Three-dimensional (3D) reconstruction from images has been one of the fundamental problems in the domain of computer vision. Shape from shading (SFS) is an approach to obtain the shape of an object from a single intensity image. This paper presents a 3D reconstruction approach for endoscope images using a fast SFS method. A new image irradiance equation of endoscope images is established based on the facts that the image is formed under perspective camera projection and the surface is illuminated by a near point light source. The light source is assumed to be located at the optical center of the camera and the attenuation of the illumination is taken into account. Then the image irradiance equation is derived as a nonlinear partial differential equation (PDE), which is associated with a static Hamilton–Jacobi equation considering the boundary conditions. The viscosity solution of the resulting Hamilton–Jacobi equation is approximated by using a new iterative fast marching method. The proposed approach is evaluated on both synthetic and real medical endoscope images and the experimental results show that the proposed approach is fast and accurate.

Abstract. In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left hand little finger or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p-value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing non-stationarity of ECoG signal.