WiMi Hologram Cloud to Introduce A 3D-CNN-based Hologram Classification Algorithm

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[{"type":"text","content":"BEIJING, June 16, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider, today announced the development of a 3D-CNN hologram classification algorithm based on deep learning. This technique uses a convolutional neural network and computer vision to build classifiers for classifying targets in holograms.\nUsing a 3D stereo hologram as input can capture the shape and spatial features of the target more accurately. The hologram image passes through convolutional, pooling, and fully connected layers, from which the algorithm extracts feature information and filters and optimizes it layer by layer. This enables fast and accurate automatic recognition and classification of 3D objects. 3D-CNN can efficiently extract 3D features of multiple resolutions and combine them to improve classification performance. When training the model, supervised learning is performed using labeled holograms, and a back-propagation algorithm optimizes the model parameters.\nThe 3D-CNN-based hologram classification technology provides essential technical support for object recognition by training neural network models to achieve fast and accurate classification of holograms with the advantage of deep learning. The implementation steps of this algorithm technology include: first, the hologram is feature extracted and pre-processed to transform it into 3D tensor data. Then, the 3D-CNN is used to train and learn the features of the hologram and extract its high-level semantic features. Finally, a classifier is used to classify the obtained features to achieve the automatic classification of the hologram.\nWiMi's 3D-CNN-based hologram classification technology can adapt to the particular characteristics of holograms and better handle the 3D and wavefront information of holograms. Its use of deep neural networks can extract more feature information to achieve higher accuracy classification. 3D-CNN can utilize GPU for efficient parallel computing and high training efficiency. Moreover, it will scale with the increased data size, which can handle more data and obtain better classification results.\nThe 3D-CNN-based hologram classification algorithm has many applications and development prospects in several fields. Meanwhile, its technical principle can als...

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