WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that it proposed hologram classification based on 3D CNN. 3D CNN is a model that extends the traditional CNN to better handle three-dimensional data. Compared with traditional image classification methods, 3D CNN can better capture the spatial and temporal information of holograms, extract richer features from holograms, and achieve accurate classification and recognition by fully utilizing the three-dimensional features of holograms.
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WiMi’s 3D CNN-based hologram classification, which firstly needs to convert holograms into 3D data format, and then extract features through multi-layer convolution and pooling operations. Next, the extracted features are mapped to different classes using fully connected layers and softmax functions to realize the classification of holograms. Specific technical application steps include data preprocessing, network architecture design, model training and optimization, and model evaluation.
Data pre-processing: First, we will pre-process the hologram data. The hologram is represented as a 3D data structure containing multiple slices or voxels, which can be viewed as representations of different depths or time steps of the image. During the processing of the input data, the 3D data of the hologram needs to be converted into an input format suitable for 3D CNN modeling, e.g., by converting the hologram into multiple 2D slices or by converting it into 3D volumes. We will also standardize and normalize the data to ensure that the input data have similar scales and ranges.
Network architecture design: Next, WiMi will design a 3D CNN network architecture suitable for hologram classification. Different from the convolutional operation in 2D CNN, the convolutional operation in 3D CNN needs to be performed in three dimensions to capture the 3D features in the hologram, this network will contain multiple convolutional layers, pooling layers and fully connected layers for the downscaling and classification tasks. In the 3D convolution operation, the convolution kernel will slide over all depths of the hologram and extract features. These features will be passed to the next layer for further extraction of higher-level features. The pooling layer is used to reduce the size of the feature map to reduce the computational complexity of the model. Finally, the fully connected layer maps the extracted features to the corresponding categories to accomplish the classification task of the hologram.
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Model training and optimization: Once the network architecture is designed, we will use the labeled hologram dataset for model training. A loss function will also be used as the objective function and a back-propagation algorithm will be used to update the network parameters, while some optimization techniques will be utilized to improve the performance of the network.
Model evaluation: After the model training is completed, we will use the hologram data to evaluate the performance of the trained model and will compute the model accuracy, precision and other metrics to evaluate the classification ability of the model. In addition, a visualization model will be drawn to evaluate the performance of the model.
WiMi can realize automatic classification and recognition of holograms by training a 3D convolutional neural network model. This technology can be applied to a variety of fields such as medical image processing, video analysis, and virtual reality. In the future, with the continuous development of deep learning and computer vision technology, hologram classification technology based on 3D convolutional neural networks is expected to be more widely used in the technology industry.
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