放射治疗计划系统中基于边缘模板的体廓自动提取

放射治疗中,治疗计划系统对CT、MRI等医学影像进行处理,获得体表、肿瘤等感兴趣组织的轮廓,是完成治疗规划的重要基础。论文针对CT/MRI图像的特点,提出并实现了一种基于边缘模板的体廓自动提取方法。该方法对图像进行平滑处理和锐化后,由Guass-Laplace算法进行边缘检测和轮廓提取,然后手工编辑和修改第一张图片的体廓数据,以此作为边缘模板,自动提取后续图片的体廓。该方法实现方便,效果好,满足了治疗计划系统对体廓自动提取的要求。     

一种基于边缘生长的灰度和彩色图象分割方法

边缘检测可以快速准确地提供区域分割的边缘点,是图象处理的一个重要领域。但由于边缘点不连续和难以把存在大量碎边缘点的高细节区提取出来这两个原因,而不能直接实现完整意义上的图象分割。为此提出用边缘生长的方法来解决不连续的边缘点链接问题和通过找出高细节区周围的区域,以便间接地将高细节区围成一个区域。该算法是边缘检测的后续处理,适合于多种应用目的,同时还可以嵌入到其它利用边缘信息的分割算法中。     

多水平外区抑制的轮廓检测

提取自然图像中的物体轮廓是机器视觉研究的重要问题,主要困难在于自然图像中的纹理性边缘严重干扰了物体轮廓的提取。研究表明视皮层方位选择性神经元的非经典感受野机制使得人类视觉系统在处理自然图像时不仅能够抑制纹理性边缘,而且能够增强物体的轮廓。基于此人们提出多种仿生轮廓检测算法,但算法中被称为抑制水平的参量在取值较高时会漏检部分轮廓,而在其取值较低时又会引入过多的纹理性边缘。针对这一问题,提出多水平外区抑制轮廓检测算法,通过整合各级单水平外区抑制的检测信息,有效抑制了纹理性边缘和降低了漏检轮廓的可能性。实验结果表明,相对于传统算法,新算法在轮廓检测性能上提高了10%左右,并具有更好的稳健性。     

基于多尺度彩色形态矢量算子的边缘检测

数学形态学在图象处理中已经得到广泛地应用，但传统的形态学常应用于二值图象处理，后来发展应用到灰度图象处理，对于其用于彩色图象处理的研究还不是很多，通过对传统的数学形态学的几何描述，以及对目前形态学在边缘检测中的应用分析，提出了一种新的多尺度的彩色形态矢量边缘检测算子，该方法是利用不同尺度形态边缘检测算子来检测不同尺度下的边缘强度，再对不同尺度下的边缘强度图进行合并，从而得到新的边缘强度图象，利用该算法对实际图象和合成图象进行了实验，将实验结果与传统的边缘检测算法相比较，由于新的多尺度彩色形态矢量算子能检测出更多的细节边缘，因此将更有利于图象的进一步分析处理，同时将实验图象人为地增加噪声后，再利用该算法进行实验，其结果表明，该算法对噪声具有很好的鲁棒性。     

An optimal graph theoretic approach to data clustering: theory andits application to image segmentation

A novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated. The data to be clustered are represented by an undirected adjacency graph 𝒢 with arc capacities assigned to reflect the similarity between the linked vertices. Clustering is achieved by removing arcs of 𝒢 to form mutually exclusive subgraphs such that the largest inter-subgraph maximum flow is minimized. For graphs of moderate size (~ 2000 vertices), the optimal solution is obtained through partitioning a flow and cut equivalent tree of 𝒢, which can be efficiently constructed using the Gomory-Hu algorithm (1961). However for larger graphs this approach is impractical. New theorems for subgraph condensation are derived and are then used to develop a fast algorithm which hierarchically constructs and partitions a partially equivalent tree of much reduced size. This algorithm results in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices. The new clustering algorithm is applied to the image segmentation problem. The segmentation is achieved by effectively searching for closed contours of edge elements (equivalent to minimum cuts in 𝒢), which consist mostly of strong edges, while rejecting contours containing isolated strong edges. This method is able to accurately locate region boundaries and at the same time guarantees the formation of closed edge contours     

The background primal sketch: An approach for tracking moving objects

In this paper we present an algorithm that integrates spatial and temporal information for the tracking of moving nonrigid objects. In addition, we obtain outlines of the moving objects.Three basic ingredients are employed in the proposed algorithm, namely, the background primal sketch, the threshold, and outlier maps. The background primal sketch is an edge map of the background without moving objects. If the background primal sketch is known, then edges of moving objects can be determined by comparing the edge map of the input image with the background primal sketch. A moving edge point is modeled as an outlier, that is, a pixel with an edge value differing from the background edge value in the background primal sketch by an amount larger than the threshold in the threshold map at the same physical location. The map that contains all the outliers is called the outlier map. In this paper we present techniques based on robust statistics for determining the background primal sketch, the threshold, and outlier maps.In an ideal situation the outlier map would contain the complete outlines of the moving objects. In practice, the outliers do not form closed contours. The final step of the algorithm employs an edge-guided morphological approach to generate closed outlines of the moving objects. The proposed approach has been tested on sequences of moving human blood cells (neutrophil) as well as of human body motion with encouraging results.     

