In this paper, a new embedded intelligent monitoring system based on face recognition is proposed. The system uses Pi Raspberry as the central processor. A sensors group has been designed with Zigbee module in order to assist the system to work better and the two alarm modes have been proposed using the Internet and 3G modem. The experimental results show that the system can work under various light intensities to recognize human face and send alarm information in real time.
In this paper, we present a novel binary descriptor with orientation, which called Intensity-Centroid LDB (IC-LDB). This descriptor resolves the problems that the current non-binary descriptors are too compute-expensive to achieve real-time performance in the nonlinear scale space and that the original Local Difference Binary (LDB) descriptors do not have an orientation component to keep rotation invariant. Experimental results demonstrate that IC-LDB proposed in this paper was faster than previously non-binary descriptors which were used in nonlinear scale space, while performing as well in many situations.
This paper proposes a novelty dense stereo matching method based on TC-MST (Threshold Constrained Minimum Spanning Tree), which aims to improve the accuracy of distance measuring. Due to the threshold has a great impact on the results of image segments, to select a better threshold, we adopt iteration threshold method. And then we uses MST to calculate the cost aggregation, and utilize the winner-take-all algorithm for the cost aggregation to obtain the disparity. Finally the method proposed is used in a distance measuring system. The experiment results show that this method improves the distance measuring accuracy compared with BM (block matching).