DETERMINATION OF IMAGE ORIENTATION SUPPORTED BY IMU AND GPS
结合视觉注意力机制和图像锐度的无参图像质量评价方法
第39卷第1期2018年1月应用光学Journal of Applied Optic;Vol. 39 No. 1 Jan. 2018文章编号！002-2082(2018)01-0051-06结合视觉注意力机制和图像锐度的无参图像质量评价方法王凡，倪晋平，董涛，郭荣礼(西安工业大学光电工程学院，陕西西安710021):针对模糊图像的质量评价，提出一种新的无参图像质量评价方法，该方法结合了自底向上的视觉注意力机制和自顶向下的图像锐度评价标准。
根据人眼视觉注意力机制模型，分别计算颜色、亮度和方向显著度图像，通过竞争机制得到人眼优先关注的区域；利用无参图像锐度评价方法分别对优先关注的区域及背景区域进行评价，综合2个区域的评价结果得到最终的图像质量评价指标。
利用该方法分别对相向运动过程中所产生的模糊图像和图像质量评价L ive数据库中的高斯模糊图像进行了评价，结果表明：针对两类图像的评价结果与主观评价结果的相关系数均较高，其中，针对相向运动模糊图像的主客观评价结果的相关系数达到0. 98。
该方法能够胜任对模糊像的质 价。
:数字图像；图像质量评价；视觉机制；图像锐度中图分类号：TN911.73;TP391.4 文献标志码：A doi：10. 5768/JAO201839. 0102002No-reference image quality assessmentmethodbasedon visualattention mechanism and sharpness metric approachWang Fan,Ni Jinping,Dong Tao,Guo Rongli(School of Optoelectronics Engineering,Xi?an Technological University,Xi?an 710032 ,China)Abstract：A no-reference image quality assessment method based on bottom-up visual attentionmechanism and top-down image sharpness metric is proposed for the blur im tion.Firstly,the color,intensity and orientation feature maps are calculated based on the humanvisual attention mechanism,and then the saliency region of the image is obtained by the winner-take-all competition.Secondly,the image of saliency region and the background region are evaluated using no-reference i mage sharpness evaluation method,and the final image quality indexcan be obtained by the combination of the above two results.The radial blur images produced inthe forward motion imaging and the Gauss fuzzy images in the Live database evaluation are evaluated using the proposed method respectively.Experiment results can prove that the correlationcoefficient between the results of our method and the subjective one is larger than 0. 98 for theradial blur images.The method c an be used for evaluating the blur image subjectively.Key words：digital images；image quality assessment；visual mechanism；image sharpness收稿日期！017-05-24 #修回日期！017-08-07基金项目：国家自然科学基金（11704302"陕西省科技厅项目（2016JQ6053)陕西省教育厅重点实验室科研计划项目(14JS035)西安工业大学光电工程学院院长基金（16GDYJZ02)作者简介：王凡（987 — "女，陕西西安人，博士，讲师，主要从事光电成像与图像质量评价方面的研究。
基于PixelGrid软件的ADS80影像数据生产技术探讨
青海国土经略·技术交流ADS80航摄仪采用线阵推扫成像原理，每一次飞行可以同时获取前视、下视、后视三度重叠连续无缝的具有相同分辨率和良好光谱信息的全色、彩色和红外影像。
因而在立体观测、矢量提取和DEM 生成及编辑等过程中，都可充分利用其前视一下视、前视-后视、下视-后视立体像对，选择最优的交会角度获取高质量数据，这是其他航空数字影像无法比拟的。
相机上集成了GPS 和惯性测量装置（IMU)，可以为每条扫描线产生较准确的外方位初值，因此在后期的空三加密数据处理中，不像传统摄影测量需要很多的平高控制点，只需在加测少量平高控制点，或无地面控制点的情况下利用PPP 技术，完成地面目标的三维定位，为摄影测量自动化开辟了崭新途径。
1 数据处理1.1 处理流程ADS80影像数据处理主要包括数据准备，工程建立，数据预处理，空中三角测量解算，DEM 匹配，DEM 编辑正射影像生成等。
其数据处理流程如下（图1）：1.2 主要技术1.2.1 数据准备本次任务收集到的ADS80影像数据是L0级数据，而PixelGrid 软件空三加密及后续生产需要的是L1级影像数据，需要L0级到L1级数据的转换。
转换时摘 要：本文通过对ADS80影像数据用于1:1万基础测绘项目生产过程中各技术环节的总结，着重说明了在PixelGrid 软件下处理ADS80数据时各工序的作业方法和注意事项，为以后ADS 系列影像在航测生产中更好、更广泛地应用提供参考。
关键词：ADS80影像；区域网平差；DOM基于PixelGrid 软件的ADS80影像数据生产技术探讨◆ 马永春 李 龙（青海省第二测绘院，青海西宁 810000）65需确认是否存在L0级数据的“.sup”和“.odf”影像参数文件，并将“.sup”文件中将其关联的几个文件的路径进行修改，使其分别对应文件的确切存放路径。
建议在进行数据拷贝时，直接拷贝“sessin”文件目录，修改计算机盘符即可，不要随意移动里面的文件，避免破坏文件组织结构。
image alignment and stitching a tutorial
Richard Szeliski Last updated, December 10, 2006 Technical Report MSR-TR-2004-92
This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It closes with a discussion of open research problems in the area.
