Feature Extraction & Lips Posture Detection Oriented to the Treatment of CLP Children

Abstract

Adequate recognition of lips posture for speech articulation analysis requires of the measurement of several anthropometric mouth parameters. These are needed to estimate the position and contour of the lips and teeth and tongue positions, as well. Here, a method is proposed for lips contour detection under natural conditions without any extra hardware requirements for image acquisition. The purpose of the suggested process is to obtain the lips contour based on red hue fields detection. Afterward, geometrical features of lips are extracted from their detected contour. Image processing is divided into the following steps: Face and mouth search, lips contour detection and feature estimation from lips geometry. Due to high dimensionality of the initial feature space, it is very important to evaluate the performance of the lips features regarding their ability to discriminate pathological lip postures in the case of children with cleft lip and palate. In this paper, a proposed method for effective selection of the image feature set was developed using multivariate analysis techniques. Finally, the discriminant performance of the selected training sets was evaluated using bayesian estimators. Results of the comparison for different common testing algorithms show that the proposed processing method exposes better performance.

O papel do estresse oxidativo e do exercí­cio fí­sico na doença de Parkinson

“…Embora a natureza do processo neurodegenerativo verificado na DP não seja conhecida, atualmente tem sido sugerido o envolvimento do alto potencial oxidativo da dopamina (DA) na geração de H 2 O 2 e outras ERO [29], assim como à alta concentração de ferro e sua homeostase alterada [30], e baixos níveis de glutationa [31]. O metabolismo alterado do ferro nas suas formas transicional ferrosa (Fe +2 ) e férrica (Fe +3 ) tem forte potencial oxidante pela geração de radicais hidroxil (OH • ) com H 2 O 2 através da reação de Fenton/Haber-Weiss [32].…”

Assessing the deformity of cleft lip nose based on neural network

“…Step 1: Divide the judges into two groups: (1) doctors and (2) laymen and then perform Kendall's (W) procedure to these groups respectively whose result is listed in Table 5. It can be seen that the consistency for the group of five laymen (0.925) is a little better than the doctor's (0.835).…”

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

“…In [23] and [24], HMM based visual speech recognition is described. Several studies have found that a multimodal approach to speech recognition, combining speech, mouth and face features, gives significantly improved performance over a single mode approach under trying test conditions [1], [3], [25] and [26].…”