WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (AR) Technology provider, announced that it has developed a CNN (convolutional neural network) algorithm-based image recognition system.
CNN is a highly efficient recognition algorithm based on an artificial neural network. WiMi applies the CNN algorithm to image recognition technology, showing apparent advantages compared to the traditional machine learning algorithm. CNN realizes the construction of features by the computer itself, thus breaking through the bottleneck of the original way of classification. This has brought image recognition to a new level. In addition, CNN has a unique structure, which can use two-dimensional images as the input layer so that some essential features of the images will not be lost, thus improving image recognition accuracy.
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In CNNs, neurons in one layer are not connected to all neurons in the next layer. Instead, CNNs use a 3D structure in which each group of neurons analyses a specific region or ‘feature’ of the image. CNNs filter connections by proximity (analyzing pixels only for nearby pixels), allowing for a computationally sound training process. It consists of multiple stages of convolution and sampling, and then the extracted features are fed into the fully connected layer for the computation of classification results. The convolutional layer obtains the features of the image from the upper layer and the data on the unit nodes from each local area in the input layer, which need to cover the entire data set. CNNs can learn the invariant features of an image through the process of feature extraction and feature mapping.
CNN algorithm-based image recognition system perform well mainly because of their multi-layer network structure and pooling operations and their ability to produce the best possible results using less training time. CNNs generally consist of three or more neurons connected for training and inference. The convolutional layer is the core part of a convolutional neural network. The essence of convolution is to use the parameters of the convolution kernel to extract features from the data and obtain the result through matrix dot product operations and summation operations. In the fully connected layer, a linear stretching of the high-dimensional feature map allows the high-dimensional feature map to be transformed into a one-dimensional vector for classification or regression in the classifier. The activation function plays a crucial role in changing the mathematical relationship between the input and output data in the neural network. By adding the activation function, the output of the previous layer is first mapped by the activation function to obtain a non-linear process, which can improve the learning and expression capability of the network.
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The main advantages of WiMi’s system are as follows: firstly, it can extract features from multiple image datasets and select feature sets and elements from the datasets. Secondly, it can connect many small-scale units to learn a bunch of essential parameters by understanding the relationships between different scales and obtaining the optimal solution from them. Thirdly, it can be trained by learning other parts of the dataset so that more information can be extracted from the image dataset and additional feature information can be better utilized. In many practical tasks, CNNs use pooling layers for network connectivity to obtain the desired features and ultimately for target detection or target recognition; or share training results between different layers for tasks such as multi-classification, regression, image classification, etc.
Image recognition technology is an important area of artificial intelligence. It has great significance in research and applications in many fields, such as navigation, resource analysis, environmental monitoring, and medical research. In the future, WiMi will continue to expand the application scenarios of its developed CNN algorithm for image recognition systems.
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