Automatic Tongue Tracking in X‐Ray Images

Abstract

X-ray imaging is an effective technique toobtain the continuous motions of the vocal tract duringspeech, and Active appearance model (AAM) is a usefultool to analyze the X-ray images. However, for the task oftongue tracking in X-ray images, the accuracy of AAM fitting is insufficient. AAM aims to minimize the residual error between the model appearance and the input image. Itoften fails to accurately converge to the true landmarks. Toimprove the tracking accuracy, we propose a fitting methodby combining Constrained local model (CLM) into AAM.In our method, we first combine the objective functionsof AAM and CLM into a single objective function. Then,we project out the texture variation and derive a gradient based method to optimize the objective function. Ourmethod effectively incorporates not only the shape priorand global texture, but also local texture around each landmark. Experiments demonstrate that the proposed methodsignificantly reduces the fitting error. We also show that realistic 3D tongue animation can be created by using tonguetracking results of the X-ray images.