Image Metamorphosis with Scattered Feature Constraints
IEEE Transactions on Visualization and Computer Graphics
Convex MRF potential functions
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Visual recognition of speech consonants using facial movement features
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
Super-resolution image reconstruction using the generalized isotropic multi-level logistic model
Proceedings of the 2009 ACM symposium on Applied Computing
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
The multi-bit watermarking method for speech signals in the time-frequency domain
Integrated Computer-Aided Engineering
MAP-MRF super-resolution image reconstruction using maximum pseudo-likelihood parameter estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Journal of Mathematical Imaging and Vision
A generalization of quad-trees applied to image coding
Integrated Computer-Aided Engineering
Scars collaborative telediagnosis platform using adaptive image flow
Integrated Computer-Aided Engineering
Querying fuzzy spatiotemporal data using XQuery
Integrated Computer-Aided Engineering
Rician noise attenuation in the wavelet packet transformed domain for brain MRI
Integrated Computer-Aided Engineering
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Dynamic magnetic resonance imaging is an emerging technique for studying speech production. Vocal tract image sequences acquired during the speech of words or phonemes allow the identification of shapes taken by the vocal tract during speech production. However, there is no prior knowledge about the spatial and temporal resolution requirements, which are expected to vary depending on the speech task. Available approaches try to enhance resolution by empowering the acquisition devices. However, there are several cost and hardware limitations. In this paper, we propose an alternative approach to enhance spatio-temporal resolution using only digital image processing techniques. We use a previous non-rigid image registration method to identify displacements and deformations among the observed images. Based on a motion compensated interpolation approach, temporal resolution is increased by generating intermediate images which are coherent with the movement present in the observed sequence. Moreover, a MAP-MRF super-resolution image reconstruction approach is used to increase spatial resolution of the whole sequence. Results indicate the effectiveness of our approach.