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In this paper, we provide a new spatial data generalization method which we applied in hand gesture recognition tasks. Data gathering can be a tedious task when it comes to gesture recognition, especially dynamic gestures. Nowadays, the standard solutions when lacking data still consist of either the expensive gathering of new data or the impractical employment of hand-crafted data augmentation algorithms. While these solutions may show improvement, they come with disadvantages. We believe that a better extrapolation of the limited data’s common pattern, through an improved generalization, should first be considered. We, therefore, propose a dynamic generalization method that allows to capture and normalize in real-time the spatial evolution of the input. The latter procedure can be fully converted into a neural network processing layer which we call Evolution Normalization Layer. Experimental results on the SHREC2017 dataset showed that the addition of the proposed layer improved the prediction accuracy of a standard sequence-processing model while requiring 6 times fewer weights on average for a similar score. Furthermore, when trained on only 10% of the original training data, the standard model was able to reach a maximum accuracy of only 36.5% alone and 56.8% when applying a state-of-the-art processing method to the data, whereas the addition of our layer alone permitted to achieve a prediction accuracy of 81.5%.

In this paper, we present a method (Hybrid-Pose) to improve human pose estimation in images. We adopt Stacked Hourglass Networks to design two convolutional neural network models, RNet for pose refinement and CNet for pose correction. The CNet (Correction Network) guides the pose refinement RNet (Refinement Network) to correct the joint location before generating the final pose. Each of the two models is composed of four hourglasses, and each hourglass generates a group of detection heatmaps for the joints. The RNet model hourglasses have the same structure. However, the CNet model is designed with hourglasses of different structures for pose guidance. Since the pose estimation in RGB images is very sensitive to the image scene, our proposed approach generates multiple outputs of detection heatmaps to broaden the searching scope for the correct joints locations. We use the RNet model to refine the joints locations in each hourglass stage horizontally, then the heatmaps of each stage are fused with the heatmaps of all the CNet model hourglasses vertically in a hybrid manner. Our method shows competitive results with the existing state-of-the-art approaches on MPII and FLIC benchmark datasets. Although our proposed method focuses on improving single-person pose estimation, we also show the influence of this improvement on multi-person pose estimation by detecting multiple people using SSD detector, then estimating the pose of each person individually.

In machine learning, few shot learning techniques try to generate high prediction accuracy from few training examples. One of those techniques is data augmentation. In this paper, we propose a method for land cover image classification using a conditional generative adversarial network (cGAN) starting from few training samples. First, we train a cGAN with a small dataset to classify images by mapping each pixel of the input image to its class. Second, we augment the images of the dataset by generating new samples using a traditional GAN model, then the labels of the new samples are generated using the previously trained cGAN model. The new augmented labeled data is associated with the original dataset to train the cGAN model again with more samples to improve the classification accuracy. Training experiments were performed on SPARCS publicly available dataset. We tested the proposed method on images of Landsat 8 and Alsat 2 satellites. The classification results show that the performance after data augmentation is better than using the original small dataset.

Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems.

In this article, we propose a Tai Chi training system based on pose estimation using Convolutional Neural Networks (CNNs) called iTai-Chi. Our system aims to overcome the disadvantages of insuffici...