|Depth edge detection
Depth edges play a very important role in many computer vision problems because they represent object contours. We strategically project structured light and exploit distortion of the light pattern in the structured light image along depth discontinuities to reliably detect depth edges.
|Masked fake face detection
This research presents a novel 2D feature space where real faces and masked fake faces can be effectively discriminated. We exploit the reflectance disparity based on albedo between real faces and make materials. The feature vector used consists of radiance measurements of forehead region under 850nm and 685nm illuminations. Facial skin and mask material show linearly separable distributions in the feature space proposed.
|Face Recognition Using ICA
We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of ˇ°recognition by parts.ˇ±
|CBCT artifacts reduction
We research a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energydependent attenuation. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced artifacts.