1 This paper presents a new approach to fringe pattern profilometry. In this paper, a generalized model for describing the relationship between the projected fringe pattern and deformed fringe pattern is derived, where the projected fringe pattern can be arbitrary rather than being limited to be sinusoidal as those for the conventional approaches. Based on this model, a new approach is proposed to reconstruct the three-dimensional object surface by estimating the shift between the projected and deformed fringe patterns. Additionally, theoretical analysis and computer simulation results are presented, both of which show the proposed approach can significantly improve the measurement accuracy, especially when the fringe patterns are distorted by unknown factors.

We present an algorithm for estimating the color demixing matrix based on the color fringe patterns captured from the reference plane or the surface of the object. The advantage of this algorithm is that it is a blind approach to calculating the demixing matrix in the sense that no extra images are required for color calibration before performing profile measurement. Simulation and experimental results convince us that the proposed algorithm can significantly reduce the influence of the color cross talk and at the same time improve the measurement accuracy of the color-channel-based phase-shifting profilometry.

Recently, a variety of methods using the Generative Adversarial Network (GAN) for face editing have been proposed. However, the existing methods cannot control the editing content of the face elements according to the user-specified attributes or need to train a conditional GAN for editing tasks, which means it is difficult to add new attributes in the future. In this paper, a method to edit face attributes by editing the latent variable with the help of a pre-trained unconditional GAN and a linear classification model is proposed. In particular, face attribute editing is divided into two separate stages: Firstly, based on the optimization function, the generative model does a latent variable search to generate a high-quality face image that is similar to the input image. Secondly, by editing the latent variable of the GAN, the attribute of the generated face image can be modified indirectly, so it is almost unaffected by the training process and network structure of GAN, which means it is a flexible method for nearly all GAN network. Images of the FFHQ dataset are edited by attribute labels defined in Celeba dataset for experiments. These experiments prove that our method can edit a variety of face images that vary with race, gender, age, and camera shooting angle. The overall quality of the edited image is not inferior to the other face attribute editing method, and attribute classification for edited image shows a 92.6% attribute editing success rate of the proposed method.

This paper presents a generalized analysis model for fringe pattern profilometry. We mathematically derived a new analysis model that gives a more general expression of the relationship between projected and deformed fringe patterns. Meanwhile, based on the proposed generalized model, a new algorithm is presented to retrieve 3-D surfaces from nonlinearly distorted fringes.Without any prior knowledge about the projection system, we still can obtain very accurate measurement results by using a generalized analysis model and a proposed algorithm. Computer simulation and experimental results show that the generalized model and the proposed algorithm can significantly improve the 3-D reconstruction precision, especially when the projected fringe pattern is nonlinearly distorted. Disciplines Physical Sciences and Mathematics