Affine and projective active contour models

We investigate the evolution of active contours in terms of progressive modification of an initial contour following the chosen Lie group of object-to-image transformations. Because of non-fronto-parallel viewing of an object or due to relative motion between the camera and the object, the resultant image may undergo affine or projective object-to-image transformations. In a recent paper we have shown that in the case of object tracking, frame-to-frame deformations of an initial curve obtained through Euler-Lagrange descent equations of a curve functional can be used to enact a desired Lie group of plane transformations [A.-R. Mansouri, D.P. Mukherjee, S.T. Acton, Constraining active contour evolution via Lie groups of transformation, IEEE Trans. Image Process. 13 (2004) 853-863]. In this work, we propose an energy functional that encodes the Lie group transformation parameters, which in turn guide shape distortion due to oblique viewing. Additional constraints, such as transformation smoothness, are imposed on the active contour by modifying the energy functional. The functional is minimized using numerical schemes similar to the conjugate gradient technique, and the convergence properties are discussed. The success of the technique for affine and projective scenes is demonstrated with both synthetic and real image examples and compared with the related approaches.     

On active contour models and balloons

The use of energy-minimizing curves, known as “snakes,” to extract features of interest in images has been introduced by Kass, Witkin & Terzopoulos (Int. J. Comput. Vision 1, 1987, 321–331). We present a model of deformation which solves some of the problems encountered with the original method. The external forces that push the curve to the edges are modified to give more stable results. The original snake, when it is not close enough to contours, is not attracted by them and straightens to a line. Our model makes the curve behave like a balloon which is inflated by an additional force. The initial curve need no longer be close to the solution to converge. The curve passes over weak edges and is stopped only if the edge is strong. We give examples of extracting a ventricle in medical images. We have also made a first step toward 3D object reconstruction, by tracking the extracted contour on a series of successive cross sections.     

Region-based strategies for active contour models

The variational method has been introduced by Kass et al. (1987) in the field of object contour modeling, as an alternative to the more traditional edge detection-edge thinning-edge sorting sequence. since the method is based on a pre-processing of the image to yield an edge map, it shares the limitations of the edge detectors it uses. in this paper, we propose a modified variational scheme for contour modeling, which uses no edge detection step, but local computations instead—only around contour neighborhoods—as well as an anticipating strategy that enhances the modeling activity of deformable contour curves. many of the concepts used were originally introduced to study the local structure of discontinuity, in a theoretical and formal statement by leclerc & zucker (1987), but never in a practical situation such as this one. the first part of the paper introduces a region-based energy criterion for active contours, and gives an examination of its implications, as compared to the gradient edge map energy of snakes. then, a simplified optimization scheme is presented, accounting for internal and external energy in separate steps. this leads to a complete treatment, which is described in the last sections of the paper (4 and 5). the optimization technique used here is mostly heuristic, and is thus presented without a formal proof, but is believed to fill a gap between snakes and other useful image representations, such as split-and-merge regions or mixed line-labels image fields.     

Parameterization-free active contour models with topology control

We present a novel approach for representing and evolving deformable active contours by restricting the movement of the contour vertices to the grid lines of a uniform lattice. This restriction implicitly controls the (re)parameterization of the contour and hence makes it possible to employ parameterization-independent evolution rules. Moreover, the underlying uniform grid makes self-collision detection very efficient. Our contour model is also able to perform topology changes, but – more importantly – it can detect and handle self-collisions at subpixel precision. In applications where topology changes are not appropriate, we generate contours that touch themselves without any gaps or self-intersections.     

结合小波变换的边缘活动轮廓模型

在基于边缘的活动轮廓模型中,边缘停止函数的选择是十分重要的。传统的边缘停止函数依赖于图像高斯平滑后的梯度模,这容易导致模型分割速度慢,无法准确分割被噪声严重污染或背景复杂的图像。结合小波变换,提出一个新的边缘停止函数。实验表明,基于该函数的边缘模型可以有效地解决上述问题,而且可以应用于多目标的选择性分割。     

Receding-horizon unbiased FIR filters for continuous-timestate-space models without a priori initial state information

A receding horizon unbiased finite-impulse response filter (RHUFF) is proposed for continuous-time state space models. Linearity, unbiasedness, finite-impulse response (FIR) structure, and independence of the initial state information will be required in advance, in addition to a performance index of minimum variance. The proposed RHUFF is obtained by directly minimizing the performance index with the unbiasedness constraint. The proposed RHUFF is represented first in a standard FIR form and then in an iterative form. It is shown that the RHUFF is equivalent to the existing receding horizon (RH) Kalman FIR filter. The former is more systematic and logical, while the latter is heuristic due to the handling of infinite covariance of the initial state information     

Using a priori information in regression analysis

The paper considers the methods to evaluate regression parameters under indefinite a priori information of two types: fuzzy and stochastic. Fuzzy a priori information is assumed to be formulated on the basis of fuzzy notions of the model designer. Stochastic a priori information is systems of equations, which are linear in regression parameters and whose right-hand sides are random variables. Regression parameters may both be constant and vary in time. A classification of the evaluation methods using indefinite a priori information is proposed and used to generalize well-known methods. An evaluation method is developed, which combines the fuzzy and stochastic a priori information about regression parameters.     