基于空频域联合闭值分割的轮廓检测方法

传统的边缘检测算子仅在空域上对梯度图像进行阈值分割来计算二值边缘图像,当应用在自然场景图像中时,检测结果中往往含有大量的干扰边缘。为了消除干扰边缘,提高传统边缘算子的轮廓检测性能,提出了基于空频域联合阈值分割的轮廓检测方法:首先对梯度图像进行频域阈值分割消除干扰边缘,然后进行空域阈值分割得到最终的二值边缘图。结合Canny算子,利用自然场景图像对该方法进行了性能评估,结果表明,该方法大大减少了干扰边缘,有效提高了Canny算子在复杂自然场景图像中的轮廓检测性能。     

基于生物视觉特征的目标轮廓提取算法

自然场景中的目标轮廓提取是计算机视觉中的一个重要研究问题。其难点在于场景中大量的纹理边缘严重地干扰了轮廓提取的完整性。近年来,一些研究工作将生物视觉特征引入图像边缘轮廓提取,取得了一定的效果。其中通过引入视觉外区抑制特征可以在提取物体轮廓边缘的同时抑制一定量的纹理边缘,从而得到轮廓边缘集合。然而在整合轮廓边缘时,传统模型仅仅采用求交并集的简单合并方法,使得强响应的细小纹理残留。基于此,提出了一种改进的基于生物视觉特征的自然场景目标轮廓提取算法。首先采用多水平抑制方法得到候选轮廓边缘集合。接着将一种基于生物视觉特征的边缘组合方法用于将候选边缘整合成为一个完整的目标轮廓。与传统的外区抑制算法相比,基于视觉特征的轮廓提取算法提高了自然场景中目标轮廓提取的准确性和完整性。     

基于边缘自适应小波变换的低比特率图像压缩算法

常规的小波压缩算法在低比特率情况下将不可避免地在图像强边缘附近产生振铃效应。为此提出了一种基于边缘自适应小波变换的低比特率图像压缩算法。在编码端，先检测出图像的强边缘并将其作为附加信息进行编码；然后，利用强边缘信息将图像沿行列方向分割成一些独立的数据段分别进行小波变换；最后，利用EBCOT算法对得到的小波系数进行编码。特别地，从图像的成像机理出发，提出了一种克服分段数据边界效应的新方法。实验结果表明，这种边缘自适应小波变换即使在比特率极低的情况下也可以保持图像轮廓的清晰，强边缘附近的振铃效应也得到有效的抑制。由于附加信息的存在，压缩图像的PSNR值相比于常规方法通常会有所降低，但图像的主观视觉质量却有明显的提高。     

基于自适应符号函数的主动轮廓模型

几何主动轮廓模型的缺点是对初始轮廓位置特别敏感,基于距离规则水平集（DRLSE）模型的初始轮廓曲线必须设置在目标边界的内部或者外部.基于边缘的自适应水平集（ALSE）模型,提出了一种提高初始轮廓鲁棒性的方法.但两种模型均容易出现陷入虚假边界、从弱边缘处泄露以及抗噪声能力差等问题.设计了一个结合自适应符号函数和自适应边缘指示函数的模型,使得主动轮廓演化能根据自适应符号函数的方向从初始轮廓开始自动进行膨胀及收缩,很好地改善了水平集对初始轮廓敏感的缺点,提高了鲁棒性,同时解决了水平集对收敛速度慢以及易从弱边缘处泄露的问题.此外,为了使得模型演化更加稳定,提出了一个新的距离规则项.实验结果表明：自适应符号函数的主动轮廓模型不仅可以提高分割质量,缩短图像分割时间,同时提高了对初始轮廓的鲁棒性.     