基于HSI色彩坐标相似度的彩色图像分割方法
基于HSI色彩坐标相似度的彩色图像分割方法李宁;许树成;邓中亮【摘要】该文提出一种基于HSI彩色空间的图像分割方法。
欧氏距离作为图像分割中常用的衡量像素点之间彩色关系的依据，在HSI坐标系下却不能很好地反应两个像素点之间的关系。
因此，提出相似度代替欧氏距离作为一种新的衡量两个像素点之间彩色关系的依据。
算法通过确定HSI分量中占主导地位的分量，建立彩色图像分割模型，创建一个和原图尺寸一样的颜色相似度等级图，并利用相应的颜色相似度等级图的颜色信息对像素点进行聚类。
实验结果表明，所提出的分割算法具有很强的鲁棒性和准确性，在其他条件相同的情况下，基于相似度的分割方法优于基于欧氏距离为基准的彩色图像分割。
%A new method for color image segmentation which based on HSI color space is presented in this paper. Euclidean distance as a common basis of measuring the colour relationship between two pixels can not reflect the relationship between the two pixels in the HSI coordinate system. Therefore,the traditional Euclidean distance is abandoned,and the color similarity is proposed as a new basis of measuring the relationship between the two pixels. The algorithm is used to build the color image seg?mentation model by at determining the dominant component in the HSI components and create a color similarity level picture with the size same as the original picture. The color information of the corresponding color level diagram is adopted to cluster the pixel points. The experimental results show that the segmentation algorithm has strong robustness and high accuracy,and under the sameconditions,the segmentation method based on similarity is better than the segmentation method based on Euclidean dis?tance.【期刊名称】《现代电子技术》【年(卷),期】2017(040)002【总页数】5页(P30-33,38)【关键词】图像分割;HSI彩色空间;颜色相似度;欧氏距离【作者】李宁;许树成;邓中亮【作者单位】北京邮电大学，北京 100876;北京邮电大学，北京 100876;北京邮电大学，北京 100876【正文语种】中文【中图分类】TN911.73-34基于彩色信息的图像分割算法在计算机视觉中扮演着重要的角色，并广泛应用于各个领域。
A Label Field Fusion Bayesian Model and Its Penalized Maximum Rand Estimator for Image Segmentation
1610IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010A Label Field Fusion Bayesian Model and Its Penalized Maximum Rand Estimator for Image SegmentationMax MignotteAbstract—This paper presents a novel segmentation approach based on a Markov random field (MRF) fusion model which aims at combining several segmentation results associated with simpler clustering models in order to achieve a more reliable and accurate segmentation result. The proposed fusion model is derived from the recently introduced probabilistic Rand measure for comparing one segmentation result to one or more manual segmentations of the same image. This non-parametric measure allows us to easily derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Concretely, this Gibbs energy model encodes the set of binary constraints, in terms of pairs of pixel labels, provided by each segmentation results to be fused. Combined with a prior distribution, this energy-based Gibbs model also allows for definition of an interesting penalized maximum probabilistic rand estimator with which the fusion of simple, quickly estimated, segmentation results appears as an interesting alternative to complex segmentation models existing in the literature. This fusion framework has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature. Index Terms—Bayesian model, Berkeley image database, color textured image segmentation, energy-based model, label field fusion, Markovian (MRF) model, probabilistic Rand index.I. INTRODUCTIONIMAGE segmentation is a frequent preprocessing step which consists of achieving a compact region-based description of the image scene by decomposing it into spatially coherent regions with similar attributes. This low-level vision task is often the preliminary and also crucial step for many image understanding algorithms and computer vision applications. A number of methods have been proposed and studied in the last decades to solve the difficult problem of textured image segmentation. Among them, we can cite clustering algorithmsManuscript received February 20, 2009; revised February 06, 2010. First published March 11, 2010; current version published May 14, 2010. This work was supported by a NSERC individual research grant. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Peter C. Doerschuk. The author is with the Département d’Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Faculté des Arts et des Sciences, Montréal H3C 3J7 QC, Canada (e-mail: mignotte@iro.umontreal.ca). Color versions of one or more of the figures in this paper are available online at . Digital Object Identifier 10.1109/TIP.2010.2044965[1], spatial-based segmentation methods which exploit the connectivity information between neighboring pixels and have led to Markov Random Field (MRF)-based statistical models [2], mean-shift-based techniques [3], [4], graph-based [5], [6], variational methods [7], [8], or by region-based split and merge procedures, sometimes directly expressed by a global energy function to be optimized [9]. Years of research in segmentation have demonstrated that significant improvements on the final segmentation results may be achieved either by using notably more sophisticated feature selection procedures, or more elaborate clustering techniques (sometimes involving a mixture of different or non-Gaussian distributions for the multidimensional texture features [10], [11]) or by taking into account prior distribution on the labels, region process, or the number of classes [9], [12], [13]. In all cases, these improvements lead to computationally expensive segmentation algorithms and, in the case of energy-based segmentation models, to costly optimization techniques. The segmentation approach, proposed in this paper, is conceptually different and explores another strategy initially introduced in [14]. Instead of considering an elaborate and better designed segmentation model of textured natural image, our technique explores the possible alternative of fusing (i.e., efficiently combining) several quickly estimated segmentation maps associated with simpler segmentation models for a final reliable and accurate segmentation result. These initial segmentations to be fused can be given either by different algorithms or by the same algorithm with different values of the internal parameters such as several -means clustering results with different values of , or by several -means results using different distance metrics, and applied on an input image possibly expressed in different color spaces or by other means. The fusion model, presented in this paper, is derived from the recently introduced probabilistic rand index (PRI) [15], [16] which measures the agreement of one segmentation result to multiple (manually generated) ground-truth segmentations. This measure efficiently takes into account the inherent variation existing across hand-labeled possible segmentations. We will show that this non-parametric measure allows us to derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Finally, this fusion model emerges as a classical optimization problem in which the Gibbs energy function related to this model has to be minimized. In other words, or analytically expressed in the regularization framework, each quickly estimated segmentation (to be fused) provides a set of constraints in terms of pairs of pixel labels (i.e., binary cliques) that should be equal or not. Finally, our fusion result is found1057-7149/$26.00 © 2010 IEEEMIGNOTTE: LABEL