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The gaze tracking system has become an active research field for handicapped persons as well as general people in recent years. The precise mapping method plays an important role in the system. In this paper, a novel infrared gaze tracking system based on nonuniform interpolation is proposed. In this system, the eye images for the computer to analyze are extracted under two infrared light sources and a charge-coupled device camera, and the users do not require wearing any device. First, the integral projection algorithm and canny edge detection are applied to extract the pupil boundary points from the captured eye images, and then the pupil center is computed using an efficient and accurate ellipse-fitting algorithm. Finally, to estimate where the user looks, a novel mapping method based on the nonuniform interpolation algorithm is proposed. In this mapping method, the complicated geometric eyeball model and the nonlinear mapping between the pupil center coordinates and computer monitor screen coordinates do not need to be taken into account. Experimental results show that the proposed mapping method is simple, fast and more accurate. Moreover, our system is the remote eye gaze tracking system. The users do not need to wear any device, which make the users feel more comfortable.

In this paper, an intelligent control scheme based on remote gaze tracking is proposed. First, the eye-moving video of the user is captured by ordinary resolution camera under the illumination of near infrared light sources, then the images of the eye region and the pupil region are extracted by processing the video in real time. We process the image of the pupil region, and get the coordinates of the pupil center and the corneal glints produced by the infrared light sources. The coordinates of the points on the screen that the user is observing are computed by the gaze-tracking algorithm based on cross-ratio-invariant, and a procedure of calibration is needed to eliminate the error produced by the deviation of the optical and visual axes of the eyeball. Finally, the gaze is tracked in real-time. The results show that the accuracy of the gaze tracking system is about 0.327 degree horizontally and 0.300 degree vertically, which is better than most gaze tracking system reported in other papers.

Super resolution reconstruction of images has become a very important research topic nowadays. The accuracy of the image registration plays a crucial role in super resolution. In this paper we present a new sub-pixel registration method which makes use of the combination of mutual information measure criteria and the NEDI interpolation algorithm. Experimental results show that our proposed method can yield accurate registration results. When applying this registration results in SR image reconstruction, PSNR of the image estimated by our proposed method is 1−2 dB higher than those of the images reconstructed by other registration methods.

This paper presents a super-resolution method based on gradient-based adaptive interpolation. In this method, in addition to considering the distance between the interpolated pixel and the neighboring valid pixel, the interpolation coefficients take the local gradient of the original image into account. The smaller the local gradient of a pixel is, the more influence it should have on the interpolated pixel. And the interpolated high resolution image is finally deblurred by the application of wiener filter. Experimental results show that our proposed method not only substantially improves the subjective and objective quality of restored images, especially enhances edges, but also is robust to the registration error and has low computational complexity., Comment: 4pages, 7figures, This paper presents a super-resolution method based on gradient-based adaptive interpolation

In this paper, based on Circularly Symmetrical Gabor Transform (CSGT) and Principal Component Analysis (PCA), we propose a face hallucination approach. In this approach, all of the face images (both input face image and original training database) are transformed through CSGT at first and then local extremes criteria is utilized to extract the intrinsic features of the faces. Based on these features, we calculate Euclidean distances between the input face image and every face image in the original training database, and then Euclidean distances are used as criteria to choose the reasonable training database. Once the training database is chosen, PCA is applied to hallucinate the input face image as the linear combination of the chosen training images. Experimental results show that our approach can choose training database automatically according to the input face image and get high quality super-resolution image.