FPGA implementation of real-time skin color detection with mean-based surface flattening

Authors:
Seunghun Jin;Dongkyun Kim;Thien Cong Pham;Jae Wook Jeon
Affiliations:
Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea
Venue:
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Year:
2009

Citing 5
Cited 0

Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
VLSI Architecture: Past, Present, and Future

ARVLSI '99 Proceedings of the 20th Anniversary Conference on Advanced Research in VLSI
A Development System for Creating Real-time Machine Vision Hardware Using Field Programmable Gate Arrays

HICSS '99 Proceedings of the Thirty-Second Annual Hawaii International Conference on System Sciences-Volume 3 - Volume 3
An FPGA-Based Verification Framework for Real-Time Vision Systems

CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Extraction of Hand Gestures with Adaptive Skin Color Models and Its Applications to Meeting Analysis

ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Skin color is widely used in many applications because of its merit in human-machine interactions. However, detecting skin color requires repetitive operations on all pixels in the image, similar to other vision-based applications. Since the per-pixel processing is difficult to perform efficiently in conventional computers, many real-time image processing applications have problems with performance. In this paper, we propose FPGA implementation of a real-time skin color detection system. Among the various skin color detection methods, we chose a parametric skin distribution modeling method based on a Gaussian mixture, due to its acceptable training amount and skin detection performance. In addition, a mean-based surface flattening method was also proposed and implemented to improve the detection performance. The proposed method flattens the surface of objects in the scene by replacing the pixel value with the mean of its similar neighborhoods to remove the color noise. After this flattening process, the pixel values of the analogous adjacent pixels are located within a narrow range and are easily segmented to a different region. To consider the inherent parallelism of local image processing, all these functions are implemented within the FPGA to meet the demands of real-time performance.