FIELD FUSION BAYESIAN MODEL AND ITS PENALIZED MAXIMUM RAND ESTIMATOR FOR IMAGE SEGMENTATION1611by searching for a segmentation map that minimizes an energy function encoding this precomputed set of binary constraints (thus optimizing the so-called PRI criterion). In our application, this final optimization task is performed by a robust multiresolution coarse-to-fine minimization strategy. This fusion of simple, quickly estimated segmentation results appears as an interesting alternative to complex, computationally demanding segmentation models existing in the literature. This new strategy of segmentation is validated in the Berkeley natural image database (also containing, for quantitative evaluations, ground truth segmentations obtained from human subjects). Conceptually, our fusion strategy is in the framework of the so-called decision fusion approaches recently proposed in clustering or imagery [17]–[21]. With these methods, a series of energy functions are first minimized before their outputs (i.e., their decisions) are merged. Following this strategy, Fred et al. [17] have explored the idea of evidence accumulation for combining the results of multiple clusterings. Reed et al. have proposed a Gibbs energy-based fusion model that differs from ours in the likelihood and prior energy design, as final merging procedure (for the fusion of large scale classified sonar image [21]). More precisely, Reed et al. employed a voting scheme-based likelihood regularized by an isotropic Markov random field priorly used to inpaint regions where the likelihood decision is not available. More generally, the concept of combining classifiers for the improvement of the performance of individual classifiers is known, in machine learning field, as a committee machine or mixture of experts [22], [23]. In this context, Dietterich [23] have provided an accessible and informal reasoning, from statistical, computational and representational viewpoints, of why ensembles can improve results. In this recent field of research, two major categories of committee machines are generally found in the literature. Our fusion decision approach is in the category of the committee machine model that utilizes an ensemble of classifiers with a static structure type. In this class of committee machines, the responses of several classifiers are combined by means of a mechanism that does not involve the input data (contrary to the dynamic structure type-based mixture of experts). In order to create an efficient ensemble of classifiers, three major categories of methods have been suggested whose goal is to promote diversity in order to increase efficiency of the final classification result. This can be done either by using different subsets of the input data, either by using a great diversity of the behavior between classifiers on the input data or finally by using the diversity of the behavior of the input data. Conceptually, our ensemble of classifiers is in this third category, since we intend to express the input data in different color spaces, thus encouraging diversity and different properties such as data decorrelation, decoupling effects, perceptually uniform metrics, compaction and invariance to various features, etc. In this framework, the combination itself can be performed according to several strategies or criteria (e.g., weighted majority vote, probability rules: sum, product, mean, median, classifier as combiner, etc.) but, none (to our knowledge) uses the PRI fusion (PRIF) criterion. Our segmentation strategy, based on the fusion of quickly estimated segmentation maps, is similar to the one proposed in [14] but the criterion which is now used in this new fusion model is different. In [14], the fusion strategy can be viewed as a two-stephierarchical segmentation procedure in which the first step remains identical and a set of initial input texton segmentation maps (in each color space) is estimated. Second, a final clustering, taking into account this mixture of textons (expressed in the set of different color space) is then used as a discriminant feature descriptor for a final -mean clustering whose output is the final fused segmentation map. Contrary to the fusion model presented in this paper, this second step (fusion of texton segmentation maps) is thus achieved in the intra-class inertia sense which is also the so-called squared-error criterion of the -mean algorithm. Let us add that a conceptually different label field fusion model has been also recently introduced in [24] with the goal of blending a spatial segmentation (region map) and a quickly estimated and to-be-refined application field (e.g., motion estimation/segmentation field, occlusion map, etc.). The goal of the fusion procedure explained in [24] is to locally fuse label fields involving labels of two different natures at different level of abstraction (i.e., pixel-wise and region-wise). More precisely, its goal is to iteratively modify the application field to make its regions fit the color regions of the spatial segmentation with the assumption that the color segmentation is more detailed than the regions of the application field. In this way, misclassified pixels in the application field (false positives and false negatives) are filtered out and blobby shapes are sharpened, resulting in a more accurate final application label field. The remainder of this paper is organized as follows. Section II describes the proposed Bayesian fusion model. Section III describes the optimization strategy used to minimize the Gibbs energy field related to this model and Section IV describes the segmentation model whose outputs will be fused by our model. Finally, Section V presents a set of experimental results and comparisons with existing segmentation techniques.II. PROPOSED FUSION MODEL A. Rand Index The Rand index [25] is a clustering quality metric that measures the agreement of the clustering result with a given ground truth. This non-parametric statistical measure was recently used in image segmentation [16] as a quantitative and perceptually interesting measure to compare automatic segmentation of an image to a ground truth segmentation (e.g., a manually hand-segmented image given by an expert) and/or to objectively evaluate the efficiency of several unsupervised segmentation methods. be the number of pixels assigned to the same region Let (i.e., matched pairs) in both the segmentation to be evaluated and the ground truth segmentation , and be the number of pairs of pixels assigned to different regions (i.e., misand . The Rand index is defined as matched pairs) in to the total number of pixel pairs, i.e., the ratio of for an image of size pixels. More formally [16], and designate the set of region labels respecif tively associated to the segmentation maps and at pixel location and where is an indicator function, the Rand index1612IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010is given by the following relation:given by the empirical proportion (3) where is the delta Kronecker function. In this way, the PRI measure is simply the mean of the Rand index computed between each [16]. As