35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)最新文献

Rapid Automated Polygonal Image Decomposition 快速自动多边形图像分解

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.30

S. Swaminarayan, Lakshman Prasad

引用次数: 26

Performance Metrics for Intelligent Systems (PerMIS) 2006Workshop: Summary and Review 智能系统性能指标(PerMIS) 2006研讨会:总结与回顾

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.29

R. Madhavan, E. Messina

引用次数: 2

Autonomous Hyperspectral Target Detection with Quasi-Stationarity Violation at Background Boundaries 背景边界准平稳冲突的自主高光谱目标检测

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.18

A. Schaum

引用次数: 3

Identification of Objects-of-Interest in X-Ray Images x射线图像中感兴趣目标的识别

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.25

Carsten K. Oertel, P. Bock

引用次数: 17

Evaluation of Algorithms for Tracking Multiple Objects in Video 视频中多目标跟踪算法的评价

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.23

A. Perera, A. Hoogs, C. Srinivas, G. Brooksby, Wensheng Hu

{"title":"Evaluation of Algorithms for Tracking Multiple Objects in Video","authors":"A. Perera, A. Hoogs, C. Srinivas, G. Brooksby, Wensheng Hu","doi":"10.1109/AIPR.2006.23","DOIUrl":"https://doi.org/10.1109/AIPR.2006.23","url":null,"abstract":"As video tracking research matures, the issue of tracker performance evaluation has emerged as a research topic in its own right, as evidenced by a series of workshops devoted solely to this purpose (the workshops on performance evaluation of tracking and surveillance-PETS). However, evaluations such as PETS have been limited to small scenarios with a handful of moving objects. In this paper, we present an evaluation methodology and set of experiments focused on large-scale video tracking problems with hundreds of objects in close proximity. The scale and complexity of this data exposes a number of issues. First, the association of computed tracks to image-truth tracks may have multiple plausible solutions, resulting in a combinatorial grouping problem that must be solved with an approximate solution. Second, computed tracks may be only partially correct, complicating the association problem further and indicating that multiple measures are required to characterize performance. We have created a system that associates computed tracks to manually-generated image-truth tracks, and calculates various measures such as the per-frame probability of detection, false alarm rate, and fragmentation, which is the number of computed tracks associated to a single track. We also normalize fragmentation by track length to reward fewer computed tracks for longer true tracks. The measures were used to compare three tracking methods on an aerial video sequence containing hundreds of objects, long occlusions, and deep shadows.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712279","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: 17

A Rate Distortion Method for Beamforming in RF Image Formation 射频图像形成中波束形成的一种速率失真方法

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.7

R. Bonneau

引用次数: 1

Data Fusion, De-noising, and Filtering to Produce Cloud-Free High Quality Temporal Composites Employing Parallel Temporal Map Algebra 数据融合，去噪和滤波产生无云的高质量的时间合成采用并行时间映射代数

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.20

B. Shrestha, C. O'Hara, P. Mali

{"title":"Data Fusion, De-noising, and Filtering to Produce Cloud-Free High Quality Temporal Composites Employing Parallel Temporal Map Algebra","authors":"B. Shrestha, C. O'Hara, P. Mali","doi":"10.1109/AIPR.2006.20","DOIUrl":"https://doi.org/10.1109/AIPR.2006.20","url":null,"abstract":"Remotely sensed images from satellite sensors such as MODIS Aqua and Terra provide high temporal resolution and wide area coverage. Unfortunately, these images frequently include undesired cloud and water cover. Areas of cloud or water cover preclude analysis and interpretation of terrestrial land cover, vegetation vigor, and/or analysis of change. Cross platform multi-temporal image compositing techniques may be employed to create daily synthetic cloud free images using fused images from Aqua and Terra MODIS satellite images, and then creating a composite that includes representative values derived from a set of possibly cloudy satellite images collected during a given longer time period of interest. Spatio-temporal analytical processing methods that utilize moderate spatial resolution satellite imagery with high temporal resolution to create multi-temporal composites are data intensive and computationally intensive. Therefore, a study of the strategies using high performance parallel solutions is required. This research focuses on analyzing the fusion, de-noising, filtering, and compositing strategies for vegetation indices using parallel temporal map algebra. The report provides objective findings on methods and the relative benefits observed from various analysis methods and parallelization strategies.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133626963","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

Adaptive Thresholding Based Cell Segmentation for Cell-Destruction Activity Verification 基于自适应阈值分割的细胞破坏活性验证

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.9

P. Sankaran, V. Asari

引用次数: 13

Application Development Framework for the Rapid Integration of High Performance Image Processing Algorithms 快速集成高性能图像处理算法的应用开发框架

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.15

S. Spetka, G. Ramseyer, R. Linderman

引用次数: 1

Recovering Spheres from 3D Point Data 从3D点数据中恢复球体

35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06) Pub Date : 2006-10-11 DOI: 10.1109/AIPR.2006.33

C. Witzgall, G. Cheok, Anthony J. Kearsley

引用次数: 9