Determining Optimal Frame Processing Strategies for Real-Time Document Recognition Systems

This chapter aims to address the ability of self-organizing neural network models to manage video and image processing in real-time. The Growing Neural Gas networks (GNG) with its attributes of growth, flexibility, rapid adaptation, and excellent quality representation of the input space makes it a suitable model for real time applications. A number of applications are presented, including: image compression, hand and medical image contours representation, surveillance systems, hand gesture recognition systems, and 3D data reconstruction.

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Toward Real-Time Pattern Recognition Systems

Machine Vision ◽

10.1016/b978-0-12-206093-9.50066-6 ◽

2005 ◽

pp. 625

Author(s):

E.R. DAVIES

Keyword(s):

Pattern Recognition ◽

Real Time ◽

Time Pattern ◽

Pattern Recognition Systems ◽

Recognition Systems

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A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Sensors ◽

10.3390/s20082321 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2321 ◽

Cited By ~ 2

Author(s):

Myoungseok Yu ◽

Narae Kim ◽

Yunho Jung ◽

Seongjoo Lee

Keyword(s):

Real Time ◽

Conventional Method ◽

Gesture Recognition ◽

Detection Method ◽

Recognition System ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Recognition Probability ◽

Recognition Systems ◽

Cw Radar

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

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