a consequence, the PRI pair measure will favor (i.e., give a high score to) a resulting acceptable segmentation map which is consistent with most of the segmentation results given by human experts. More precisely, the resulting segmentation could result in a compromise or a consensus, in terms of level of details and contour accuracy exhibited by each ground-truth segmentations. Fig. 8 gives a fusion map example, using a set of manually generated segmentations exhibiting a high variation, in terms of level of details. Let us add that this probabilistic metric is not degenerate; all the bad segmentations will give a low score without exception [16]. C. Generative Gibbs Distribution Model of Correct Segmentations (i.e., the pairwise empirical As indicated in [15], the set ) defines probabilities for each pixel pair computed over an appealing generative model of correct segmentation for the image, easily expressed as a Gibbs distribution. In this way, the Gibbs distribution, generative model of correct segmentation, which can also be considered as a likelihood of , in the PRI sense, may be expressed as(1) which simply computes the proportion (value ranging from 0 to 1) of pairs of pixels with compatible region label relationships between the two segmentations to be compared. A value of 1 indicates that the two segmentations are identical and a value of 0 indicates that the two segmentations do not agree on any pair of points (e.g., when all the pixels are gathered in a single region in one segmentation whereas the other segmentation assigns each pixel to an individual region). When the number of and are much smaller than the number of data labels in points , a computationally inexpensive estimator of the Rand index can be found in [16]. B. Probabilistic Rand Index (PRI) The PRI was recently introduced by Unnikrishnan [16] to take into accounttheinherentvariabilityofpossible interpretationsbetween human observers of an image, i.e., the multiple acceptable ground truth segmentations associated with each natural image. This variability between observers, recently highlighted by the Berkeley segmentation dataset [26] is due to the fact that each human chooses to segment an image at different levels of detail. This variability is also due image segmentation being an ill-posed problem, which exhibits multiple solutions for the different possible values of the number of classes not known a priori. Hence, in the absence of a unique ground-truth segmentation, the clustering quality measure has to quantify the agreement of an automatic segmentation (i.e., given by an algorithm) with the variation in a set of available manual segmentations representing, in fact, a very small sample of the set of all possible perceptually consistent interpretations of an image [15]. The authors [16] address this concern by soft nonuniform weighting of pixel pairs as a means of accounting for this variability in the ground truth set. More formally, let us consider a set of manually segmented (ground truth) images corresponding to an be the segmentation to be compared image of size . Let with the manually labeled set and designates the set of reat pixel gion labels associated with the segmentation maps location , the probabilistic RI is defined bywhere is the set of second order cliques or binary cliques of a Markov random field (MRF) model defined on a complete graph (each node or pixel is connected to all other pixels of is the temperature factor of the image) and this Boltzmann–Gibbs distribution which is twice less than the normalization factor of the Rand Index in (1) or (2) since there than pairs of pixels for which are twice more binary cliques . is the constant partition function. After simplification, this yields(2) where a good choice for the estimator of (the probability of the pixel and having the same label across ) is simply (4)MIGNOTTE: LABEL FIELD FUSION BAYESIAN MODEL AND ITS PENALIZED MAXIMUM RAND ESTIMATOR FOR IMAGE SEGMENTATION1613where is a constant partition function (with a factor which depends only on the data), namelywhere is the set of all possible (configurations for the) segof size pixels. Let us add mentations into regions that, since the number of classes (and thus the number of regions) of this final segmentation is not a priori known, there are possibly, between one and as much as regions that the number of pixels in this image (assigning each pixel to an individual can region is a possible configuration). In this setting, be viewed as the potential of spatially variant binary cliques (or pairwise interaction potentials) of an equivalent nonstationary MRF generative model of correct segmentations in the case is assumed to be a set of representative ground where truth segmentations. Besides, , the segmentation result (to be ), can be considered as a realization of this compared to generative model with PRand, a statistical measure proportional to its negative likelihood energy. In other words, an estimate of , in the maximum likelihood sense of this generative model, will give a resulting segmented map (i.e., a fusion result) with a to be fused. high fidelity to the set of segmentations D. Label Field Fusion Model for Image Segmentation Let us consider that we have at our disposal, a set of segmentations associated to an image of size to be fused (i.e., to efficiently combine) in order to obtain a final reliable and accurate segmentation result. The generative Gibbs distribution model of correct segmentations expressed in (4) gives us an interesting fusion model of segmentation maps, in the maximum PRI sense, or equivalently in the maximum likelihood (ML) sense for the underlying Gibbs model expressed in (4). In this framework, the set of is computed with the empirical proportion estimator [see (3)] on the data . Once has been estimated, the resulting ML fusion segmentation map is thus defined by maximizing the likelihood distributiontions for different possible values of the number of classes which is not a priori known. To render this problem well-posed with a unique solution, some constraints on the segmentation process are necessary, favoring over segmentation or, on the contrary, merging regions. From the probabilistic viewpoint, these regularization constraints can be expressed by a prior distribution of treated as a realization of the unknown segmentation a random field, for example, within a MRF framework [2], [27] or analytically, encoded via a local or global [13], [28] prior energy term added to the likelihood term. In this framework, we consider an energy function that sets a particular global constraint on the fusion process. This term restricts the number of regions (and indirectly, also penalizes small regions) in the resulting segmentation map. So we consider the energy function (6) where designates the number of regions (set of connected pixels belonging to the same class) in the segmented is the Heaviside (or unit step) function, and an image , internal parameter of our fusion model which physically represents the number of classes above which this prior constraint, limiting the number of regions, is