Binarizing historically degraded as-built drawing (HDAD) maps is a challenging job, especially in removing noise, yellowing areas, and folded lines while preserving the foreground components. This paper first proposes a convolutional neural networks-based (CNN-based) color classifier to determine the dominant color class of each HDAD block. Then, a dominant color driven- and CNN-based binarization method is proposed, producing a high-quality binarized HDAD map. Based on real HDAD dataset, the thorough experiments have been carried out to show that in terms of F-measure and perceptual effect, our binarization method substantially outperforms existing state-of-the art binarization methods.
In this paper, we propose a novel Vision- and system on chip (SoC)- based fall detection method for the elderly. Once, a fall event is detected, an alarm signal is immediately sent out to query first aid to the elderly. Our novel fall detection method consists of five effective steps: checking whether the light condition has been stabilized, GMM-based background and foreground estimation, a new strategy to solve the foreground lag problem, solving the false fall detection problem when light comes from a neighboring room, as well as the fall detection determination and the general-purpose input/output based warning mechanism. Based on the test videos, the experiments have been carried out demonstrate that our proposed fall detection method can meet the real-time, low cost, and high accuracy demands.