Notion of optical flow literally refers to the displacements of intensity patterns. In that sense, extracting interested information from 2D scene is analogy to modulation/demodulation in random signal processing. To address the limitations presented in computer vision based on static image, we propose a novel metal component defect detection method, specified as the instance of turbine blade surface detection, using optical flow estimation.To start the specified pattern recognition in 2D presentation, we modulate the brightness constancy assumption equation as illumination varying model, by sampling the second image with function whose frequency was chosen according to the Nyquist sampling theorem, and a sinusoidal factor was introduced as an additive factor. This tunable channel based on 2D image transfers intensity features into optical modes. Then, we implement optical flow estimation on two sequential images. Experimental results reveal grayscale space shows completness in representing the optical modes of turbine blade with various kinds of surface characteristics. By modifying the index of information content, we propose quantitative index to evaluate the performance of our method. Evaluation reveals optical flow algorithm is qualified to examine defects on highly reflective turbine blade, and our method extends the application of optical flow.

In this paper, a novel region-based active surface model in a level set framework is proposed for subthalamic nucleus segmentation on MRI. The method is an extension of region based active contour in which the joint prior information of the object shape and the imaging intensity are utilized to drive the surface evolution in a level set formulation for segmentation. The mean surface area calculated from labeled samples is used as prior constraints of the object to segment. This feature and the intensity difference between object and background define a region-based force that drives a set of 3D surfaces towards the optimal segmentation. Specially, the pre-segmentation of visible structure within the region of interest constitutes an important step of subthalamic nucleus segmentation and the result forms confinement for the evolution domain of the surface, which enhances the validity of data term in the model.