WiMi Hologram Cloud Inc (WIMI)

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WIMI (NASDAQ: WIMI) launched a 3D-CNN-based hologram classification algorithm.

Source
https://t.cj.sina.com.cn/articles/view/1747383115/6826f34b020018pra?from=tech

May 18, 2023

A hologram is a holographic interferogram that records an object. It has very rich optical information and can reconstruct the three-dimensional structure of the object at different angles. It has the characteristics of high pixel density, wide field of view, and deep depth of field. Due to its high similarity to the three-dimensionality of real objects, it is widely used in many fields, including medical imaging, material science, and three-dimensional display technology. Hologram classification is an important technology to extract objects and information in holograms, which can be used to judge the type or state of objects.

It is reported that WIMI (NASDAQ: WIMI) has developed a three-dimensional convolutional neural network (3D-CNN) hologram classification algorithm based on deep learning technology, which uses convolutional neural network technology and computer vision to build a classifier. A technique to classify objects in a hologram. Using a three-dimensional holographic image as input, it captures the shape and spatial characteristics of the target more accurately, extracts feature information through operations such as convolutional layers, pooling layers, and fully connected layers, and screens and optimizes them layer by layer to achieve three-dimensional Fast and accurate automatic identification and classification of objects.

3D-CNN can efficiently extract 3D features at multiple resolutions and combine them to improve classification performance. When training the model, the labeled hologram is used for supervised learning, and the model parameters are optimized through the backpropagation algorithm. The hologram classification technology based on 3D-CNN takes advantage of deep learning to realize fast and accurate classification of holograms by training the neural network model, which provides important technical support for object recognition.
The implementation steps of the algorithm technology include:
- first, feature extraction and preprocessing of the hologram, and transform it into three-dimensional tensor data;
- then, use 3D-CNN to train and learn the features of the hologram, and extract its high-level semantics features;
- finally, a classifier is used to classify the obtained features to realize automatic classification of holograms.

WIMI's 3D-CNN-based hologram classification technology can adapt to the particularity of holograms and better handle the three-dimensional information and wavefront information of holograms. It uses deep neural networks to extract more feature information, thereby Achieve higher accuracy classification. 3D-CNN can use GPU for efficient parallel computing, high training efficiency, and expands with the increase of data size, can process more data and obtain better classification results.

The hologram classification algorithm based on 3D-CNN has wide application and development prospects in many fields. At the same time, its technical principles can also be applied to the classification or processing of other 3D images, which has good promotion value. At present, hologram classification technology based on 3D-CNN has been widely used in the fields of automatic driving, medical image diagnosis, intelligent security, virtual reality and so on.

In automatic driving, hologram classification can identify objects such as vehicles, pedestrians, and traffic lights on the road, thereby helping automatic driving decision-making and realizing functions such as vehicle automatic driving, safety detection, and path planning.
In medical image diagnosis, hologram classification can analyze and diagnose medical images, help doctors make a diagnosis quickly and accurately, and improve the work efficiency of doctors.
In intelligent security, 3D-CNN-based hologram classification technology can be used for character recognition, behavior analysis, etc., to improve monitoring effects and early warning capabilities.
In virtual reality, hologram classification can realize object recognition in the virtual world, thereby enhancing the realism and interactivity of virtual reality.

With the continuous development and deepening of artificial intelligence technology, the application field of hologram classification technology based on 3D-CNN will continue to expand, and its application in intelligent transportation, intelligent medical care, intelligent security, virtual reality and other fields will bring more More convenience and innovation.

However, there are also some challenges in the 3D-CNN-based hologram classification algorithm technology. It faces problems such as difficult data acquisition, high computational complexity, and model parameter optimization, and requires continuous exploration and research on solutions. In the future, WIMI will further study how to improve the performance and efficiency of 3D-CNN-based hologram classification algorithm technology, and continue to expand its application scenarios.