Capturing Features for Height Computation Derived With Gaussian Mixture Model

ADOMAR L., ILAO and JENNIFER C., CUA and KEVIN ABRAM B, HERNANDEZ and MEILYNNE S, SUNCHUANGCO (2014) Capturing Features for Height Computation Derived With Gaussian Mixture Model. In: Second International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2014, 20 - 21 December, 2014, Kuala Lumpur, Malaysia.

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Abstract

Height is a biometric trait which is considered as one of the important parameters for the identification of a person and nutritional status. This study generally aimed to obtain the height of a person through experimental approach utilizing computer vision. The web cam captures group of students into a single image. Canny edge detection is applied for image segmentation and Gaussian Mixture Model (GMM) for background subtraction. Segmented images were evaluated to identify the ideal number of students from a controlled environment lessening computer vision constraints. Data collected from the experiment were subjected to one-way ANOVA and T-Test to analyze the difference between prototype derived height from a captured image and actual height of the student. The prototype was developed using OpenCV library integrated to C# available in Microsoft Studio 2010

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: background subtraction, Gaussian Mixture Model, Canny Edge Detection, height derivation
Depositing User: Mr. John Steve
Date Deposited: 22 Apr 2019 11:45
Last Modified: 22 Apr 2019 11:45
URI: http://publications.theired.org/id/eprint/1440

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