Preserving cultural heritage and historic sites is an important problem. These sites are subject to erosion, vandalism, and as long-lived artifacts, they have gone through many phases of construction, damage and repair. It is important to keep an accurate record of these sites using 3-D model building technology as they currently are, so preservationists can track changes, foresee structural problems, and allow a wider audience to "virtually" see and tour these sites Due to the complexity of these sites, building 3-D models is time consuming and difficult, usually involving much manual effort. This paper discusses new methods that can reduce the time to build a model using automatic methods. Examples of these methods are shown in reconstructing a model of the Cathedral of Ste. Pierre in Beauvais, France.'

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For the development of intelligent transportation systems, if real-time information on the number of people on buses can be obtained, it will not only help transport operators to schedule buses but also improve the convenience for passengers to schedule their travel times accordingly. This study proposes a method for estimating the number of passengers on a bus. The method is based on deep learning to estimate passenger occupancy in different scenarios. Two deep learning methods are used to accomplish this: the first is a convolutional autoencoder, mainly used to extract features from crowds of passengers and to determine the number of people in a crowd; the second is the you only look once version 3 architecture, mainly for detecting the area in which head features are clearer on a bus. The results obtained by the two methods are summed to calculate the current passenger occupancy rate of the bus. To demonstrate the algorithmic performance, experiments for estimating the number of passengers at different bus times and bus stops were performed. The results indicate that the proposed system performs better than some existing methods.

Statistics have shown that most fall events are associated with identifiable risk factors, such as weakness, unsteady gait, medication use, and the environment. Falls can result in abrasions, broken bones, or even death. A real time fall detection system should be developed, which can trigger an alarm people once a fall event occurs. In this study, the proposed scheme obtains image sequences from an interior camera system. The imaged are first used to build up a model of the background using Gaussian mixture model (GMM) with the extraction of foreground images achieved through subtraction. Morphological operations are then used to repair damage to the image and connected-component labeling is used for elimination of noise. From foreground objects, the aspect ratio of the bounding box, the orientation of the ellipse, and the vertical velocity of the center point are extracted for use as input features in a learning algorithm. Fall detection is based on the classification results of learning algorithm using a back propagation neural network.

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