No abstract

The stair-phase-coding patterns have been widely used to determine the fringe order for phase unwrapping of the wrapped phase in three-dimensional shape measurement. Although the special coding sequence algorithm can achieve with a large number of codewords, it needs the current codeword and its adjacent codewords to jointly determine the fringe order. If any codeword of the grouped adjacent codewords is incorrectly recognized, it will result in many false fringe orders. It increases the probability of fringe order error in the decoding process. And it is challenging to significantly increase the number of codewords. To solve this problem, we propose an absolute phase measurement method based on bidirectional coding patterns. The wrapped phase of the object is obtained by four-step phase-shifting patterns, and the fringe order is obtained by bidirectional coding patterns. When generating the bidirectional coding patterns, we code two groups of stair phase with different frequencies along the horizontal direction, which respectively represent local fringe order and partition information. Then, we alternately repeat the two groups of stair phase along the vertical direction in the whole pattern to obtain the bidirectional coding patterns. Each local fringe order information and the corresponding partition information in a small region jointly determine the fringe order of pixels in this small region. Fringe order errors in a small region do not affect other regions. To verify the effectiveness of our method, we performed simulations and experiments. Simulation and experimental results show that our method is effective for objects with different sizes and isolated objects.

Background: Early identification of degenerative processes in Alzheimer’s disease (AD) is essential. Cerebello-cerebral network changes can be used for early diagnosis of dementia and its stages, namely mild cognitive impairment (MCI) and AD. Methods: Features of cortical thickness (CT) and cerebello-cerebral functional connectivity (FC) extracted from MRI data were used to analyze structural and functional changes, and machine learning for the disease progression classification. Results: CT features have an accuracy of 92.05% for AD vs. HC, 88.64% for MCI vs. HC, and 83.13% for MCI vs. AD. Additionally, combined with convolutional CT and cerebello-cerebral FC features, the accuracy of the classifier reached 94.12% for MCI vs. HC, 90.91% for AD vs. HC, and 89.16% for MCI vs. AD, evaluated using support vector machines. Conclusions: The proposed pipeline offers a promising low-cost alternative for the diagnosis of preclinical AD and can be useful for other degenerative brain disorders.

Although Deep learning-based Fringe projection profilometry (FPP ) has some success in in three-dimensional(3D) shape measurement,it still exists some difficult problems,especially, collecting a huge of training datasets is particularly troublesome and inconvenient.We introduce Blender software to simulate virtual scanning in 3D shape measurement, present a synthetic training datasets generating method.The proposed method can produce an effective training datasets that does not need real-world 3D scanning procedure.It can automatically produce deformed fringe patterns of the tested object in a very short time when a group of phase-shifting fringe pattern is projected onto the tested object in virtual scanning way,then calculate the wrapped phase using phase-shifting demodulation algorithm and obtain the 3D shape information from the continuous phase after the measurement system is calibrated.To verify the effectiveness of our method, we did simulations and real experiments. The results show that our method is effective for the measuring the complex object’s shape. Compared the training datasets obtained with real-world scanning,the network model trained by training datasets obtained from our proposed method has the similar accuracy and generalization ability, but our method is simple and fast in preparing training datasets for network.