Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.

Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers

Vision based tracking can provide the spatial location of construction entities such as equipment, workers, and materials in large scale, congested construction sites. It tracks entities in video streams by inferring their locations based on the entities’ visual features and motion histories. To initiate the process, it is necessary to determine the pixel areas corresponding to the construction entities to be tracked in the following consecutive video frames. In order to fully automate the process, an automated way of initialization is needed. This paper presents the method for construction worker detection which can automatically recognize and localize construction workers in video frames. The method first finds the foreground areas of moving objects using a background subtraction method. Within these foreground areas, construction workers are recognized based on the histogram of oriented gradients (HOG) and histogram of the HSV colors. HOG’s have proved to work effectively for detection of people, and the histogram of HSV colors helps differentiate between pedestrians and construction workers wearing safety vests. Preliminary experiments show that the proposed method has the potential to automate the initialization process of vision based tracking.

Construction Worker Detection in Video Frames for Initializing Vision Trackers

Monitoring the location of resources on large scale, congested, outdoor sites can be performed more efficiently with vision tracking, as this approach does not require any pre-tagging of resources. However, the greatest impediment to the use of vision tracking in this case is the lack of detection methods that are needed to automatically mark the resources of interest and initiate the tracking. This paper presents such a novel method for construction worker detection that localizes construction workers in video frames. The proposed method exploits motion, shape, and color cues to narrow down the detection regions to moving objects, people, and finally construction workers, respectively. The three cues are characterized by using background subtraction, the histogram of oriented gradients (HOG), and the HSV color histogram. The method has been tested on videos taken in various environments. The results demonstrate its suitability for automatic initialization of vision trackers.

Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables

We demonstrate how a prior assumption of smoothness can be used to enhance the reconstruction of free energy profiles from multiple umbrella sampling simulations using the Bayesian Gaussian process regression approach. The method we derive allows the concurrent use of histograms and free energy gradients and can easily be extended to include further data. In Part I we review the necessary theory and test the method for one collective variable. We demonstrate improved performance with respect to the weighted histogram analysis method and obtain meaningful error bars without any significant additional computation. In Part II we consider the case of multiple collective variables and compare to a reconstruction using least squares fitting of radial basis functions. We find substantial improvements in the regimes of spatially sparse data or short sampling trajectories. A software implementation is made available on www.libatoms.org.

A linkage map of common carp (Cyprinus carpio) based on AFLP and microsatellite markers

P>Common carp (Cyprinus carpio) is an important fish for aquaculture, but genomics of this species is still in its infancy. In this study, a linkage map of common carp based on Amplified Fragment Length Polymorphism (AFLP) and microsatellite (SSR) markers has been generated using gynogenetic haploids. Of 926 markers genotyped, 151 (149 AFLPs, two SSRs) were distorted and eliminated from the linkage analyses. A total of 699 AFLP and 20 microsatellite (SSR) markers were assigned to the map, which comprised 64 linkage groups and covered 5506.9 cM Kosambi, with an average interval distance of 7.66 cM Kosambi. The normality tests on interval map distances showed a non-normal marker distribution. Visual inspection of the map distance distribution histogram showed a cluster of interval map distances on the left side of the chart, which suggested the occurrence of AFLP marker clusters. On the other hand, the lack of an obvious cluster on the right side showed that there were a few big gaps which need more markers to bridge. The correlation analysis showed a highly significant relatedness between the length of linkage group and the number of markers, indicating that the AFLP markers in this map were randomly distributed among different linkage groups. This study is helpful for research into the common carp genome and for further studies of genetics and marker-assisted breeding in this species.

Characteristic rainfall for warning of debris flows

A characteristic rainfall is introduced to overcome the difficulties encountered in determining a critical rainfall value for triggering debris flow. The characteristic value is defined as the rainfall at which debris-flow occurrence probability shows a rapid increase, and can be used as a warning rainfall threshold for debris flows. Investigation of recorded debris flows and 24-hour rainfall data at Jiangjia basin, Yunnan Province, in southwestern China, demonstrates the existence of such a characteristic rainfall. It was found that the characteristic rainfall corresponds to the daily rainfall of 90% cumulative probability by analyzing the basin's daily rainfall histogram. The result provides a simple and useful method for estimating a debris-flow warning rainfall threshold from the daily rainfall distribution. It was applied to estimate the debris-flow warning rainfall threshold for the Subaohe basin, a watershed in the 2008 Wenchuan earthquake zone with many physical characteristics similar to those of the Jiangjia basin.