taken into account. From the probabilistic viewpoint, this regularization constraint corresponds to a simple shifted (from ) exponential distribution decreasing with the number of regions displayed by the final segmentation. In this framework, a regularized solution corresponds to the maximum a posteriori (MAP) solution of our fusion model, i.e., that maximizes the posterior distribution the solution , and thus(7) with is the regularization parameter controlling the contribuexpressing fidelity to the set of segtion of the two terms; encoding our prior knowledge or mentations to be fused and beliefs concerning the types of acceptable final segmentations as estimates (segmentation with a number of limited regions). In this way, the resulting criteria used in this resulting fusion model can be viewed as a penalized maximum rand estimator. III. COARSE-TO-FINE OPTIMIZATION STRATEGY A. Multiresolution Minimization Strategy Our fusion procedure of several label fields emerges as an optimization problem of a complex non-convex cost function with several local extrema over the label parameter space. In order to find a particular configuration of , that efficiently minimizes this complex energy function, we can use a global optimization procedure such as a simulated annealing algorithm [27] whose advantages are twofold. First, it has the capability of avoiding local minima, and second, it does not require a good solution. initial guess in order to estimate the(5) where is the likelihood energy term of our generative fusion . model which has to be minimized in order to find Concretely, encodes the set of constraints, in terms of pairs of pixel labels (identical or not), provided by each of the segmentations to be fused. The minimization of finds the resulting segmentation which also optimizes the PRI criterion. E. Bayesian Fusion Model for Image Segmentation As previously described in Section II-B, the image segmentation problem is an ill-posed problem exhibiting multiple solu-1614IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010Fig. 1. Duplication and “coarse-to-fine” minimization strategy.An alternative approach to this stochastic and computationally expensive procedure is the iterative conditional modes (ICM) introduced by Besag [2]. This method is deterministic and simple, but has the disadvantage of requiring a proper initialization of the segmentation map close to the optimal solution. Otherwise it will converge towards a bad local minima . In order associated with our complex energy function to solve this problem, we could take, as initialization (first such as iteration), the segmentation map (8) i.e., in choosing for the first iteration of the ICM procedure amongst the segmentation to be fused, the one closest to the optimal solution of the Gibbs energy function of our fusion model [see (5)]. A more robust optimization method consists of a multiresolution approach combined with the classical ICM optimization procedure. In this strategy, rather than considering the minimization problem on the full and original configuration space, the original inverse problem is decomposed in a sequence of approximated optimization problems of reduced complexity. This drastically reduces computational effort and provides an accelerated convergence toward improved estimate. Experimentally, estimation results are nearly comparable to those obtained by stochastic optimization procedures as noticed, for example, in [10] and [29]. To this end, a multiresolution pyramid of segmentation maps is preliminarily derived, in order to for each at different resolution levels, and a set estimate a set of of similar spatial models is considered for each resolution level of the pyramidal data structure. At the upper level of the pyramidal structure (lower resolution level), the ICM optimization procedure is initialized with the segmentation map given by the procedure defined in (8). It may also be initialized by a random solution and, starting from this initial segmentation, it iterates until convergence. After convergence, the result obtained at this resolution level is interpolated (see Fig. 1) and then used as initialization for the next finer level and so on, until the full resolution level. B. Optimization of the Full Energy Function Experiments have shown that the full energy function of our model, (with the region based-global regularization constraint) is complex for some images. Consequently it is preferable toFig. 2. From top to bottom and left to right; A natural image from the Berkeley database (no. 134052) and the formation of its region process (algorithm PRIF ) at the (l = 3) upper level of the pyramidal structure at iteration [0–6], 8 (the last iteration) of the ICM optimization algorithm. Duplication and result of the ICM relaxation scheme at the finest level of the pyramid at iteration 0, 1, 18 (last iteration) and segmentation result (region level) after the merging of regions and the taking into account of the prior. Bottom: evolution of the Gibbs energy for the different steps of the multiresolution scheme.perform the minimization in two steps. In a first step, the minimization is performed without considering the global constraint (considering only ), with the previously mentioned multiresolution minimization strategy and the ICM optimization procedure until its convergence at full resolution level. At this finest resolution level, the minimization is then refined in a second step by identifying each region of the resulting segmentation map. This creates a region adjacency graph (a RAG is an undirected graph where the nodes represent connected regions of the image domain) and performs a region merging procedure by simply applying the ICM relaxation scheme on each region (i.e., by merging the couple of adjacent regions leading to a reduction of the cost function of the full model [see (7)] until convergence). In the second step, minimization can also be performed . according to the full modelMIGNOTTE: LABEL FIELD FUSION BAYESIAN MODEL AND ITS PENALIZED MAXIMUM RAND ESTIMATOR FOR IMAGE SEGMENTATION1615with its four nearest neighbors and a fixed number of connections (85 in our application), regularly spaced between all other pixels located within a square search window of fixed size 30 pixels centered around . Fig. 3 shows comparison of segmentation results with a fully connected graph computed on a search window two times larger. We decided to initialize the lower (or third upper) level of the pyramid with a sequence of 20 different random segmentations with classes. The full resolution level is then initialized with the duplication (see Fig. 1) of the best segmentation result (i.e., the one associated to the lowest Gibbs energy ) obtained after convergence of the ICM at this lower resolution level (see Fig. 2). We provide details of our optimization strategy in Algorithm 1. Algo I. Multiresolution minimization procedure (see also Fig. 2). Two-Step Multiresolution Minimization Set of segmentations to be fusedPairwise probabilities for each pixel pair computed over at resolution level 1. Initialization Step • Build multiresolution Pyramids from • Compute the pairwise probabilities from at resolution level 3 • Compute the pairwise probabilities from at full resolution PIXEL LEVEL Initialization: Random initialization of the upper level of the pyramidal structure with classes • ICM optimization on • Duplication (cf. Fig 1) to the full resolution • ICM optimization on REGION LEVEL for each region at the finest level do • ICM optimization onFig. 