Digital Light Processing (DLP) 3D printing is implemented by projecting the image of the slice of the model on the exposure surface, and the projected image's accuracy will influence the printed object's quality. It is necessary to maintain the accuracy of the projected image. A prerequisite for high-precision printing is that the projector is horizontal. The existing methods calibrate the levelness of the projector by observing the level meter; the calibration precision cannot be guaranteed. In this paper, a vision-based scheme is proposed to evaluate the levelness of a projector and to estimate the rotation angles. Firstly, the hardware is designed to capture the projected image on the exposure surface. A pixel in the projected image is about 74*74 pixels in the captured image. Secondly, the possible variation of pixel relation in different situations is analyzed in theory. Then, the variation of pixel distance (or slope) by tilt angle is further analyzed. The corresponding variation curves are obtained by curve fitting. Finally, based on the corresponding curves, the tilt direction and angle of the projector can be estimated from the captured image. The experimental results demonstrate that the average error of the estimated tilt angle is smaller than 0.2 degrees.
The unstable quality of 3D printing products is one of the obstacles that hinder the popular application of 3D printing technology. In this paper, we propose a novel mask projection 3D printing scheme with visual surveillance. We introduce a vision system to monitor the gray level variation, so that two kinds of problems could be detected in time: 1. the printed objects are stuck on the bottom of the tank. 2. the printed objects are stretched out and part of objects are stuck on the bottom of the tank. The normal and abnormal printing procedures correspond to different gray level variation curves, which can be discriminated by classify algorithms. By the computing capability of our computer, we could monitor gray level variation curves at 470 points in real time. If 95 % curves are normal, the layer is thought successfully printed, the next layer will be printed continuously. Otherwise, this layer is abnormal, and the printing will be stopped. The experiment show that the proposed scheme can stop the abnormal printing in time. it can improve the production rate of the finished products and reduce the material waste.
KEYWORDS: Facial recognition systems, 3D modeling, Image segmentation, Detection and tracking algorithms, Mouth, Image processing algorithms and systems, Feature extraction, 3D image processing, Eye, Nose
Face recognition has higher performance with controlled illumination and pose. But in some applications such as video surveillance, imaging condition is uncontrolled and the subject is not cooperative. In this paper pose invariant face recognition in complex backgrounds is discussed and a framework is proposed. Our algorithm is comprised of four parts. In the first part a face location algorithm combining face feature and template is proposed to determine the face location, represented as center of eyes and mouth. In the second part a face segmentation algorithm using curve fitting is proposed to segment face region in the image. The third part is face normalization---to obtain a front view face from a face with variant pose. In the forth part, the face recognition based on normalized faces is implemented using eigenface method. The algorithm is tested using 70 images of 14 persons, the experimental results confirm the efficiency of our algorithms.
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