基于直方图插值的均值移动小尺寸目标跟踪算法

小尺寸目标跟踪是视觉跟踪中的难题。该文首先指出了均值移动小尺寸目标跟踪算法中的两个主要问题:算法跟踪中断和丢失跟踪目标。然后,论文给出了相应的解决方法。对传统Parzen窗密度估计法加以改进,并用于对候选目标区域的直方图进行插值处理,较好地解决了算法跟踪中断问题。论文采用Kullback-Leibler距离作为目标模型和候选目标之间的新型相似性度量函数,并推导了其相应的权值和新位置计算公式,提高了算法的跟踪精度。多段视频序列的跟踪实验表明,该文提出的算法可以有效地跟踪小尺寸目标,能够成功跟踪只有6×12个像素的小目标,跟踪精度也有一定提高。

基于直方图处理和新型相似性度量函数的小尺寸目标跟踪算法

小尺寸目标跟踪是视觉跟踪中的难题。本文使用均值移动算法跟踪小尺寸目标。论文首先分析了基于均值移动的小尺寸目标跟踪算法的两个主要问题:跟踪算法中断和跟踪目标丢失。然后,论文在这两个方面对小尺寸目标跟踪算法进行改进。给出了一种新的直方图单元编号方法,使包含目标颜色分量的直方图单元分布得更为集中紧凑。当候选目标与目标模型不匹配时,给出一种平滑算法来处理候选目标的直方图。论文提出一种新的相似性度量函数,推导了相应的权值计算公式,在此基础上建立了基于均值移动的目标跟踪算法。多段真实场景视频序列的跟踪实验表明,本文提出的算法可以有效地跟踪小尺寸目标,跟踪精度也有一定提高。

用于移动机器人自定位的视觉路标设计

为了实现室内移动机器人的自定位,提出了一种简易美观的新型视觉人工路标以及基于对数极坐标系投影直方图的路标识别方法,并用基于共面四点的位姿估计算法计算机器人位姿。实验结果说明,路标检测具有很高的鲁棒性;路标识别方法抗噪声和形变的能力强;位姿精度足够满足室内移动机器人自定位的需要。

基于改进角度直方图和最小二乘法的移动机器人地图构建

本文主要研究完全未知结构化环境下的移动机器人二维几何地图构建及其不确定性描述问题。在考虑测量噪声干扰的基础上，基于改进的角度直方图算法进行环境线段特征提取和参数初始估计，然后利用加权最小二乘法对线段特征参数及其方差进行精确估计，并同时给出了多位姿地图合并的处理方法.文章最后给出了在SmartROB2机器人平台上进行的实验结果，证明了算法的有效性和实用性。

一种基于多特征融合的汽车变速箱螺栓在线视觉检测系统

本文根据汽车变速箱装配线螺栓检测的要求,结合检测现场的实际图像特点,采用融合灰度投影与颜色直方图两种特征的方法,利用相似性测度的计算方法,设计了一种变速箱螺栓在线装配质量检测系统,该系统用于实现螺栓的的缺失及漏拧检测,试验结果表明,方法具有良好的实用性,取得了比较理想的效果。

龙滩工程左岸边坡监测资料分析与稳定性评价研究

On the basis of the geological analysis and rock mass toppling deformation and failure mechanism analysis of Longtan engineering left bank slope, the synthetic space-time analysis and influence factors analysis on the surface monitoring data and deep rock mass monitoring data of B-zone of left bank slope are carried on. At the same time, based on the monitoring data analysis in conjunction with the predecessor's mechanics analysis results, the deformation state of B-zone of the left bank slope is discussed and its stability is synthetically evaluated. The detailed research contents and results are as following: According to the monitoring drill histogram analysis of Longtan engineering left bank slope, numerical simulation analysis and model experimentation analysis of bedded counter-inclined steep slope, a new type of toppling deformation and failure mode is proposed, that is "up-slope warping". Then the deformation and failure mode of bedded counter-inclined steep slope is summarized as "down-slope toppling" type, "up-slope warping" type and "complex fold" type. On the basis of synthetic space-time analysis to surface monitoring data and deep rock mass deformation monitoring data of B-zone of Longtan left bank slope;, we can get the conclusion that there exists potential instability rock mass over 520m altitude, especially over 560m altitude of slope B, and the rock mass of around strong-weathering line or creep rock mass breaking band controls the deformation of the whole slope. 1. According to the synthetic space-time analysis and influence factors analysis to the surface monitoring data of B-zone of Longtan left bank slope, a dynamical index, accumulative total acceleration index, which is used to analyze the influence factors of slope surface deformation, is raised. The principle and method of accumulative acceleration index are explained, and the index can be used for the influence factors analysis of the similar slope. 2. Summarize the results of geologic analysis, monitoring analysis and mechanics analysis, the following conclusion can be gotten: the stability of B-zone of the slope is basically good. However, on the condition of drainage and slope toe loading engineering, there is still some creep deformation in the rock mass over 520m altitude, especially over 560m altitude. So, better measures of the monitoring and timely maintenance of the drainage system are suggested in the paper.

Skin Color-Based Video Segmentation under Time-Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24% is observed in 17 out of 21 test sequences. In all but one case the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.

An alignment based similarity measure for hand detection in cluttered sign language video

Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a web cam in a user’s home. Moreover, the signers’ clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The Histogram of Oriented Gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a Support Vector Machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the Vocabulary Guided Pyramid Match Kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.