A knowledge-based neural network for fusing edge maps of multi-sensor images

With the goal of fusion prescribed as building an edge map that contains as many edges as possible from the given multi-spectral/sensor images, a new fusion scheme, called the knowledge-based neural network fusion (KBNNF), is proposed to fuse edge maps of these images in order to generate a combined edge map that has more complete and reliable edge information than what one can obtain from any single image.The KBNNF is used to fuse edge maps of images having mutually complementary edge information in the following sense: (i) the edges in the images are compatible, i.e., can be interpreted together; and (ii) the edges in the different images reveal different parts of the scene. More complete edge contours of the same object are obtained by linking the edge sections obtained from different images together. The resulting edge map can be used for subsequent study (like object recognition).The proposed scheme bases its confidence and reliability on the analysis of variance (ANOVA)-based edge detector that can address two important issues of edge based image fusion well: (i) the difference in edge position among the images because of the different characteristics of the images and the error in the image registration process; and (ii) the variance existing among the edge test values calculated from different images. The KBNNF has been applied to fuse: (i) radar (SAR)–optical (SPOT), (ii) optical–optical, (iii) infrared–infrared, and (iv) optical–infrared (satellite) image combinations. Comparisons are made with the relevant existing techniques in the literature. The paper concludes with some examples to illustrate the efficacy of the proposed scheme.     

结合相位一致与全变差模型的高分辨率遥感图像边缘检测

目的边缘检测是有效利用遥感数据开展地物目标自动识别的重要步骤。高分辨率遥感图像地物类型复杂,细节信息过于丰富,使得基于相位一致的边缘检测结果中存在过多的噪声与伪边缘。为此提出了一种结合相位一致与全变差模型的高分辨率遥感图像边缘检测方法。方法根据相位一致原理,应用Log Gabor构造的2维相位一致模型,引入全变差去噪模型对基于相位一致的边缘强度图进行改进。结果借助有界变差空间对图像光滑性的约束,实现了高分辨率遥感图像噪声去除与伪边缘抑制,利用改进后的相位一致边缘强度图可有效检测高分辨率遥感图像的边缘。结论实验结果表明,与相位一致模型、Canny算法相比,该方法能消除了高分辨率遥感图像中同类地物内部细节特征形成的噪声,抑制相位一致边缘检测结果中的伪边缘,突出地物的真实边缘,并能正确地提取地物目标的整体轮廓信息,有助于后续地物目标的自动识别。     

A new edge detector integrating scale-spectrum information

This paper presents a new scale space-based method to extract edges in gray level images. The method is based on a novel representation of gray-level shape called the scale-spectrum space. The scale space representation is used to describe an image at different scales. In order to obtain the original image edges, an edge detector is applied to each simplified image on the corresponding scale. At best, some form of compromise among the edges at different scale levels may be sought. To overcome this problem, we present a stability criterion to combine edges obtained at different scales. Usual problems in edge detection such as displacement, redundancy and error are analyzed and solved using a realistic estimation of displacement of points across scale space. The proposed approach suppress the finer details without weakening or dislocating the larger scale edges (the usual problems of edge detection using an isotropic diffusion procedure) in an improved manner compared to anisotropic diffusion procedures because a tuning function is not required. The proposed methodology is biologically inspired by the behavior of visual cortex neurones as well as retinal cells.     

基于多尺度多分辨率局部边缘模式的边缘检测

由于传统基于梯度的方形边缘检测算子包含边缘方向过少（一般为2个或4个方向）,因此无法从多分辨率角度检测边缘,进而会丢失其他方向的边缘信息。针对上述问题,提出一种具有多尺度、多分辨率特性的边缘检测算子,称为可变局部边缘模式（Varied Local Edge Pattern,VLEP）算子,并用来提取图像边缘信息。算法主要思路包括,将图像经过高斯滤波器平滑,使用一组或多组VLEP算子与滤波后的图像进行卷积,得到边缘强度,从而获得边缘梯度值,最后设置适当的梯度阈值,对梯度图像进行二值化处理,完成图像的边缘检测。此外,当多组VLEP算子被同时使用时,考虑结合加权融合思想,以便获得更加丰富的边缘信息。实验结果表明,提出的边缘检测算法比其他经典的方法具有更好的边缘检测效果。     

Robust Visual Tracking Based on Convolutional Features with Illumination and Occlusion Handing

视觉追踪是在计算机视觉的一个重要区域。怎么处理照明和吸藏问题是一个挑战性的问题。这份报纸论述一篇小说和有效追踪算法处理如此的问题。一方面,一起始的外观总是有的目标清除轮廓,它对照明变化光不变、柔韧。在另一方面,特征在追踪起一个重要作用,在哪个之中 convolutional 特征显示出有利性能。因此,我们采用卷的轮廓特征代表目标外观。一般来说,一阶的衍生物边坡度操作员在由卷检测轮廓是有效的他们与图象。特别, Prewitt 操作员对水平、垂直的边更敏感,当 Sobel 操作员对斜边更敏感时。内在地, Prewitt 和 Sobel 与对方一起是补足的。技术上说,这份报纸设计二组 Prewitt 和 Sobel 边察觉者提取一套完全的 convolutional 特征,它包括水平、垂直、斜的边特征。在第一个框架,轮廓特征从目标被提取构造起始的外观模型。在有这些轮廓特征的试验性的图象的分析以后,明亮的部分经常提供更有用的信息描述目标特征,这能被发现。因此,我们建议一个方法比较候选人样品和我们仅仅使用明亮的象素的训练模型的类似,它使我们的追踪者有能力处理部分吸藏问题。在得到新目标以后,变化以便改编外观,我们建议相应联机策略逐渐地更新我们的模型。convolutional 特征由井综合的 Prewitt 和 Sobel 边察觉者提取了的实验表演能是足够有效的学习柔韧的外观模型。九个挑战性的序列上的众多的试验性的结果证明我们的建议途径与最先进的追踪者比较很有效、柔韧。     

