Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.

The adaptive spatial filtering method is commonly adopted to extract the þ1 term spectrum in digital holography for real-time dynamic analysis. However, the typical filtering method is not satisfactory for automatic analysis, because the reset of the filtering window is needed to extract the area of the þ1 term spectrum. Therefore, an adaptive spatial filtering method based on region growing and the characteristic of the spectrum separation is proposed. Its filtering window is automatically formed by region growing. The key parameters, including threshold and seed point, are set by the intensity distribution of the hologram spectrum. Then the adaptive filtering extracting the þ1 term spectrum is realized by multiplying the hologram spectrum by the filtering window. Compared to the typical filtering method, the experimental results of a microhole array and a phase step show that the proposed method has better adaptability and a higher precision. Moreover, the applicability of this method for different uses is also demonstrated by experiments with a microhole array and a phase step. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms

No abstract

Abstract-The traditional village carries the important memory of the development of farming civilization in our country. To reasonable protect and utilize the traditional village, this paper takes Guangdong Province as an example, search for the differentiation and influence factors of 278 traditional villages, finally puts forward some suggestions to promote the overall protection of traditional villages. To estimate the differentiation, this paper uses the nearest neighbor index method, the kernel density analysis method and the hot spot analysis method. Then choose the terrain, hydrology and human factors as the starting point to test the influencing factors. The results visualized by ArcGis10. 2 shows that traditional villages tend to concentrated and have a greater correlation with terrain, hydrology and human factors.

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.