In this paper, a novel method is introduced to estimate height from surface normal and implemented in a photometric 3D reconstruction system, base on the improved optimization function in the height domain. To avoid the global distortion from local integration, a pyramid reconstruction ideal is proposed, which sample the surface every certain points to form the separated point sets called sub-surfaces, and then step by step the coarser sub-surfaces is combined into finer ones and eventually derive the whole surface. Experiment shows that our method is faster and more accurate than some of the main method in this field.

The reliability of facial recognition techniques is often affected by the variation of illumination, such as shadows and illumination direction changes. In this paper, we present a novel framework, called the self-quotient image, for the elimination of the lighting effect in the image. Although this method has a similar invariant form to the quotient image by Shashua etc. (2001), it does not need the alignment and bootstrap images. Our method combines the image processing technique of edge-preserved filtering with the Retinex applications of by Jobson, et al., (1997) and Gross and Brajovie (2003). We have analyzed this algorithm with a 3D imaging model and formulated the conditions where illumination-invariant and -variant properties can be realized, respectively. A fast anisotropic filter is also presented. The experiment results show that our method is effective in removing the effect of illumination for robust face recognition.

Abstract Two approaches, image‐space and object‐space, exist for non‐photorealistic rendering (NPR). The object‐space approach has an advantage that it is able to access the 3D shape information of the scene with a 3D model. In this paper, we present a non‐stroke based image‐space technique that has the strength similar to that of the object‐space approach, but without involving explicit 3D models. The basic idea is to factorise the Lambertian model to obtain the shape and texture information implicitly contained in the image. We also develop a specific anisotropic low‐pass image filter, which can smooth an image without blurring important features. With the developed techniques, we present two NPR styles, the sketching style and the painting style. Although our work does not aim to mimic any specific existing art form, the sketching style can generate effects similar to those of the pen‐and‐ink and the painting style is able to produce the colour permeating effects of watercolour. Copyright © 2004 John Wiley & Sons, Ltd.

We present a new algorithm for recognizing face images under different lighting conditions. Instead of the commonly used methods of modeling the 3D shape or finding some illumination invariant, the basic idea of our algorithm is to create new synthetic images by simulating lighting effects of the input image for each candidate image in the database, and then checking correlation between the input image and the resulting synthesized images. With our method, only a single image is needed for each face in the database, which significantly decreases the size of the database. Theory analysis and experimental results show that this algorithm can strikingly enhance the recognition rate and can be used for real time applications.

To deal with image variations due to illumination problem, recently Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them had the assumption that the 3D surface normal and albedo are known. This assumption limits this algorithm's application. In this paper, we present a novel method for modeling 3D face shape and albedo from only three images with unknown light directions and this work well fills the blank, which Ramamoorthi and Basri left. By taking the advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.

To deal with image variations due to illumination problem, Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them assume that the 3D surface normals and albedo (or unit albedo) are known, which limit this algorithm's application. We present a novel method for modelling 3D face shape and albedo from only three images and this work will fill the blank which Ramamoorthi and Basri left. Our work is closely related to photometric stereo, but conditions of photometric stereo for estimating albedo and surface normal are too strict to be applied for real application. Moreover, the conventionally used singular value decomposition (SVD) approach leads to the notorious linear ambiguity and the solution to this problem needs to introduce more constraints or more images. By taking advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.