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Speech Emotion Recognition from Variable-Length Inputs with Triplet Loss Function
会议论文
Hyderabad, India, 2018.9.2-2018.9.6
作者:
Huang, Jian
;
Li, Ya
;
Tao, Jianhua
;
Lian, Zheng
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浏览/下载:7/0
  |  
提交时间:2020/06/20
Sub-blocks segmentation based on multi-feature fusion
会议论文
Beijing, China, May 22-24, 2018
作者:
Hui B(惠斌)
;
Chang Z(常铮)
;
Luo HB(罗海波)
;
Chen HY(陈宏宇)
;
Jiao AB(焦安波)
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  |  
浏览/下载:22/0
  |  
提交时间:2018/12/24
Computer Vision
Target Tracking
Deformable Model
Multi-feature Fusion
Sub-blocks Segmentation
Edge Direction Dispersion Degree
Hybrid patch based diagonal pattern geometric appearance model for facial expression recognition
会议论文
Beijing, 2016-10-19
作者:
Jain Deepak Kumar
;
Zhang Zhang
;
Huang Kaiqi
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浏览/下载:20/0
  |  
提交时间:2018/06/19
Facial Expressions
Feature Points
A novel hand posture recognition system based on sparse representation using color and depth images
会议论文
2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013, Tokyo, Japan
作者:
Xu Dan
;
Chen Yen-Lun
;
Wu Xinyu
;
Feng Wei
;
Qian Huihuan
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  |  
浏览/下载:9/0
  |  
提交时间:2015/08/27
The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE)
会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
Sun H.
;
Han H.-X.
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浏览/下载:50/0
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提交时间:2013/03/25
Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring
precision
and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection
the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure
but in order to capture the change of the state space
it need a certain amount of particles to ensure samples is enough
and this number will increase in accompany with dimension and increase exponentially
this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining"
we expand the classic Mean Shift tracking framework.Based on the previous perspective
we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis
Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism
used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation
and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information
this approach also inhibit interference from the background
ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
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