KEYWORDS: 3D printing, 3D modeling, Printing, Data storage, Data modeling, 3D image processing, Image processing, Detection and tracking algorithms, Floods, 3D applications
Traditional 3D printing usually uses professional software such as SolidWorks and Maya for modeling. However, applying the software mentioned above to customize 3D food printing patterns has the problems of a solid professional operation and high skill requirements. In response to this problem, a pattern drawing software applied to 3D food printing equipment was designed. The software uses image processing technology and C# programming language, based on Windows operating system and. NET Framework, combined with SQLite database technology to achieve product pattern drawing and model export functions. Among them, the automatic extraction of the enclosed area realized by the flood filling algorithm is the core function of product pattern drawing; the automatic acquisition of the contour of the connected graph recognized by the canny edge detection algorithm is the crucial step of the model derivation. The test results show that the software can quickly implement template import, pattern drawing, model export, and other operations, which improves the efficiency and ease of operation of pattern drawing in 3D food printing; The model file exported by the software can be directly used in the layered printing process of a 3D printer, which provides a direction for promoting the application of 3D printing technology in the food industry.
To further promote the improvement of pedestrian re-identification performance, this paper studies the reid framework based on "reid-strong-baseline", and uses different optimization schemes to improve the network performance. Firstly, the study tests three kinds of loss: Softmax, triplet hard, and Softmax + triplet hard, to verify the Rank-1 performance obtained and which can achieve the best performance. Secondly, based on the prototype network obtained by applying Softmax + triplet hard loss, we utilize several optimization methods including data enhancement, learning rate optimization, sampling method, and Label smoothing. Then we study the effectiveness of these optimizations on the performance of the Baseline model and the degree of improvement. Finally, this paper studies the efficiency of different Backbone and network depths on the performance of pedestrian re-identification.
In order to effectively solve the problems of insufficient storage capacity, too late to shoot wonderful moments, and limited range of shooting space, a target video intelligent processing system based on Raspberry Pi is proposed. The system runs on the Raspberry Pi, and drives the camera to shoot a wider range of indoor environment video streams through the servo gimbal. For each frame of image in the video stream, image grayscale, filter denoising and histogram, equalization techniques are used for preprocessing. In the target detection and tracking stage, first use the OpenCV machine vision library to call the MobileNet lightweight convolutional neural network and SSD algorithm combined model (MobileNetSSD) for target detection, then it can alculate the relative position of the camera center focus and the target center, and finally drive the camera to track the target object. In terms of video processing, with the help of the results obtained during target detection, the automatic video editing process is completed by discarding the image frame without target object. Experiments show that the system can quickly and accurately track the target object to shoot, and effectively reduce the storage capacity of the video.
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