We suggest the method to recover the 3D shape of an object by using a color image scanner which has three light sources. The photometric stereo is traditional to recover the surface normals of objects using multiple light sources. In this method, it usually assumes distant light sources to make the optical models simple. But the light sources in the image scanner are so close to an object that the illuminant intensity varies with the distance from the light source, therefore these light sources should be modeled as the linear light sources. In this method, by using these models and two step algorithm; the initial estimation by the iterating computation and the optimization by the non-linear least square method, not only the surface normal but also the absolute distance from the light source to the surface can be estimated. By using this method, we can recover the 3D shape more precisely. In the experimental results, the 3D shape of real objects can be recovered and the effectiveness of the proposed method is shown.

This paper describes a new method to determine the 3-D position coordinates of a Lambertian surface from four shaded images acquired with an actively controlled, nearby moving point light source. The method treats both the case when the initial position of the light source is known and the case when it is unknown.

This paper proposes a new method to determine the shape of a surface by learning the mapping between three image irradiances observed under illumination from three lighting directions and the corresponding surface gradient. The method uses Phong reflectance function to describe specular reflectance. Lambertian reflectance is included as a special case. A neural network is constructed to estimate the values of reflectance parameters and the object surface gradient distribution under the assumption that the values of reflectance parameters are not known in advance. The method reconstructs the surface gradient distribution after determining the values of reflectance parameters of a test object using two step neural network which consists of one to extract two gradient parameters from three image irradiances and its inverse one. The effectiveness of this proposed neural network is confirmed by computer simulations and by experiment with a real object.

We describe an approach for modelling a person's face for model-based coding. The goal is to estimate the 3D shape by combining the contour analysis and shading analysis of the human face image in order to increase the quality of the estimated 3D shape. The motivation for combining contour and shading cues comes from the observation that the shading cue leads to severe errors near the occluding boundary, while the occluding contour cue provides incomplete surface information in regions away from contours. Towards this, we use the deformable model as the common level of integration such that a higher-quality measurement will dominate the depth estimate. The feasibility of our approach is demonstrated using a real facial image.