Titlebook: Machine Learning for Vision-Based Motion Analysis; Theory and Technique Liang Wang,Guoying Zhao,Matti Pietik?inen Book 2011 Springer-Verlag

BULB

書目名稱Machine Learning for Vision-Based Motion Analysis影響因子(影響力)




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書目名稱Machine Learning for Vision-Based Motion Analysis被引頻次




書目名稱Machine Learning for Vision-Based Motion Analysis被引頻次學(xué)科排名




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書目名稱Machine Learning for Vision-Based Motion Analysis年度引用學(xué)科排名




書目名稱Machine Learning for Vision-Based Motion Analysis讀者反饋




書目名稱Machine Learning for Vision-Based Motion Analysis讀者反饋學(xué)科排名

欲望

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.

聯(lián)邦

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

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

Customary

改變

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

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

妨礙

頭盔

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