Detection of head position using chain code algorithm

Fuad, Norfaiza (2007) Detection of head position using chain code algorithm. Masters thesis, Universiti Putra Malaysia.

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Abstract

Nowadays, autonomous vision-based system has been applied to handle human job that reliability and efficiency of any intelligent system gain improvement and enhancement. Human head detection is the first step of an autonomous human recognition system. This thesis focuses on a method to recognize and detect a human at the surveillance or highlighted area boundary based their head beside; a simulation system of head detection was developed using image processing. The main contribution of this thesis is it contributes an algorithm of head recognition and detecting which based on image segmentation, Prewitt edge detection and Chain Code algorithm. Static or still images are used as input data for simulation process. The use of Median Filter (MF) method for preprocessing stage is studied and implemented to make low noise for good signification in an image. Prewitt edge detecting (PED) has been applied to present boundary of features in the images in early stage. The image converted to binary image using Threshold Coding (TC) for difference between boundary and background. Features from the image are train using the Chain Code Algorithm (CCA) to do recognition of the crux human head then do detecting process. The low of complexity in mathematical equation is the factor of chosen this method, compares other techniques. Two environments have been applied to demonstrate the performance of the system, a person or more was detected for texture background and untextured background. The anahsis. design and development of simulation system are done in Visual C++. All the methods have been tested on image data and the experimental results have demonstrated a robust system.

Item Type:Thesis (Masters)
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
ID Code:705
Deposited By:Ms Aryanti Ahmad
Deposited On:30 Mar 2011 14:31
Last Modified:29 Apr 2011 14:41

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