No abstract

This paper presents a new method for video segmentation using deep learning neural networks in the quaternion space into sets of objects, background, static and dynamic textures. We introduce a novel quaternionic anisotropic gradient (QAG) which can combine the color channels and the orientations in the image plane. The local polynomial estimates and the ICI rule are used for QAG calculation. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. Using the QAGs, we extract the local orientation information in the color images. Second, to improve the segmentation result we applied neural network to this derived orientation information. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. Experimental comparisons to stateof-the-art video segmentation methods demonstrate the effectiveness of the proposed approach.

Currently, there are many options for controlling robotic devices. Human-machine interaction is a key component of the control infrastructure. The most common solution is mobile devices or embedded touch screens, as well as next generation virtual reality devices. In human-machine interaction, most input devices are controlled manually, which is not always convenient, and sometimes even impossible. One option is gesture control, which has become increasingly common in the last few years. This artificial cognitive "sensory perception" or ability is a communication channel between a human and a machine. This article presents a two-steps approach to real-time control robotic devices. The first step is the hand recognition method base on palm detection (SSD Detector) and hand landmark models. After a palm detection, the hand landmark model performs fine localization of the key points of the 3-D coordinate of the hand inside the detected areas of the hand through regression and direct coordinate prediction. The model learns a consistent internal representation of the hand posture and is resistant to even partially visible hands and self-occlusions. The second human gesture recognition step is based on obtaining the coordinates of the hand, the distance from the camera to the hand in space, raised, lowered fingers and other indicators that allow you to accurately determine the shown gesture. In terms of gesture recognition accuracy, the proposed real-time system is better than the state-of-the-art methods.