Auxiliary signal design for active failure detection in uncertain linear systems with a priori information

A method for auxiliary signal design for active failure detection based on a multi-model formulation of normal and failed systems is developed which allows for a priori information about initial conditions and the possibility of having a known input in addition to the test signal. These results permit the consideration of additional types of failures, such as biases, that a previous approach could not handle. Both theoretical results and computational algorithms are developed.     

活动轮廓模型目标跟踪算法综述

目标跟踪是当前计算机视觉领域最活跃的研究主题。首先对基本的跟踪类型进行了介绍;然后讨论了基于活动轮廓模型的图像分割,重点分析了参数活动轮廓模型的梯度矢量流模型（Gradient Vector Flow,GVF）,以及几何活动轮廓模型中的模型;并讨论了基于粒子滤波的目标跟踪算法的研究现状,最后展望了这一领域未来研究的热点。     

Segmentation of volumetric tissue images using constrained active contour models

In this article we describe an application of active contour model for the segmentation of 3D histo-pathological images. The 3D images of a thick tissue specimen are obtained as a stack of optical sections using confocal laser beam scanning microscope (CLSM). We have applied noise reduction and feature enhancement methods so that a smooth and slowly varying potential surface is obtained for proper convergence. To increase the capture range of the potential surface, we use a combination of distance potential and the diffused gradient potential as external forces. It has been shown that the region-based information obtained from low-level segmentation can be applied to reduce the adverse influence of the neighbouring nucleus having a strong boundary feature. We have also shown that, by increasing the axial resolution of the image stack, we can automatically propagate the optimum active contour of one image slice to its neighbouring image slices as an appropriate initial model. Results on images of prostate tissue section are presented.     

Efficient numerical schemes for Chan-Vese active contour models in image segmentation

Multimedia Tools and Applications - In this paper, we introduce multi-symplectic Lagrangian variational integrators for solving Chan-Vese active contour models in image segmentation. Energy...     

结合统计和梯度信息的高效活动轮廓模型

提出一种由统计和梯度信息驱动的活动轮廓模型。该模型有效利用梯度信息使演化轮廓线快速精确地定位到物体的边缘;同时,由局部统计信息和全局统计信息构造符号压力函数,减少噪声对轮廓线演化的影响。另外,模型利用局部统计信息能够有效处理灰度分布不均的图像,全局信息的利用避免了演化轮廓线陷入局部最小,因此,该模型可以任意设置初始轮廓线。最后通过高斯卷积核对水平集函数规则化,避免了传统模型中计算代价高昂的重新初始化和规则化。实验结果表明,提出的模型不仅能够在任意初始轮廓下精确有效地分割灰度分布均匀的图像和不均匀的图像,而且对噪声具有较好的鲁棒性。     

Network snakes: graph-based object delineation with active contour models

In this paper, a graph-based method of active contour models called network snakes is presented and investigated. Active contour models are a well-known method in computer vision, bridging the gap between low-level feature extraction or segmentation and high-level geometric representation of objects. But the original concept is limited to single closed object boundaries. Network snakes are the method enabling a free optimization of arbitrary graphs representing the geometric position of networks and boundaries between adjacent objects. The main impacts of network snakes are the combination of the image energy representing objects in the real world, the internal energy incorporating shape characteristics, and the topology representing the structure of the scene. The introduction and exploitation of the topology in a comprehensive energy functional turn out to be a powerful technique to cope with complex questions of object delineation from imagery. Network snakes are analyzed and evaluated with both synthetic and real data to point out the role of the required initialization, the benefit of the introduced topology and the transferability. Exemplary investigated real applications are the delineation of field boundaries from remotely sensed imagery, the refinement of road networks from airborne SAR images and bio-medical tasks delineating adjacent biological cells in microscopic images. Concluding remarks are given at the end to discuss potential future research.     

