Journal article  Open Access

Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images

Danielis A., Giorgi D., Larsson M., Stromberg T., Colantonio S., Salvetti O.

Blood concentration map  Hyper-spectral  Lambertian shading  Lip spatial pattern  Segmentation  Fourier descriptors  Artificial Intelligence  Computer Vision and Pattern Recognition  Software  Morphological  Signal Processing 

Lip segmentation is a non-trivial task because the colour difference between the lip and the skin regions maybe not so noticeable sometimes. We propose an automatic lip segmentation technique for hyper-spectral images from an imaging prototype with medical applications. Contrarily to many other existing lip segmentation methods, we do not use colour space transformations to localise the lip area. As input image, we use for the first time a parametric blood concentration map computed by using narrow spectral bands. Our method mainly consists of three phases: (i) for each subject generate a subset of face images enhanced by different simulated Lambertian illuminations, then (ii) perform lip segmentation on each enhanced image by using constrained morphological operations, and finally (iii) extract features from Fourier-based modeled lip boundaries for selecting the lip candidate. Experiments for testing our approach are performed under controlled conditions on volunteers and on a public hyper-spectral dataset. Results show the effectiveness of the algorithm against low spectral range, moustache, and noise.

Source: Pattern recognition 63 (2017): 355–370. doi:10.1016/j.patcog.2016.10.007

Publisher: Pergamon Press., New York, Regno Unito


Back to previous page
BibTeX entry
	title = {Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images},
	author = {Danielis A. and Giorgi D. and Larsson M. and Stromberg T. and Colantonio S. and Salvetti O.},
	publisher = {Pergamon Press., New York, Regno Unito},
	doi = {10.1016/j.patcog.2016.10.007},
	journal = {Pattern recognition},
	volume = {63},
	pages = {355–370},
	year = {2017}

SEMEiotic Oriented Technology for Individual’s CardiOmetabolic risk self-assessmeNt and Self-monitoring