4. Segmentation (image no. 385028 from Berkeley database). From top to bottom and left to right; segmentation map respectively obtained by 1] our multiresolution optimization procedure: = 3402965 (algo), 2] SA : = 3206127, 3] rithm PRIF : = 3312794, 4] SA : = 3395572, 5] SA : = 3402162. SAFig. 3. Comparison of two segmentation results of our multiresolution fusion procedure (algorithm PRIF ) using respectively: left] a subsampled and fixed number of connections (85) regularly spaced and located within a square search window of size = 30 pixels. right] a fully connected graph computed on a search window two times larger (and requiring a computational load increased by 100).NUU 0 U 00 U 0 U 0D. Comparison With a Monoresolution Stochastic Relaxation In order to test the efficiency of our two-step multiresolution relaxation (MR) strategy, we have compared it to a standard monoresolution stochastic relaxation algorithm, i.e., a so-called simulated annealing (SA) algorithm based on the Gibbs sampler [27]. In order to restrict the number of iterations to be finite, we have implemented a geometric temperature cooling schedule , where is the [30] of the form starting temperature, is the final temperature, and is the maximal number of iterations. In this stochastic procedure, is crucial. The temperathe choice of the initial temperature ture must be sufficiently high in the first stages of simulatedC. Algorithm In order to decrease the computational load of our multiresolution fusion procedure, we only use two levels of resolution in our pyramidal structure (see Fig. 2): the full resolution and an image eight times smaller (i.e., at the third upper level of classical data pyramidal structure). We do not consider a complete graph: we consider that each node (or pixel) is connected。
New staff orientation procedure(知名注塑公司新员工入职培训程序)
HI-P INTERNATIONAL LIMITED
Title 文件名
Rev 版本号
HI-P INTERNATIONAL LIMITED 赫比国际有限公司
New Employee Orientation Procedure
新员工入职培训管理程序
2.0
Released Date 发布日期
11 Jan, 2006
4.1.2.2 To manage and update the training record 培训记录管理
4.2 onsibilities of Immediate Supervisor 新员工的直接主管职责
4.2.1 To brief the new employee on the JD, KPI and to highlight the essential knowledge and skills required for the job. 与新员工沟通 JD，KPI，并定义与新员工工作相关的基本知识和技能
作环境，掌握与工作相关的基本知识和技能，从而更好地投入到新的工作中，特制定本程
序。
2 SCOPE 适用范围
2.1 This policy is applicable to all Hi-P Group of companies. 所有赫比公司。
3 DEFINITIONS 定义
3.1 The Welcome Package shall be hereafter known as “WP”. 《新员工 Welcome Package》以下将简称为“WP”
5.1.2.2The contents of the new employee orientation programme, shall include but not limited to: Ethics, Company Introduction, Hi-P Culture, Tracker (for Level 3 Employees), company policy and procedure, EHSS, Basic Knowledge on Quality, etc. Training on Hi-P Culture shall be conducted on the first day and Tracker shall be conducted within a month of the new employee’s reporting date. 新员工入职培训内容应包括但不局限于：道德规范，公司简介、企业文化、 Tracker（Level 3 员工）、公司规章制度、EHSS、基本质量意识等。其中企 业文化在员工入职第一天举行， Tracker 在入职一个月内举行。
The ZπM algorithm for interferometric image reconstruction in SARSAS
Jos´ e M. B. Dias∗ and Jos´ e M. N. Leit˜ ao
Abstract The paper presents an effective algorithm for absolute phase (not simply modulo-2π ) estimation from incomplete, noisy, and modulo-2π observations in interferometric aperture radar and sonar (InSAR/InSAS). The adopted framework is also representative of other applications such as optical interferometry, magnetic resonance imaging, and diffraction tomography. The Bayesian viewpoint is adopted; the observation density is 2π -periodic and accounts for the interferometric pair decorrelation and system noise; the a priori probability of the absolute phase is modelled by a Compound Gauss Markov random field (CGMRF) tailored to piecewise smooth absolute phase images. We propose an iterative scheme for the computation of the maximum a posteriori probability (MAP) phase estimate. Each iteration embodies a discrete optimization step (Z-step), implemented by network programming techniques, and an iterative conditional modes (ICM) step (π -step). Accordingly, the algorithm is termed Zπ M, where the letter M stands for maximization. A set of experimental results, comparing the proposed algorithm with alternative approaches, illustrates the effectiveness of the proposed method.
纹理物体缺陷的视觉检测算法研究--优秀毕业论文
摘 要
在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足 轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比，自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代 化电子 系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本 算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测；纹理；物体变形；可调控金字塔；重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
DETERMINATION OF IMAGE ORIENTATION SUPPORTED BY IMU AND GPSKarsten JacobsenUniversity of HannoverInstitute for Photogrammetry and Engineering SurveysNienburger Str. 1D-30167 HannoverJacobsen@ipi.uni-hannover.deKEYWORDS: Inertial Measurement Unit (IMU), GPS, bundle block adjustmentABSTRACTFor operational use, the photo orientations of a blockwith more than 1000 images have been determinedwith an LCR88 inertial measurement unit (IMU) andGPS. The relation of the IMU to the camera andcorrections for the GPS-data of the projection centershave been determined and improved by means of asmall reference block with 5 – 8 photos. Forchecking purposes the photo coordinates of 252photos have been measured and the orientations aredetermined by a combined bundle block adjustmentwith the GPS-data of the projection centers based on9 control points. The achieved accuracy of the photoorientations based on IMU and GPS are sufficient forthe creation of orthophotos but problems are stillexisting with y-parallaxes in the models. The y-parallaxes can be reduced by a combined bundleblock adjustment without control points or a moreexpensive inertial measurement system.1.INTRODUCTIONA combined bundle block adjustment without control points is possible for a block, if a larger percentage of the projection centers are determined by relative kinematic GPS-positioning (Jacobsen 1997). In the case of a real block structure, attitude data are not required, they can be determined by the combined block adjustment with GPS-data (figure 1), that means, if at least 2 parallel flight strips are available. The flight strips may be located one beside the other or even with a vertical displacement of the flight lines (figure 2). The classical location of one flight axis beside the other has the advantage of the same photo scale, this makes the determination of tie points more easy.Figure 2: block configuration of linear objects –IMU-data not requiredOnly for a single flight strip or a combination of single flight strips (figure 3) attitude