Articolo in rivista, 2017, ENG, 10.1016/j.patcog.2016.10.007
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
Blood concentration map, Fourier descriptors, Hyper-spectral, Lambertian shading, Lip spatial pattern, Morphological, Segmentation
Danielis Alessandro, Colantonio Sara, Giorgi Daniela, Salvetti Ovidio
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 links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.patcog.2016.10.007
URL: http://www.sciencedirect.com/science/article/pii/S0031320316303235
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