## 6 Discussions and Future Directions
These representations synthesize the time-varying data to visualize hidden features that are not easily observed from the laryngeal sequences. However, many of the FP presented in the literature have some drawbacks that restrict their applicability since they rely on glottal-area segmentation (GAW, GVG, PVG, PVG-wavegram, VFT, VP, EFA and HTA). Additionally, most of the FP consider only those points belonging to the glottal contours and neglect the MW contribution. Others, such as GVG, PVG, PVG-wavegram, VFT and VP rely on the computation of glottal main axis, which strongly depends on the geometry of the detected glottal area. FP such as GAW, HTA, NDA and PGAW are based on glottal area waveform computation, so they do not preserve spatial informationabout vocal folds vibration. On the other hand, FP such as OFW, OFKG, DKG and MKG restrict the information along one single line. The challenge is to provide representations to overcome the drawbacks of existing ones, providing simultaneously features that would integrate the time dynamics, such as velocity, acceleration, instants of maximum and minimum velocity, vocal folds displacements during phonation and motion analysis of the MW. To the best of our knowledge, only few works study the MW propagation [46; 144; 146; 170]. Future studies have to analyze in more detail the objective detection and quantification of MW propagation since its existence and magnitude provides valuable information about the coupling between the mucosa and the subjacent vocal folds muscle. Additionally, it is widely demonstrated that MW activity is a useful indicator of the quality of voice production and the presence of voice disorders. One possible solution to address these shortcomings, it could be created kinematics models that simulate the MW based on physical models and FP, similar that the one presented in [170]. In this way, it will be possible to explore the variations of the MW under different constraints. Besides, it can be exploited the potential of 3D visualization of vocal folds to understand the propagation of mucosal wave using motion estimation techniques as 3D feature tracking or dense 3D motion field [171; 172]. Further development should explore other techniques such as registration [64], Haar wavelets [173], Haar-like features [174], local binary patterns (LBP) [175], Histogram of Oriented Gradient (HOG) [176] or supervised machine-learning to characterize the kinematics of the vocal folds. The image registration is one of the fundamental tasks within image processing, which let to find an optimal geometric transformation between two corresponding images. Therefore, it will be possible to model the deformation of the vocal folds for each particular laryngeal mechanism using geometric transformations. The HOG well captures local shape characteristics of the targeted object based on directions and magnitudes of image derivatives. Then it could be used to model the wavelike motion of the vocal folds during different vocal tasks. Besides, HOG could be combined with Haar wavelets, Haar-like features and local binary patterns (LBP) to create a new feature descriptor to classify different vocal folds vibratory patterns [177]. On the other hand, supervised machine-learning techniques, especially deep learning [178], is part of state-of-the-art systems in various disciplines, particularly in computer vision and image processing. In addition, it has been used to make critical decisions in medical diagnosis. It would be possible to train a neural network to extract 3D structure of vocal folds; to segment the glottal gap; to compensate camera rotations and translations; to enhance the quality of laryngeal imaging; to synthesize video information; to classify different vibratory patterns; to generate synthetic datasets; to track different glottal structures (e.g., artytenoids, epiglottis); image data augmentation to increase video frame rate; among others.