Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lip segmentation is a non-trivial task because the colour difference beten the lip and the skin regions maybe not so noticeable sometimes.  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,  do not use colour space transformations to localise the lip area. As input image,  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.
['Alessandro Danielis', 'Daniela Giorgi', 'Marcus Larsson', 'Tomas Strömberg', 'Sara Colantonio', 'Ovidio Salvetti']
I am writing a research paper on a idea Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images
[{"from": "human", "value": "I am writing a research paper on a idea Lip segmentation based on Lambertian shadings and morphological operators for hyper-spectral images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Lip segmentation is a non-trivial task because the colour difference beten the lip and the skin regions maybe not so noticeable sometimes.  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,  do not use colour space transformations to localise the lip area. As input image,  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."}]