Many methods have been proposed to reconstruct the moving object based on phase shifting profilometry. Quality reconstruction results can be achieved when a single moving object or multiple objects with same movement are measured. However, errors will be introduced when multiple objects with individual movements are reconstructed. This paper proposes an automated method to track and reconstruct the multiple objects with individual movement. First, the objects are identified automatically and their bounding boxes are obtained. Second, with the identified objects’ images before movement, the objects are tracked by the KCF algorithm in the successive fringe pattern after movement. Third, the SIFT method is applied on the tracked object images and the objects’ movement is described individually by the rotation matrix and translation vector. Finally, the multiple objects are reconstructed based on the different movement information. Experiments are presented to verify the effectiveness.

When implementing the phase shifting profilometry to reconstruct an object, the object is always required to be kept stable as multiple fringe patterns are required. Movement during the measurement will cause failed reconstruction. This paper proposes a general model describing the fringe patterns with any three-dimensional movement based on phase shifting profilometry. The object movement is classified as five types and their characteristics are analyzed respectively. Then, by introducing a virtual plane, the influence on the phase value caused by different types of movement is described mathematically and a new model including movement information is proposed. At last, with the help of the movement tracking and least-square algorithm, the moving object is reconstructed with high accuracy. The proposed method can remove the reference plane during the reconstruction of the moving object, which extends the application range of the phase shifting profilometry. The effectiveness of the proposed model is verified by the experiments.

Temporal phase unwrapping is among the most robust and efficient phase unwrapping methods in fringe projection profilometry. They can recover the fringe orders even in the presence of surface discontinuities. However, fringe order errors may occur due to phase noise and poor measurement conditions. Such errors often exhibit an impulsive nature and introduce errors to the absolute phase map. Most existing fringe order error correction methods detect and correct the errors in a pixel-by-pixel manner, which may under-utilize the correlation of the fringe orders on different pixels. In this paper, we propose a new method to cope with the fringe order errors associated with temporal phase unwrapping. By exploiting the low-rankness of the fringe order map and sparse nature of the impulsive fringe order errors, we develop a robust principal component analysis (RPCA)-based approach to remove the impulsive fringe order errors. Experiments demonstrate that the proposed method is valid in eliminating the fringe order errors.

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.

Phase shifting profilometry is an important technique for reconstructing the three-dimensional (3D) geometry of objects with purely diffuse surfaces. However, it is challenging to measure the transparent objects due to the pattern aliasing caused by light refraction and multiple reflections inside the object. In this work, we analyze the aliasing fringe pattern formation for transparent objects and then, propose to learn the front surface light intensity distribution based on the formation principle by using the diffusion models for generating the non-aliased fringe patterns reflected from the front surface only. With the generated fringe patterns, the 3D shape of the transparent objects can be reconstructed via the conventional structured light. We show the feasibility and performance of the proposed method on the data of purely transparent objects that are not seen in the training stage. Moreover, we found it could be generalized to other cases with local-transparent and translucent objects, showing the potential capability of the diffusion based learnable framework in tackling the problems of transparent object reconstruction.

Image saturation is a challenging problem for three-dimensional shape measurement of objects with shiny surfaces using fringe projection profilometry technology. This paper presents a new method based on projection of multiple fringe patterns, providing an effective solution to the problem. First, a set of phase shifted sinusoidal fringe patterns with different intensity levels are projected to the object of interest, and the reflections are captured by the camera. With the captured images a set of masks are created, which are able to pick up the unsaturated data from the captured images. The unsaturated data are then combined based on maximal Signal-to-noise Ratio (SNR) scenario, and the combined images are used to retrieve the phase information. Comparing to existing methods, the proposed technique is advantageous by improved measurement accuracy due to the improved SNR. The performance of the proposed method is verified by the experiments.