A New Face Tracking Method Using Block Difference Image and Kalman Filter in Moving Picture

Jang, Hee-Jun; Ko, Hye-Sun; Choi, Young-Woo; Han, Youngjoon; Hahn, Hernsoo

doi:10.5391/jkiis.2005.15.2.163

Cited by 2 publications

(1 citation statement)

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“…Anisotropic diffusion can be implemented on 1D, 2D, and 3D data. Gerig et al [10] used anisotropic diffusion for filtering MRI data where it has been applied to 2D and 3D image data with one or two channels [11,12]. Linear anisotropic diffusion mesh filtering has also been investigated [13].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Face Point Cloud Smoothing Based on Modified Anisotropic Diffusion Method

Wibowo¹,

Kim

2014

International Journal of Fuzzy Logic and Intelligent Systems

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This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

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Section: Introductionmentioning

confidence: 99%