A novel approach to 3-D gaze tracking using 3-D computer vision techniques is proposed in this paper. This method employs multiple cameras and multiple point light sources to estimate the optical axis of user's eye without using any userdependent parameters. Thus, it renders the inconvenient system calibration process which may produce possible calibration errors unnecessary. A real-time 3-D gaze tracking system has been developed which can provide 30 gaze measurements per second. Moreover, a simple and accurate calibration method is proposed to calibrate the gaze tracking system. Before using the system, each user only has to stare at a target point for a few (2-3) seconds so that the constant angle between the 3-D line of sight and the optical axis can be estimated. The test results of six subjects showed that the gaze tracking system is very promising achieving an average estimation error of under 1 degree.

Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

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In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.