BULB
发表于 2025-3-21 19:42:29
书目名称Machine Learning for Vision-Based Motion Analysis影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620655<br><br> <br><br>书目名称Machine Learning for Vision-Based Motion Analysis读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620655<br><br> <br><br>
欲望
发表于 2025-3-21 20:43:32
Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models multidimensional autoregressive models of order 2. Experimental results concerning identity recognition are shown that prove how such optimal pullback Fisher metrics greatly improve classification performances.
联邦
发表于 2025-3-22 00:31:20
Discriminative Multiple Target Trackingimplicit exclusive principle is naturally reinforced in the proposed framework, which renders the tracker to be robust to cross occlusions among the multiple targets. We demonstrate the efficacy of the proposed multiple target tracker on benchmark visual tracking sequences, and real-world video sequences as well.
大方不好
发表于 2025-3-22 07:53:19
Recognition of Spatiotemporal Gestures in Sign Language Using Gesture Threshold HMMs system included testing the performance of conditional random fields (CRF), hidden CRF (HCRF) and latent-dynamic CRF (LDCRF) based systems and comparing these to our GT-HMM based system when recognizing motion gestures and identifying inter gesture transitions.
高原
发表于 2025-3-22 11:23:50
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Customary
发表于 2025-3-22 14:15:56
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改变
发表于 2025-3-22 17:19:25
Learning Behavioral Patterns of Time Series for Video-Surveillanceupervised and unsupervised settings and is based on a set of loosely labeled data acquired by a real video-surveillance system. The experiments highlight different peculiarities of the methods taken into consideration, and the final discussion guides the reader towards the most appropriate choice for a given scenario.
AWL
发表于 2025-3-22 23:01:45
2191-6586 ffective vision-based motion understanding from a machine le.Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human
妨碍
发表于 2025-3-23 02:15:40
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头盔
发表于 2025-3-23 07:03:34
Mixed-State Markov Models in Image Motion Analysisn of mixed-state models to motion texture analysis. Motion textures correspond to the instantaneous apparent motion maps extracted from dynamic textures. They depict mixed-state motion values with a discrete state at zero and a Gaussian distribution for the rest. Mixed-state Markov random fields and