title":"Obtaining accurate change detection results from high-resolution satellite sensors","authors":"N. Bryant, W. Bunch, R. Fretz, P. Kim, T. Logan, M. Smyth, A. Zobrist","doi":"10.1109/AIPR.2012.6528199","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528199","url":null,"abstract":"Multi-date acquisitions of high-resolution imaging satellites (e.g. GeoEye and WorldView), can display local changes of current economic interest. However, their large data volume precludes effective manual analysis, requiring image co-registration followed by image-to-image change detection, preferably with minimal analyst attention. We have recently developed an automatic change detection procedure that minimizes false-positives. The processing steps include: (a) Conversion of both the pre- and post- images to reflectance values (this step is of critical importance when different sensors are involved); reflectance values can be either top-of-atmosphere units or have full aerosol optical depth calibration applied using bi-directional reflectance knowledge. (b) Panchromatic band image-to-image co-registration, using an orthorectified base reference image (e.g. Digital Orthophoto Quadrangle) and a digital elevation model; this step can be improved if a stereo-pair of images have been acquired on one of the image dates. (c) Pan-sharpening of the multispectral data to assure recognition of change objects at the highest resolution. (d) Characterization of multispectral data in the post-image (i.e. the background) using unsupervised cluster analysis. (e) Band ratio selection in the post-image to separate surface materials of interest from the background. (f) Preparing a pre-to-post change image. (g) Identifying locations where change has occurred involving materials of interest.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126137636","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: 2

A new approach to graph analysis for activity based intelligence 基于活动智能的图形分析新方法

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528204

William Raetz

引用次数: 3

Future image frame generation using Artificial Neural Network with selected features 未来的图像帧生成使用人工神经网络选定的特征

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528189

N. Verma

{"title":"Future image frame generation using Artificial Neural Network with selected features","authors":"N. Verma","doi":"10.1109/AIPR.2012.6528189","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528189","url":null,"abstract":"This paper presents a novel approach for the generation of Future image frames using Artificial Neural Network (ANN) on spatiotemporal framework. The input to this network are hyper-dimensional color and spatiotemporal features of every pixel of an image in an image sequence. Principal Component Analysis, Mutual Information, Interaction Information and Bhattacharyya Distance measure based feature selection techniques have been used to reduce the dimensionality of the feature set. The pixel values of an image frame are predicted using a simple ANN back propagation algorithm. The ANN network is trained for R, G and B values for each and every pixel in an image frame. The resulting model is successfully applied on an image sequence of a landing fighter plane. As Mentioned above four feature selection techniques are used to compare the performance of the proposed ANN model. The quality of the generated future image frames is assessed using, Canny edge detection based Image Comparison Metric(CIM) and Mean Structural Similarity Index Measure(MSSIM) image quality measures. The proposed approach is found to have generated six future image frames successfully with acceptable quality of images.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423603","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: 12

Automatic ship hull inspection using fuzzy logic 基于模糊逻辑的船体自动检测

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528214

J. Contreras, William Cuadrado, David Muñoz, George Archbold, Geraldine Delgado, V. Diaz

引用次数: 2

Registration of region of interest for object tracking applications in wide area motion imagery 广域运动图像中目标跟踪应用的兴趣区域注册

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528202

K. Jackovitz, V. Asari, E. Balster, J. Vasquez, P. Hytla

引用次数: 10

Image search system 图像搜索系统

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528193

P. Cho, Michael Yee

引用次数: 1

Distributed adaptive spectral and spatial sensor fusion for super-resolution classification 分布式自适应光谱与空间传感器融合的超分辨分类

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528194

T. Khuon, R. Rand, J. Greer, E. Truslow

{"title":"Distributed adaptive spectral and spatial sensor fusion for super-resolution classification","authors":"T. Khuon, R. Rand, J. Greer, E. Truslow","doi":"10.1109/AIPR.2012.6528194","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528194","url":null,"abstract":"A distributed architecture for adaptive sensor fusion (a multisensor fusion neural net) is introduced for 3D imagery data that makes use of a super-resolution technique computed with a Bregman-Iteration deconvolution algorithm. This architecture is a cascaded neural network, which consists of two levels of neural networks. The first level consists of sensor networks: two independent sensor neural nets, namely, a spatial neural net and spectral neural net. The second level is a fusion neural net, which contains a single neural net that combines the information from the sensor level. The inputs to the sensor networks are obtained from unsupervised spatial and spectral segmentation algorithms that can be applied to the original imagery or imagery enhanced by a proposed super-resolution process. Spatial segmentation is obtained by a mean-shift method and spectral segmentation is obtained by a Stochastic Expectation Maximization method. The decision outputs from the sensor nets are used to train the fusion net to a specific overall decision. The overall approach is tested with an experiment involving a multi-sensor airborne collection of LIDAR and Hyperspectral data over a university campus in Gulfport MS. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated. The final class map contains the geographical classes as well as the signature classes.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116216587","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

Fusion of LIDAR data with hyperspectral and high-resolution imagery for automation of DIRSIG scene generation 激光雷达数据与高光谱和高分辨率图像的融合，用于DIRSIG场景生成自动化

2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2012-10-09 DOI: 10.1109/AIPR.2012.6528191

Ryan N. Givens, K. Walli, M. Eismann

{"title":"Fusion of LIDAR data with hyperspectral and high-resolution imagery for automation of DIRSIG scene generation","authors":"Ryan N. Givens, K. Walli, M. Eismann","doi":"10.1109/AIPR.2012.6528191","DOIUrl":"https://doi.org/10.1109/AIPR.2012.6528191","url":null,"abstract":"Developing new remote sensing instruments is a costly and time consuming process. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model gives users the ability to create synthetic images for a proposed sensor before building it. However, to produce synthetic images, DIRSIG requires facetized, three-dimensional models attributed with spectral and texture information which can themselves be costly and time consuming to produce. Recent work by Walli has shown that coincident LIDAR data and high-resolution imagery can be registered and used to automatically generate the geometry and texture information needed for a DIRSIG scene. This method, called LIDAR Direct, greatly reduces the time and manpower needed to generate a scene, but still requires user interaction to attribute facets with either library or field measured spectral information. This paper builds upon that work and presents a method for autonomously generating the geometry, texture, and spectral content for a scene when coincident LIDAR data, high-resolution imagery, and HyperSpectral Imagery (HSI) of a site are available. Then the method is demonstrated on real data.","PeriodicalId":406942,"journal":{"name":"2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173733","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: 5