Articolo in rivista, 2017, ENG, 10.1016/j.patcog.2016.10.007

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.

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Linköping University, Linköping, Sweden; Linköping University, Linköping, Sweden; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy

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.

Pattern recognition 63 , pp. 355–370

Keywords

Blood concentration map, Fourier descriptors, Hyper-spectral, Lambertian shading, Lip spatial pattern, Morphological, Segmentation

CNR authors

Danielis Alessandro, Colantonio Sara, Giorgi Daniela, Salvetti Ovidio

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 369108

Year: 2017

Type: Articolo in rivista

Creation: 2017-04-20 18:39:15.000

Last update: 2023-03-18 12:04:59.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:369108

DOI: 10.1016/j.patcog.2016.10.007

Scopus: 2-s2.0-84998996795

ISI Web of Science (WOS): 000389785900028