Distributed edge event‐triggered consensus protocol of multi‐agent systems with communication buffer

This paper proposes a distributed edge event‐triggered (DEET) scheme of multi‐agent systems via a communication buffer to reduce unnecessary update of controllers induced by fast information transmission. This edge scheme avoids a synchronous phenomenon in node event‐triggered mechanism, in which the triggering of one agent activates information transmission of all edges linked with this agent. Hence, the node event‐triggered scheme leads to unnecessary update of control protocols while the DEET provides a new approach without constrains on synchronous phenomenon of edge information exchange. That is, the communication on each edge is independent with other edges. In addition, we investigate another case where edge information transmission is subject to quantization and a quantized edge event‐triggered control protocol is proposed. Note that such a quantized protocol guarantees asymptotical consensus instead of bounded consensus in most of the existing literature. Meanwhile, both DEET and quantized edge event‐triggered schemes have nontrivial properties of excluding Zeno behavior. Furthermore, an algorithm is provided to avoid continuous event detection; hence, the communication traffic of the whole network is reduced significantly. Copyright © 2016 John Wiley & Sons, Ltd.     

Sparsity Enforcing Edge Detection Method for Blurred and Noisy Fourier data

We present a new method for estimating the edges in a piecewise smooth function from blurred and noisy Fourier data. The proposed method is constructed by combining the so called concentration factor edge detection method, which uses a finite number of Fourier coefficients to approximate the jump function of a piecewise smooth function, with compressed sensing ideas. Due to the global nature of the concentration factor method, Gibbs oscillations feature prominently near the jump discontinuities. This can cause the misidentification of edges when simple thresholding techniques are used. In fact, the true jump function is sparse, i.e. zero almost everywhere with non-zero values only at the edge locations. Hence we adopt an idea from compressed sensing and propose a method that uses a regularized deconvolution to remove the artifacts. Our new method is fast, in the sense that it only needs the solution of a single l ₁ minimization. Numerical examples demonstrate the accuracy and robustness of the method in the presence of noise and blur.     

含边缘信息的C-V模型

边缘信息对图像分割是十分重要的。把图像的边缘信息融入C-V模型（active contours without edges）,提出一个新的几何模型,它同时利用同质区域信息和边缘信息使演化曲线在目标边缘处停止。实验显示：新模型能够克服C-V模型的一些缺点;在减少分割时间的同时,对目标灰度不均匀或背景灰度不均匀、含弱边缘或强噪声的图像,分割效果不仅优于C-V模型,也优于C-V模型的两个最新改进模型（LBF和GACV）。     

BIORV-NSA: Bidirectional inhibition optimization r-variable negative selection algorithm and its application

The original negative selection algorithm (NSA) has the disadvantages that many “black holes” cannot be detected and excessive invalid detectors are generated. To overcome its defects, this paper improves the detection performance of NSA and presents a kind of bidirectional inhibition optimization r-variable negative selection algorithm (BIORV-NSA). The proposed algorithm includes self set edge inhibition strategy and detector self-inhibition strategy. Self set edge inhibition strategy defines a generalized radius for self individual area, making self individual radius dynamically be variable. To a certain extent, the critical antigens close to self individual area are recognized and more non-self space is covered. Detector self-inhibition strategy, aiming at mutual cross-coverage among mature detectors, eliminates those detectors that are recognized by other mature detectors and avoids the production of excessive invalid detectors. Experiments on artificially generating data set and two standard real-world data sets from UCI are made to verify the performance of BIORV-NSA, by comparison with NSA and R-NSA, the experimental results demonstrate that the proposed BIORV-NSA algorithm can cover more non-self space, greatly improve the detection rates and obtain better detection performance by using fewer mature detectors.     

Lip contour extraction for language learning in VEC3D

Lip image analysis is a critical problem in many vision-based applications such as lip reading, speaker authentication, video conferencing and language learning. We design three dynamic probability maps based on the color, shape, and edge features, respectively. These probability maps are then utilized to extract both inner and outer lip contours using a grid-based gradient-ascent approach. The experiments show that the proposed algorithm can extract inner/outer lip contours efficiently and reliably. The proposed lip contour extraction algorithm is applied successfully in a language-learning application called VEC3D.