data are required in addition to GPS-coordinates of the projection centers if no control points are available, because of the problem with the lateral tilt. But even for a real block structure, the combined use of GPS and IMU in the aircraft has some advantages. In a combined computation of the positions with IMU-and GPS-data, GPS cycle slips can be determined and so the problem of shifts and drifts of the GPS-data, different from flight strip to strip can be solved. with GPS – crossing flight strips every 20 – 30 baseIn such a case the crossing flight strips are not directly required, but they do have the advantage of a better control of the block geometry and they are avoiding also problems of a not accurate lateral tilt of long flight strips.Figure 3: typical block configuration of linear objects2.PROJECTBecause of a very restricted time frame for a project handled by the company BSF (Berliner Spezialflug Luftbild und Vermessungen GmbH, Diepensee) the bundle adjustment of a block with 1041 photos should be replaced by the direct determination of the photo orientations by means of relative kinematic GPS-positioning in combination with IMU. The relation between the LCR88 inertial measurement system, mounted on top of the camera and the camera itself should be determined by means of a small reference block flown before and after the project area. The large block size required a photo flight in 2 days, so the small reference block has been imaged 4 times with in total 37 photos.The flying height of approximately 1090m above terrain corresponds with the focal length of 305mm to a photo scale 1 : 3500. The large photo scale was not required for the accuracy of the ground points but for the identification of the mapping objects.For checking purposes a block adjustment of 252 photos (check area in figure 4) has been made, based on 9 control points. 9 control points are not sufficient for such a block of 12 flight strip without crossing lines, so a combined adjustment with coordinates of the projection centers determined by kinematic GPS-positioning was required. Of course this is not a total independent determination of the photo orientations -the same GPS-data have been used like in the determination of the orientations without control points, but the systematic GPS-data could be determined independently based on the control points in the check area.3.PREPARATION OF THE INERTIAL DATA The combined determination of the GPS-positions together with the attitudes, based on a LCR88, has been made by IGI, Hilchenbach by means of Kalman filtering. The conditions for the GPS-positioning was not optimal, partially only 5 satellites have been available and the PDOP was going up to 3.As a first result only pitch, roll and yaw have been available. With the program IMUPRE of the Hannover program system BLUH this has been converted into the usual photo orientations respecting the convergence of meridians, thedifferent rotation definition and the dimension of the attitude data (grads instead of degrees).Figure 5: one of the reference blocks with control pointsBy a comparison of the photo orientations of the reference blocks (figure 5) with the orientations determined by means of GPS and IMU, the relation of the axis between the photogrammetric camera and the IMU has been determined as well as systematic differences of the GPS-positions. By linear time depending interpolation, based on the relation before and after the flight over the main area, the photo orientations of the images in the main area have been improved. The improvement of the attitude data was done in the pitch, roll and yaw-system,corresponding to the relation of the axes.roll [grads]pitch [grads]yaw [grads]systematicdifferences day 1-.445-.469.534 “ day 2-.454-.462.571mean square differenceswithout systematic differences day 1.039.012.044 “ day 2.029.016.049after linear fitting day 1.025.009.007 “ day 2.021.009.010after fitting by t³ day 1.011.009.007 “ day 2.021.009.010Table 1: differences of the attitude data IMU –controlled bundle block adjustment (reference blocks)Table 1 shows the differences and mean square differences between the IMU-data and the orientations determined by bundle block adjustment of the small reference blocks only based on control points. The first and last images of the reference blocks have not been taken into account because theyare not so well supported by control points (see also figure 5), so approximately only 6 photos of each of the 4 control blocks have been used for comparison.A linear time depending improvement of the attitude data is required because the roll has changed both days approximately 0.070 grads between the reference area flown before and after the main area,the yaw has changed the first day 0.080 and the second day 0.100 grads. There was no significant change of the pitch.The photo orientations determined by bundle block adjustment based on control points is not free of errors. The adjustment is giving following mean square standard deviations as mean value of all:Sphi=0.0017 grads, Somega=0.0017 grads,Skappa=0.00042 grads, SX0=0.033m, SY0=0.034m,SZ0=0.015m. But this is only the internal accuracy,it does not show the problems of the strong correlation between phi and X0 and omega and Y0.phi omega kappa X0 Y0 Z0phi 1.00.03-.061.00-.03.30omega .03 1.00.08.03-1.00.11kappa -.06.08 1.00-.06-.09-.01X0 1.00.03-.06 1.00-.03.29Y0-.03-1.00-.09-.03 1.00-.11Z0.30.11-.01.29-.111.00Table 2: correlation matrix of the photo orientations Table 2 shows the strong correlation listed with 1.00,that means it is larger than 0.995. By this reason a complete separation between the attitude data and the projection center coordinates is not possible. It may happen that a correction of the attitude data will be made, but the differences are belonging to the GPS-data and reverse. A separation between both is only possible based on opposite flight directions ordifferent flying altitudes (see also Jacobsen 1999).Figure 6:discrepancies in the projection centers:GPS – bundle block adjustment as a function of the timeThe graphic representation of the discrepancies in the projection centers between the GPS-data and the photo orientations of the bundle block adjustments in figure 6 are showing problems of the GPS-data. The drift of the X-coordinate of the second part of the second day in the range of 1.5m is corresponding to a difference in phi of 0.078 grads. This is very exactly corresponding to a drift of phi with a size of 0.079 grads. This demonstrates the problem of the reference data, especially if a normal angle camera (f=305mm) is used. Such corresponding values cannot be seen at the other reference blocks.4.ANALYSISBased on the bundle block adjustment of the check area, including photos of 12 flight strips, each with 21 images, the photo orientations based on IMU and GPS improved by means of the reference blocks have been analyzed. 