Temporal segmentation and assignment of successive actions in a long-term video

Temporal segmentation of successive actions in a long-term video sequence has been a long-standing problem in computer vision. In this paper, we exploit a novel learning-based framework. Given a video sequence, only a few characteristic frames are selected by the proposed selection algorithm, and then the likelihood to trained models is calculated in a pair-wise way, and finally segmentation is obtained as the optimal model sequence to realize the maximum likelihood. The average accuracy on IXMAS dataset reached to 80.5% at frame level, using only 16.5% of all frames in computation time of 1.57 s per video which has 1160 frames on the average.     

基于最优阈值法的锅炉火焰图像处理的研究

工业锅炉炉膛内部是一个高温、高辐射、高灰度的环境，炉内燃料的燃烧过程伴随着剧烈而连续的发光发热的物理、化学反应。在锅炉火焰图像识别中，原始图像对该复杂环境变化非常敏感，并严重地影响图像的识别率。针对这一问题，本文介绍了一种提取锅炉火焰特征的最优阈值法。该方法依据统计学基本原理，对锅炉火焰自动识别的实际问题建立一个统计回归模型，并根据回归模型参数直接求取最优的分割阈值。     

A survey of shaped-based registration and segmentation techniques for cardiac images

Heart disease is the leading cause of death in the modern world. Cardiac imaging is routinely applied for assessment and diagnosis of cardiac diseases. Computerized image analysis methods are now widely applied to cardiac segmentation and registration in order to extract the anatomy and contractile function of the heart. The vast number of recent papers on this topic point to the need for an up to date survey in order to summarize and classify the published literature. This paper presents a survey of shape modeling applications to cardiac image analysis from MRI, CT, echocardiography, PET, and SPECT and aims to (1) introduce new methodologies in this field, (2) classify major contributions in image-based cardiac modeling, (3) provide a tutorial to beginners to initiate their own studies, and (4) introduce the major challenges of registration and segmentation and provide practical examples. The techniques surveyed include statistical models, deformable models/level sets, biophysical models, and non-rigid registration using basis functions. About 130 journal articles are categorized based on methodology, output, imaging system, modality, and validations. The advantages and disadvantages of the registration and validation techniques are discussed as appropriate in each section.     

Statistical analysis of manual segmentations of structures in medical images

The problem of extracting anatomical structures from medical images is both very important and difficult. In this paper we are motivated by a new paradigm in medical image segmentation, termed Citizen Science, which involves a volunteer effort from multiple, possibly non-expert, human participants. These contributors observe 2D images and generate their estimates of anatomical boundaries in the form of planar closed curves. The challenge, of course, is to combine these different estimates in a coherent fashion and to develop an overall estimate of the underlying structure. Treating these curves as random samples, we use statistical shape theory to generate joint inferences and analyze this data generated by the citizen scientists. The specific goals in this analysis are: (1) to find a robust estimate of the representative curve that provides an overall segmentation, (2) to quantify the level of agreement between segmentations, both globally (full contours) and locally (parts of contours), and (3) to automatically detect outliers and help reduce their influence in the estimation. We demonstrate these ideas using a number of artificial examples and real applications in medical imaging, and summarize their potential use in future scenarios.     

A novel mesh subdivision algorithm for dense reconstruction from multi-image based on preliminary model

To solve the disadvantages of the recent three-dimensional (3D) reconstruction methods, this paper presents a dense reconstruction algorithm which incorporates image features into the mesh subdivision. A preliminary model is deformed from statistical deformation model (SDM) to catch the roughest features, and later densified under a multi-resolution frame to achieve a dense model until all image features are used. In every round of this framework, the image features are detected, and then cut into planar points. At the same time, the transitional model is re-projected into views, and all images are segmented to triangles. So the planar points are constrained by these triangles, and the corresponding relations among these triangles are certain. Then some new spatial points are generated from these planar points and subdivide the transitional model. Later, the transitional model is re-arranged to get a right topology, and becomes denser. This method contributes three novelties: (1) there is an inherited relationship between the later points and the early points; (2) if the hierarchy of multi-resolution frame and the number of new spatial points at every round are set appropriately, the transitional model evolves with a correct topology; (3) the density of the final model is proportional to the information account of views. The experiment validates these virtues.     

