2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)最新文献

Optimal wavelet features for an infrared satellite precipitation estimate algorithm 红外卫星降水估计算法的最优小波特征

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759702

Majid Mahrooghy, V. Anantharaj, N. Younan, J. Aanstoos

引用次数: 1

Vehicle load estimation from observation of vibration response 基于振动响应观测的车辆载荷估计

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759718

P. Robertson, W. B. Coney, R. Bobrow

引用次数: 2

Pre-attentive detection of depth saliency using stereo vision 使用立体视觉的深度显著性的预先注意检测

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759692

M. Z. Aziz, B. Mertsching

引用次数: 5

Classification of levees using polarimetric Synthetic Aperture Radar (SAR) imagery 利用偏振合成孔径雷达(SAR)图像对堤防进行分类

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759703

Lalitha Dabbiru, J. Aanstoos, N. Younan

{"title":"Classification of levees using polarimetric Synthetic Aperture Radar (SAR) imagery","authors":"Lalitha Dabbiru, J. Aanstoos, N. Younan","doi":"10.1109/AIPR.2010.5759703","DOIUrl":"https://doi.org/10.1109/AIPR.2010.5759703","url":null,"abstract":"The recent catastrophe caused by hurricane Katrina emphasizes the importance of examination of levees to improve the condition of those that are prone to failure during floods. On-site inspection of levees is costly and time-consuming, so there is a need to develop efficient techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under flood loading. This research uses NASA JPL's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) backscatter data for classification and analysis of earthen levees. The overall purpose of this research is to detect the problem areas along the levee such as through-seepage, sand boils and slough slides. This paper focuses on detection of slough slides. Since the UAVSAR is a quad-polarized L-band (λ = 25 cm) radar, the radar signals penetrate into the soil which aids in detecting soil property variations in the top layer. The research methodology comprises three steps: initially the SAR image is classified into three scattering components using the Freeman-Durden decomposition algorithm; then unsupervised classification is performed based on the polarimetric decomposition parameters: entropy (H) and alpha (α); and finally reclassified using the Wishart classifier. A 3×3 coherency matrix is calculated for each pixel of the radar's compressed Stokes matrix multi-look backscatter data and is used to retrieve these parameters. Different scattering mechanisms like surface scattering, dihedral scattering and volume scattering are observed to distinguish different targets along the levee. The experimental results show that the Wishart classifier can be used to detect slough slides on levees.","PeriodicalId":128378,"journal":{"name":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130287836","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: 7

Successful design of biometric tests in a constrained environment 在受限环境下成功设计生物特征测试

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759713

V. Dvornychenko

引用次数: 0

Tracking cables in sonar and optical imagery 声纳和光学成像跟踪电缆

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759686

J. Isaacs, R. Goroshin

引用次数: 1

A manifold based methodology for color constancy 基于流形的颜色恒定性方法

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759707

A. Mathew, A. Alex, V. Asari

{"title":"A manifold based methodology for color constancy","authors":"A. Mathew, A. Alex, V. Asari","doi":"10.1109/AIPR.2010.5759707","DOIUrl":"https://doi.org/10.1109/AIPR.2010.5759707","url":null,"abstract":"In this paper, we propose a manifold-based methodology for color constancy. It is observed that the center surround information of an image creates a manifold in color space. The relationship between the points in the manifold is modeled as a line. The human visual system is capable of learning these relationships. This is the basis of color constancy. In illumination correction, the image in the reference illumination is operated on with a wide Gaussian function to extract the global illumination information. The global illumination information creates a manifold in color space which is learnt by the system as a line. An image in a different color perception creates a different manifold in color space. To transform the color perception of a scene in a given illumination to the reference color perception, the color relationships in the reference color perception are applied on the new image. This is achieved by projecting the pixels in the new image to the line representing the manifold of reference color perception. This model can be used for color correction of images with different color perceptions to a learnt color perception. This method, unlike other approaches, has a single step convergence and hence is faster.","PeriodicalId":128378,"journal":{"name":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122337286","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

Visual attention based detection of signs of anthropogenic activities in satellite imagery 卫星图像中基于视觉注意的人为活动迹象检测

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759693

A. Skurikhin

引用次数: 4

Performance evaluation of color image retrieval 彩色图像检索的性能评价

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759680

E. Mendi, Coskun Bayrak

引用次数: 1

Motion imagery metadata standards assist in object and activity classification 运动图像元数据标准有助于对象和活动分类

2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2010-10-01 DOI: 10.1109/AIPR.2010.5759700

Darrell L. Young

引用次数: 1