9 control points are not sufficient for such a block without crossing flight strips, so a combined adjustment with GPS-data of the projection centers was necessary.Figure 7: configuration of the check areawith the control pointsThe mean square differences at the control points have been 3cm for X and Y and 6cm for the height, together with a sigma0 of 9 µm. Based on the control points, the improved GPS-data have been shifted 11cm in X, 15cm in Y and 59cm in the height, indicating, that the GPS-data improved by the reference blocks still do have remarkable systematic errors.Figure 8: discrepancies of the attitude data corrected IMU – bundle block adjustment f(time)pitch[grads]roll[grads]yaw[grads] absolute0.0280.0200.059 without shifterrors0.0100.0100.013 linear fitting0.0100.0100.007 Table 3: discrepancies of the attitude datacorrected IMU – bundle block adjustmentFigure 8 and table 3 are showing the discrepancies of the attitude data between the IMU-data improved by the reference blocks and the orientations determined by bundle block adjustment and the results after elimination of constant shifts and also drifts individually for every flight strip. Especially larger differences in the yaw can be seen. The yaw has only a very small correlation to other orientation elements and it can be determined more precise than the other attitude values, that means the determined discrepancies can only be explained by the IMU-data. On the other hand the influence of errors in yaw to the image and also the object space is smaller than the influence of the other attitude data.X0 [m]Y0 [m]Z0 [m] absolute0.210.220.64 without shifterrors0.150.130.05 linear fitting0.160.140.05 Table 4: discrepancies of the projection centerscorrected GPS-data – bundle block adjustmentThe discrepancies at the projection center coordinates between the GPS-data corrected by the reference blocks and the results of the bundle blockadjustment of the check area are corresponding to the discrepancies determined by the combined block adjustment itself. Especially the discrepancies in Z0 are obvious.More important than the discrepancy of the individual orientation components are the discrepancies at the ground coordinates determined with the improved photo orientations. With the photo coordinates and the photo orientations determined by GPS and IMU a combined intersection has been computed (iteration 0 of program system BLUH) and the resulting ground coordinates have been compared with the results of the controlled bundle blockX, Y RMSX=0.42m RMSY=0.18mThe discrepancies at the ground coordinates shown in figure 9 (only 10% of the 1886 points are plotted) are within the project specifications. Changing systematic errors can be seen, but the relative accuracy is still better.X [m]Y [m]Z [m] RMS of absolutedifferences0.420.180.85systematic differences-0.180.01-0.59 RMS withoutsystematic differences0.380.180.61relative accuracy(<500m)0.190.100.36Table 5: discrepancies at the ground coordinates As it can be seen in figure 10 and also in table 5, the discrepancies of the Z-components of the ground points are dominated by systematic errors. But also if the overall systematic error of –0.59m is respected, the root mean square differences are only reduced to 0.61m. For a comparison with the X and Y-component, the height to base relation of 3.2 has to be respected, that means, the value of 0.61m corresponds to 0.19m and this is still in the range of the X- and Y-component.Figure 10: discrepancies at the ground coordinates Z - plot of 10% of the 1886 pointsThe absolute differences of the ground coordinates are important for the creation of orthophotos. For the setup of models, the y-parallax is more important. If the y-parallax is reaching the size of the floating mark, usually in the range of 30µm, the operator is getting problems with the stereoscopic impression of the floating mark in relation to the model. For the y-parallax only the relative accuracy of the orientations of both used photos are important. The relative accuracy of the attitude data of neighbored photos has been determined by program BLAN of program system BLUH together with the covariance function. The correlation of neighbored phi-values are c=0.81 and for omega it is c=0.57, that means, the values are strongly dependent. The relative accuracy has following values: Sphi rel = 0.011grads, Somega rel= 0.010grads, Skappa rel = 0.005grads. For theinfluence to the model, these values have to bemultiplied by 2, but the influence of the reference data has to be taken out.Just the value omega has an influence in the center of the model of tan 0.010 grads • 305mm = 53 µm, multiplied by 2 it reaching 75µm. Corresponding to this, the combined intersection of the photo orientations based on IMU- and GPS-data with the photo coordinates of the check area has had a resulting standard deviation of the photo coordinates of 105µm. Such an amount can not be accepted for a model orientation.5.CONCLUSIONThe determination of the image orientations by means of an LCR88-IMU and GPS has resulted in an accuracy of the ground coordinates of 0.42m for X, 0.18m for Y and 0.85m for Z. This was sufficient for the project. Systematic errors are existing, especially for the height.A problem is existing with the used reference blocks, each with 9 images, required for the determination of the relation between the IMU and the photogrammetric camera, but also for a shift-correction (datum) of the projection center coordinates determined by relative kinematic GPS-positioning. The separation of the influence of the IMU and GPS is a problem especially for normal angle cameras (f=305mm). Such reference blocks have to be flown twice in opposite direction or with a different flying altitude.The achieved image orientations are not sufficient for the setup of a model. If this is required, a more accurate IMU-system, that means a more expensive one, has to be used. But even this does not guarantee today the required quality. The best and save solution is the use of the IMU- and GPS-data in a combined bundle block adjustment. This still requires the determination of photo coordinates for the block adjustment – with automatic aero triangulation the effort is limited. A combined bundle adjustment includes also a better reliability. The main advantage of photo orientations based on IMU- and GPS-data is the possibility to reduce the number of required control points, especially for linear objects. Without control or check points usually such results are not respected. Only for special projects in remote areas or in the coastal zone today such photo orientations are accepted without additional checking possibilities.6.ACKNOWLEDGMENTThanks are going to BSF (Berliner Spezialflug Luftbild und Vermessungen GmbH, Diepensee) and IGI, Hilchenbach for the availability of the data and the fruitful cooperation.7.REFERENCESElias, B., (1998): Bündelblockausgleichung mit INS-Daten, diploma thesis University ofHannover 1998Jacobsen, K. (1997): Operational Block Adjustment without Control Points, ASPRS AnnualConvention 1997, SeattleJacobsen, K. (1999): Combined Bundle Block Adjustment with Attitude Data, ASPRSAnnual Convention 1999, PortlandLee, J.O. (1996): Untersuchung von Verfahren zur kombinierten Aerotriangulation mittelsintegriertem GPS/INS, doctor thesisUniversity of Hannover 1996。