3D-freehand-pose initialization based on operator’s cognitive behavioral models

Feng, Zhiquan; Zhang, Minming; Pan, Zhigeng; Yang, Bo; Xu, Tao; Tang, Haokui; Li, Yang

doi:10.1007/s00371-010-0452-z

Cited by 17 publications

(11 citation statements)

References 13 publications

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“…For example, Smart TV systems by Samsung can support people's complex interaction with TV sets with freehand gestures. In comparison, gesture-based commands for PC-based applications largely focus on simple commands, such as pointing and drawing [31][32][33] and manipulating objects [1,34,35]. Second, when evaluating our system, we intended to compare the recognition result of our system with others.…”

Section: Fig 4 Ten User-defined Gestures For Tv Commandsmentioning

confidence: 99%

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Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

Wu¹,

Wang

Zhang

2015

Vis Comput

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In the design of gesture-based user interfaces, continuously recognizing complex dynamic gestures is a challenging task, because of the high-dimensional information of gestures, ambiguous semantic meanings of gestures, and the presence of unpredictable non-gesture body motions. In this paper, we propose a hybrid model that can leverage the time-series modeling ability of hidden Markov model and the fuzzy inference ability of fuzzy neural network. First, a complex dynamic gesture is decomposed and fed into the hybrid model. The likelihood probability of an observation sequence estimated by the hidden Markov model is used as fuzzy membership degree of the corresponding fuzzy class variable in fuzzy neural network. Next, fuzzy rule modeling and fuzzy inference are performed by fuzzy neural network for gesture classification. To spot key gestures accurately, a threshold model is introduced to calculate the likelihood threshold of an input pattern and provide a reliability measure of whether to accept the pattern as a gesture. Finally, the proposed method is applied to recognize ten user-defined dynamic gestures for controlling interactive digital television in a smart room. Results of our experiment show that B Huiyue Wu the proposed method performed better in terms of spotting reliability and recognition accuracy than conventional gesture recognition methods.

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Section: Fig 4 Ten User-defined Gestures For Tv Commandsmentioning

confidence: 99%

“…Recent advances in interaction technologies make it possible for people to use natural hand gestures in such applications like virtual/augmented reality [1], computer games [2], and ubiquitous computing [3]. In such applications, hand gestures are captured and turned into interaction commands in real time.…”

Section: Introductionmentioning

confidence: 99%

Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

Wu¹,

Wang

Zhang

2015

Vis Comput

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“…It defines each finger's name and each joint angle's name. The 3D model used in this paper was proposed by Feng [6], as displayed in Fig. 1(b).…”

Section: D Hand Modelmentioning

confidence: 99%

An Observation Likelihood Model Integrating Edge Feature with Silhouette Feature

Liu¹,

Feng²,

Sang³

2013

JSW

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3D hand tracking in recent years has become a hot spot in Human Computer Interaction(HCI), and it also exists many difficulties. Our research roots in the observation model which is used to measure similarity between hand image and hand model for hand tracking. Firstly, using edge feature extracted from hand image and hand model projection contour to get a probability function, which measures the similarity between hand model and hand image according their chamfer distance. Secondly, using skin color segmentation algorithm to get binary hand image, at the same time we can get hand model silhouette from hand model projection points. After getting the binary hand image and hand model silhouette we then build another probability function based silhouette feature. Finally we combine the two functions to build the observation model to evaluate the probability of hand state according current observation. Applying the observation model into the weight calculation stage of particle filter can effectively increase calculation accuracy. On the other hand, we introduce hand constraints to hand model, utilize these constraints in sampling stage not only can reduce sampling degree of freedom(DOF) but also can revise the incorrect samples.

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“…Creating a very fine hand model is a key factor to achieve a natural gesture interaction. The 3D hand model used in this paper was proposed by Feng [8], as displayed in figure 1. This model has 26 degrees of freedom (DOF): 20 local DOF and 6 global DOF.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Gesture Tracking Algorithm Based on Microstructure of Hand Motion

Min¹,

Feng²

2013

2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications

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Aiming at describing human hand 3D tracking from a new perspective and reducing the time cost of hand motion tracking, a novel model-based approach to 3D hand tracking from single camera is presented. First of all this paper introduces the hand model and the virtual platform based on data glove as the basic conditions of microstructure research. Then, the basic concept of microstructure of hand motion is proposed. By applying microstructure of hand motion to exploring hand state prediction, the microstructure in the process of grasping an object is emphatically described. Finally, a human hand 3D tracking algorithm based on microstructure of hand motion is put forward. The main innovation of the algorithm is to avoid our algorithm excessively relying on the dynamic model forecast. By predicting the current state on the whole grasping process with microstructure, rather than predicting the current state on a few frames before, the proposed algorithm can sample on the most likely particles. This method avoids impossible or less likely particles as far as possible. In order to validate validity and performance of the algorithm, a great deal of experiments are completed. The results show that, compared with the particle filter (PF) gesture tracking algorithm, the proposed algorithm has low time cost.Keywords-human hand 3D tracking; particle filter; microstructure of hand motion 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications /13 $26.00

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3D-freehand-pose initialization based on operator’s cognitive behavioral models

Cited by 17 publications

References 13 publications

Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

An Observation Likelihood Model Integrating Edge Feature with Silhouette Feature

Three-Dimensional Gesture Tracking Algorithm Based on Microstructure of Hand Motion

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