Figure - available from: International Journal of RF and Microwave Computer-Aided Engineering
This content is subject to copyright. Terms and conditions apply.
Source publication
Due to millimeter‐wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non‐contact inspection of the human body's surface. Therefore, it is of great significance to study the security inspection equipment and target recognition technology based on millimeter wave imaging. In this paper, an active a...
Citations
... Literature [21], in order to solve the problem of scene recognition, explores the application ability of the dataset of PLACES2, which contains close to 7 million images of various scenes, which can be used as inputs for training and testing the model, and design the neural architecture of the machine through convolutional neural network, which is used to store the images required for scene recognition, and conduct simulation experiments after a large number of training to verify the reliability of the dataset. Literature [22] proposed a multi-feature fusion method based on weighted sequence fusion to obtain fused feature vectors of active and passive millimeter-wave urban and rural areas, which is applied with millimeterwave imaging target recognition, and the performance of this method is proved to be better than that based on the original feature vectors. Literature [23] adopts the method of RGB and NIR image fusion in order to improve the ability of scene classification, on the basis of which it proposes the technique based on the improvement of visually salient points, and after simulation experiments, it is proved that the comprehensive performance of this method has been improved to the original method. ...
In this paper, the image Gabor features extracted by Gabor wavelet are fused with the image grayscale map to construct the enhanced Gabor features, and then combined with the characteristics of Gabor wavelet and convolutional layer, the Gabor feature extraction module, parallel convolution module and spatial transformation pooling module are designed. The corresponding Gabor convolutional layer and Gabor convolutional neural network are constructed using the appropriate module in accordance with the image recognition task application scenario. The convex set projection image super-resolution reconstruction method is used in this paper to improve the resolution of images with low resolution. The construction of a computerized image recognition system involves combining a Gabor convolutional neural network and a convex set projection method. This system has been tested and found to have a recognition accuracy of 93.5% for object images. This system’s ability to accurately recognize low-resolution shadow-obscured face images is possible thanks to using the convex set projection method to reconstruct the image and recognize it accurately with an accuracy of up to 93.85%. This system’s recognition performance for complex images has been proven through experiments.
