Ongoing efforts at our laboratory aim at establishing a gesture recognition system to control the movement of mobile robot The proposed work is recognizing several predefined gestures by using a sequence of depth images which obtain from Kinect We present a new method to recognize hand gesture including five main steps: gestures segmentation, feature extraction and matching, establish 3D coordinate, trajectory extraction and classification. Experimental results are shown that the system can be used to effectively control the movement of the mobile robot.

At present, digital image is the most significant information carrier on the Internet, which provides convenience for people to exchange information. With the progress of image processing theory, digital image inpainting technology has also developed rapidly. It not only enhances the image expression but also provides tools for image forgery. Misusing the tools may trigger a crisis of trust in the image. To eliminate the negative impact of image forgery, many scholars have conducted in-depth research on it and proposed a series of detection methods called image forensics for institutions or communities to identify such forged images. Current forensic technology still has many limitations, such as relying on specific features and data distribution which makes forensic technology lag behind image inpainting technology. So far, academia still lacks a unified understanding of image forgery and detection technology, and the research architecture of inpainting and forensic technology is not clear. This article reviews the development of image inpainting and forensic technology and systematically summarizes and scientifically classifies the current research work. Finally, the forensic technology is summarized and the future research direction is prospected to guide subsequent scholars to further promote the progress of image forensic technology.

A Modeling Approach for Energy Saving Based on GA-BP Neural Network Energy saving;Model;Software runtime characteristics;GA-BP neural network;DVFS;

Now, more and more mobile smart devices are emerging massively; energy consumption of these devices has become an important consideration due to the limitation of battery capacity. Displays are the dominant energy consuming component of battery-operated devices, giving rise to organic light-emitting diode (OLED) as a new promising display technology, which consumes different power when displaying different content due to their emissive nature. Based on this property, we propose an approach to improve image energy efficiency on OLED displays by perceiving image content. The key idea of our approach is to eliminate undesired details while preserving the region of interest of the image by leveraging the color and spatial information. First, we use edge detection algorithm to extract region of interest (ROI) of an image. Next, we gradually change luminance and saturation of region of noninterest (NON-ROI) of the image. Then we perform detailed experiment and case study to validate our approach; experiment results show that our approach can save 22.5% energy on average while preserving high quality of the image.