Ant Colony Optimization-based method for optic cup segmentation in retinal images

An accurate detection of the cup region in retinal images is necessary to obtain relevant measurements for glaucoma detection. In this work, we present an Ant Colony Optimization-based method for optic cup segmentation in retinal fundus images. The artificial agents will construct their solutions influenced by a heuristic that combines the intensity gradient of the optic disc area and the curvature of the vessels. On their own, the exploration capabilities of the agents are limited; however, by sharing the experience of the entire colony, they are capable of obtaining accurate cup segmentations, even in images with a weak or non-obvious pallor. This method has been tested with the RIM-ONE dataset, yielding an average overlapping error of 24.3% of the cup segmentation and an area under the curve (AUC) of 0.7957 using the cup to disc ratio for glaucoma assessment.     

A Character Flow Framework for Multi-Oriented Scene Text Detection

Scene text detection plays a significant role in various applications,such as object recognition,document management,and visual navigation.The instance segmentation based method has been mostly used in existing research due to its advantages in dealing with multi-oriented texts.However,a large number of non-text pixels exist in the labels during the model training,leading to text mis-segmentation.In this paper,we propose a novel multi-oriented scene text detection framework,which includes two main modules:character instance segmentation (one instance corresponds to one character),and character flow construction (one character flow corresponds to one word).We use feature pyramid network(FPN) to predict character and non-character instances with arbitrary directions.A joint network of FPN and bidirectional long short-term memory (BLSTM) is developed to explore the context information among isolated characters,which are finally grouped into character flows.Extensive experiments are conducted on ICDAR2013,ICDAR2015,MSRA-TD500 and MLT datasets to demonstrate the effectiveness of our approach.The F-measures are 92.62％,88.02％,83.69％ and 77.81％,respectively.     

Ultrasonic Tomographic Imaging of an Encased,Highly Attenuating Solid Medium

Abstract

Ultrasonic tomographic imaging has been performed on a highly attenuating, filled, hydroxyl-terminated polybutadiene, cast into a steel case. The detection of defects and the characterization of solid rocket propellant were thus possible. Extremely low signal levels due to the attenuation and scattering properties of the polymer were present. In addition, a nonideal tomographic reconstruction geometry was present, because of a cylindrical air-filled cavity in the center of the polymer. Complications also arose from high-amplitude guided waves within the steel case, which tended to mask directly transmitted signals through the propellant. These problems were solved using specially designed transducers and pulse-compression signal processing techniques. Modifications to the filtered back-projection reconstruction procedure were made to account for the discontinuity caused by the hole. Images are presented showing the detection of an artificial defect to demonstrate the technique.     

Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine

The operation of water supply channels is threatened by the occasionally occurred slope damages. Timely detection of their occurrence is critical for the rapid enforcement of mitigation measures. However, current practices based on routine inspection and structural heath monitoring are inefficient, laborious and tend to be biased. As an attempt to address the limitations, this paper proposes a bottom-up image detection approach for slope damages, which includes four steps, i.e. superpixel segmentation, feature handcrafting, superpixel classification based on support vector machine (SVM), and slope damage recognition. The approach employs a bottom-up strategy to infer the upper-level slope condition from the classification results of individual superpixels in the bottom level. Experiments were conducted to demonstrate the effectiveness of the approach. The handcrafted feature “LBP + HSV” was demonstrated to be effective in characterizing the image features of slope damages. An SVM model with “LBP + HSV” as input can reliably identify the slope condition in superpixels. Based on the SVM model, the bottom-up strategy achieved high recognition performance, of which the overall accuracy can be up to 91.7%. The proposed approach has potential to facilitate the early and comprehensive awareness of slope damages along the entire route of water channel by the integration with unmanned aerial vehicles.     

CO_2跨临界循环中绝热毛细管数值模拟计算与分析

前期通过搭建试验台,分析了二氧化碳跨临界循环中毛细管的节流特性,并运用无量纲分析法计算出了毛细管质量流量无量纲试验关联式,经可靠性分析发现该关联式优于diogo的关联式。若超出考虑的范围时,该关联式模型可能产生较大的误差。由此,利用微元分割法,设计VB程序对毛细管长度和流量进行数值计算,将计算结果与前期试验测试结果和文献值做比较。在流量计算方面:95%的计算值与前期试验值的偏差在10%以内,与文献试验值的偏差全部在10%以内。在长度计算方面,90%的计算值与前期试验值的偏差在15%以内,75%的长度计算值与文献试验值的偏差在15%以内;在偏差分布方面:流量计算值与前期试验值和文献试验值,均有95%以上的流量计算值与试验值的偏差在10%以内。最后利用该计算程序,在标准工况下对毛细管尺寸进行数值计算,绘制成毛细管选型图。