Implementation of Separable & Steerable Gaussian Smoothers on an FPGA

<p>Smoothing filters have been extensively used for noise removal and image restoration. Directional filters are widely used in computer vision and image processing tasks such as motion analysis, edge detection, line parameter estimation and texture analysis. It is practically impossible to tune the filters to all possible positions and orientations in real time due to huge computation requirement. The efficient way is to design a few basis filters, and express the output of a directional filter as a weighted sum of the basis filter outputs. Directional filters having these properties are called "Steerable Filters." This thesis work emphasis is on the implementation of proposed computationally efficient separable and steerable Gaussian smoothers on a Xilinx VirtexII Pro FPGA platform. FPGAs are Field Programmable Gate Arrays which consist of a collection of logic blocks including lookup tables, flip flops and some amount of Random Access Memory. All blocks are wired together using an array of interconnects. The proposed technique [2] is implemented on a FPGA hardware taking the advantage of parallelism and pipelining.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis-Restricted

Discipline thesis:degree_discipline

Electrical Engineering

Year

2010

Author dc:creator

• Joginipelly, Arjun

Contributors dc:contributor

• Charalampidis, Dimitrios
• Jilkov, Vesselin
• Ioup, George

• Field Programmable Gate Arrays (FPGAs)
• Parallel Image Processing
• Directional Smoothing Filters
• Steerable Filters
• Gaussian Mask
• Separable Convolution

Repository record dc:identifier

https://scholarworks.uno.edu/td/98

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1097