Research of Multi-Touch Projection Camera Interaction Techniques Based on SOPC

Projection-camera interaction is a human-computer interaction technology which uses projector-camera as input-output devices, and combined with human movement as interactive mode. Introducting radial distortion and tangential distortion of lens to projection-geometric calibrate the camera system, the platform is used the Xilinx main processor Virtex-4 FX experimental box to building a projection-camera interactive implementation platform, and used the Baum-Welch algorithm which based on hidden Markov model for training gesture.

Download Full-text

Input-Output Hidden Markov Model to Manage the Remaining useful Life of System Under Missing Data

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference ◽

10.3850/978-981-14-8593-0_3708-cd ◽

2020 ◽

Author(s):

Kamrul Islam Shahin ◽

Christophe Simon ◽

Philippe Weber

Keyword(s):

Missing Data ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Remaining Useful Life ◽

Input Output ◽

Useful Life

Download Full-text

Hand Motion Gesture for Human-Computer Interaction Using Support Vector Machine and Hidden Markov Model

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v11i5.8641 ◽

2016 ◽

Vol 11 (5) ◽

pp. 374

Author(s):

Suharjito Suharjito ◽

Fitra Bayu Adinugraha

Keyword(s):

Support Vector Machine ◽

Human Computer Interaction ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Support Vector ◽

Hand Motion ◽

Computer Interaction

Download Full-text

A New Language and Input–Output Hidden Markov Model for Automated Audit Inquiry

IEEE Intelligent Systems ◽

10.1109/mis.2019.2963653 ◽

2020 ◽

Vol 35 (6) ◽

pp. 39-49

Author(s):

Pushkin Kachroo ◽

Aaron Saiewitz ◽

Robyn Raschke ◽

Shaurya Agarwal ◽

Jiheng Huang

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Input Output

Download Full-text

Input-Output Hidden Markov Model for System Health Diagnosis Under Missing Data

2020 28th Mediterranean Conference on Control and Automation (MED) ◽

10.1109/med48518.2020.9182819 ◽

2020 ◽

Author(s):

Kamrul Islam Shahin ◽

Christophe Simon ◽

Philippe Weber ◽

Didier Theilliol

Keyword(s):

Missing Data ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Input Output ◽

Health Diagnosis

Download Full-text

TRACKING AND SURVEILLANCE IN WIDE-AREA SPATIAL ENVIRONMENTS USING THE ABSTRACT HIDDEN MARKOV MODEL

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001401000782 ◽

2001 ◽

Vol 15 (01) ◽

pp. 177-196 ◽

Cited By ~ 42

Author(s):

HUNG H. BUI ◽

SVETHA VENKATESH ◽

GEOFF WEST

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Markov Models ◽

Hidden Markov ◽

Human Movement ◽

Spatial Layout ◽

Wide Area ◽

Multiple Sensors ◽

Levels Of Detail ◽

Different Levels

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

Download Full-text