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Titlebook: Augmented Vision Perception in Infrared; Algorithms and Appli Riad I. Hammoud Book 2009 Springer-Verlag London 2009 Laser.Performance.Stere

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发表于 2025-3-21 19:26:19 | 显示全部楼层 |阅读模式
期刊全称Augmented Vision Perception in Infrared
期刊简称Algorithms and Appli
影响因子2023Riad I. Hammoud
视频video
发行地址Provides a critical analysis of state of the art techniques, in the field of computer vision including comparison of existing methods and benchmarking.Highlights new trends and directions in the field
学科分类Advances in Computer Vision and Pattern Recognition
图书封面Titlebook: Augmented Vision Perception in Infrared; Algorithms and Appli Riad I. Hammoud Book 2009 Springer-Verlag London 2009 Laser.Performance.Stere
影响因子Throughout much of machine vision’s early years the infrared imagery has suffered from return on investment despite its advantages over visual counterparts. Recently, the ?scal momentum has switched in favor of both manufacturers and practitioners of infrared technology as a result of today’s rising security and safety challenges and advances in thermographic sensors and their continuous drop in costs. This yielded a great impetus in achieving ever better performance in remote surveillance, object recognition, guidance, noncontact medical measurements, and more. The purpose of this book is to draw attention to recent successful efforts made on merging computer vision applications (nonmilitary only) and nonvisual imagery, as well as to ?ll in the need in the literature for an up-to-date convenient reference on machine vision and infrared technologies. Augmented Perception in Infrared provides a comprehensive review of recent deployment of infrared sensors in modern applications of computer vision, along with in-depth description of the world’s best machine vision algorithms and intel- gent analytics. Its topics encompass many disciplines of machine vision, including remote sensing,
Pindex Book 2009
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Coalitional Tracker for Deception Detection in Thermal Imageryated fault-tolerant behavior that far exceeds the performance of single-particle filter trackers. Our method represents a shift from the typical tracking paradigms and may find application in demanding imaging problems across the electromagnetic spectrum.
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Genetic Improvement and Variabilityres. To combine the match scores of multiple signatures from a single tracked object, we use the sequential probability ratio test. Using infrared data, we show excellent classification performance, with low expected error rates, when using at least 25 looks.
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Verwertung genetischer Informationen,ference, which are either costly or can only partially detect the object. We propose an MHI-based method that can accurately detect location and shape of moving objects for initializing a tracker or recovering from tracking failure. The effectiveness of this method is quantified using long and varied video sequences.
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,Vererbung als biologisches Grundphänomen,y of disparity voting techniques for multimodal registration by yielding qualitative and quantitative results that outperform approaches that do not consider occlusions. A framework for tracking with the registered multimodal features is also presented and experimentally validated.
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Vehicle Classification in Infrared Video Using the Sequential Probability Ratio Testres. To combine the match scores of multiple signatures from a single tracked object, we use the sequential probability ratio test. Using infrared data, we show excellent classification performance, with low expected error rates, when using at least 25 looks.
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