Human interaction recognition using spatial-temporal salient feature

Multimedia Tools and Applications - Depth sensor is widely used today and has great impact in object pose estimation, camera tracking, human actions, and scene reconstruction. This paper presents a...     

Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

基于边缘和局部熵融合的视觉安全性评估方法

针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性，引入图像的边缘特征，通过共有边缘来衡量加密图像与原始图像的边缘相似度，消除了块效应。由于局部熵对加密等级低的图像不敏感，边缘相似度对加密等级高的图像不敏感，将两个评估方法进行自适应融合，提出[SLEES]（Local Entropy and Edge Similarity，[SLEES]）指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试，实验结果表明，[SLEES]指标相比传统评估指标有更好的鲁棒性，评估范围更广。     

Intelligent generation of indoor topology (i-GIT) for human indoor pathfinding based on IFC models and 3D GIS technology

Indoor topology is the basis for most indoor location-based services, such as wayfinding in shopping malls, escape route planning, and passenger transfer in transport stations. Currently, however, indoor networks for wayfinding by people have not yet been developed to a standardized representation as street maps have been, but many studies have attempted to develop indoor network models for indoor LBS.A complete indoor network includes both floor-level paths and non-level paths, such as stairs, elevators, escalators, ramps, and so on. Most existing studies, however, have explored floor-level paths; few studies have considered non-level paths. Many studies have used 3D building models based on Industry Foundation Classes (IFC) to develop algorithms for generating paths, but most of them require the IFC model to contain semantic elements for the spatial relationships among building components, which means extra efforts are needed when preparing the IFC Model from the original building model. Moreover, most algorithms dealing with floor-level paths use simplified floor plans as the input, which are different from the real data retrieved directly from IFC models. All of these requirements and assumptions add to the cost of producing an indoor network.In this study, we propose an approach called i-GIT to produce a graph-based indoor network including floor-level and non-level paths from IFC-based building models. i-GIT requires only the geometric information of IFC data models to automatically identify indoor space boundaries as well as to produce six categories of indoor paths. The innovation of this study is that it includes novel algorithms to produce non-level paths, and introduces the polygon regularization on indoor space boundaries to reduce the number of excessive nodes before applying Constrained Delaunay Triangulation to produce level paths.Three campus buildings and a Taiwan High Speed Rail (THSR) station were chosen to validate the proposed approach. Multiple OD pairs in one of test cases were randomly selected to find the shortest routes using the paths generated by i-GIT. Compared with paths manually drawn using the principles of Medial Axis Transform (MAT) and the route lengths from actual measurement, the average path availability is higher than 96%, while the average error of route lengths based on generated paths is approximately 5%. The results showed that i-GIT was able to accurately and effectively produce a complete indoor network.     

基于各向异性热扩散方程的多聚焦图像融合算法

本文根据多聚焦图像的光学成像原理,利用各向异性热扩散方程来估计图像深度信息,并讨论一种基于深度信息提取的多聚焦图像的融合算法.算法首先对两幅多聚焦图像进行光学成像过程的模拟,分别建立正向区域的热扩散方程,通过对热扩散方程的迭代求解估计出图像场景的深度信息.然后对深度信息进行自适应的区域划分,分别得到每幅图像的清晰区域,模糊区域和中间过渡区域.最后通过提取清晰区域的像素点,融合过渡区域的像素点实现多聚焦图像的融合.理论推导和实验验证表明,本方法能够克服常用算法中出现的块效应和人工痕迹,是一种有效的图像融合算法.     

GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target’s locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture.Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples’ fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity.     

某大视场机载摆扫红外扫描仪几何成像仿真与误差分析

为了分析大视场高空间分辨率红外多光谱扫描仪系统误差的影响，为检校方案的确定提供依据，通过利用严格成像模型对机载摆扫红外扫描仪进行成像仿真分析。针对红外扫描仪摆扫系统中相机投影中心与稳定平台回转中心不重合的设计特点，重点研究相机安置误差与POS系统安置误差的相关关系。仿真实验表明:相机安置误差与POS系统安置误差对定位精度影响规律基本一致，两者存在较强相关性但随着摆扫角度增大而减小；在摆扫幅度小于20 时，相机安置误差可合并到POS系统安置误差。该结论可为后期的检校方案设计提供参考。     

Multi-scale qualitative location: A direction-based model

Qualitative locations describe the locations of spatial objects by relating them to a reference frame with qualitative relations. Existing models concerned with regional partitions are mainly topology-based and do not consider the effects of scale changes on locations. This study develops a direction-based multi-scale qualitative location (DMQL) model to fill this gap. First, a cell partition is defined by extending the borders of the minimum bounding rectangles of the regions in a regional partition. Relating spatial objects to all regions by a set of directions is equal to representing the objects as a set of cells in a cell partition. Second, due to the multiple cell representations of spatial objects and the changes in direction relations across scales, some approaches are presented to derive the direction changes between regions in different frames, between spatial objects and regions, and between spatial objects at different scales. Third, the location and relation consistencies of qualitative locations are evaluated based on the cell representations of spatial objects at multiple scales through a case study. The results indicate that the DMQL model can locate objects more precisely than the topology-based models.