Head wearing, no difference from ordinary glasses.
Guide a reasonable diet with a personal digital health library.
A food image recognition method based on DCNN and transfer learning. According to the pre-trained DCNN model on the ImageNet image dataset, the network parameters are initialized, and then the fine-tuning training method is used to perform transfer learning on the self-built small-scale food image database set in order to obtain high-level attribute features of the food image. Finally, the high-level attribute features learned by DCNN are input to a linear support vector machine for classification of food images. The experiment proves that the accuracy of western-style restaurant recognition is 94.20%.
Adopting video DTG perspective technology, high dynamic range image acquisition through binocular camera, real-time rendering through computer algorithms, new "realistic pictures" are obtained and presented from a large perspective. Through cloud computing, overlay some virtual images to achieve AR, or fully overlay virtual images to achieve VR.