一种基于主动轮廓模型的蚁群图像分割算法

通过对主动轮廓模型进行图像分割的过程研究发现,其多阶段决策问题与蚁群算法的决策过程非常相似．文中根据主动轮廓模型的特点构建了一类新的蚁群求解算法,把图像分割问题转化成最优路径的搜索问题,为获取精确的图像轮廓提供了新方法．证明了该方法以概率1收敛到最优解,即可以在能量函数的约束下找到最好的边界．本方法还可以推广到其他主动轮廓模型的图像分割问题中．仿真结果表明,本文提出的分割方法比文献中的遗传算法更为有效．     

基于先验知识的肝脏轮廓线提取算法研究

准确有效地提取肝脏CT序列的轮廓线是腹部软组织三维模型重建与可视化的关键问题之一。针对肝脏轮廓线提取准确性不高的问题, 提出了一种基于先验知识的肝脏轮廓线提取算法。首先利用拉普拉斯算法进行CT图像增强, 再利用基于边缘先验知识的套索模型对感兴趣区域进行半自动的初始化, 最后通过改进的Snake算法准确地提取肝脏CT图像的边缘。针对序列CT肝脏的边缘提取, 提出根据CT图像序列之间的相关性, 将上一幅图像的轮廓线提取结果作为下一幅CT图像边缘提取的初始化点, 接着批处理地提取CT序列的肝脏边缘。实验结果表明：该算法大大减少了手动初始化结果对目标边缘轮廓准确提取的依赖性, 并有效地解决了肝脏轮廓线的提取问题。     

Iterative SNR estimation using a priori information

A class iterative signal-to-noise ratio (SNR) estimation algorithm is proposed in this paper. The data samples are governed by a given distribution with a priori. The expectation maximization (EM) algorithm is applied to iteratively maximize the likelihood function so as to realize the SNR estimation. Cramer–Rao bounds (CRB) with different a priori are compared for binary phase shift keying and orthogonal phase shift keying systems, which show the potential of the SNR estimator in turbo-like systems. In high-order modulations, simulation results show that the reduced-complexity iterative method with equal a priori has better performance in middle or high SNR region than the foregone ones. Moreover, the new method with feedback information is the best when its iteration number is 4 and extrinsic information larger than 0.4. These methods are applied in the bit-interleaved coded modulation with iterative decode (BICM-ID) system to validate the effect of the proposed methods.     

Vanishing point detection without any a priori information

Even though vanishing points in digital images result from parallel lines in the 3D scene, most of the proposed detection algorithms are forced to rely heavily either on additional properties (like orthogonality or coplanarity and equal distance) of the underlying 3D lines, or on knowledge of the camera calibration parameters, in order to avoid spurious responses. In this work, we develop a new detection algorithm that relies on the Helmoltz principle recently proposed for computer vision by Desolneux et al (2001; 2003), both at the line detection and line grouping stages. This leads to a vanishing point detector with a low false alarms rate and a high precision level, which does not rely on any a priori information on the image or calibration parameters, and does not require any parameter tuning.     

利用区域信息融合混合活动轮廓模型的 河流遥感图像分割

目的 河流遥感图像是背景复杂的非匀质图像,利用传统的活动轮廓模型进行分割往往不够准确。针对这一问题,提出了基于区域信息融合的混合活动轮廓模型来分割河流遥感图像。方法 该混合模型将Chan-Vese(CV)模型和基于交叉熵的活动轮廓模型的外部能量约束项相结合,并赋予归一化调节比例系数。通过计算轮廓曲线内外区域像素灰度的方差和交叉熵,指导曲线逼近目标边缘。为了加速混合模型的演化,引入曲线内外区域像素灰度的类内绝对差,取代原有的内外区域能量权值,以提高混合模型的分割效率。结果 大量实验结果表明,相较于CV模型、测地线模型、基于交叉熵的活动轮廓模型、CV模型和测地线模型的混合模型以及局部全局灰度拟合能量模型(LGIF),本文混合模型分割河流遥感图像的灵敏度和上述方法都接近于100%,准确率大幅提升,在90%以上,虚警率则下降了约50%,且所需迭代次数和运行时间更少。结论 本文提出的混合模型主要适用于具有一定对比度的河流遥感图像,在分割性能和分割效率两个方面,都有明显的优势。     

边缘检测和Snake Model结合的轮廓识别

基于主动轮廓识别和被动轮廓识别方法各自的优缺点,提出结合主动和被动的方法,先对图像进行被动轮廓的预处理,然后再应用主动轮廓的方法,通过比较选出Canny算子进行预处理,再通过基于概率的方法分割图像,最后再应用Snake模型进行轮廓提取。并取得较好的应用。     

自适应正则化活动轮廓模型

针对Chan-Vese模型含有许多参数,分割时需要人为调整参数,耗费大量的人力和时间的问题,提出了一个自适应正则化活动轮廓模型。首先,对Chan-Vese模型的数据项进行简化;其次,使用改进的边界加权H¹正则化代替长度项;最后,形成了一个新的不含任何参数的活动轮廓模型。在分割实验中,该模型对初始轮廓的大小、位置不敏感,具有较强的抗噪性,分割6幅图像的平均时间和迭代次数分别为1.5834 s、19次。实验结果表明,所提模型无需人工调整参数,能够分割强噪声图像和灰度不均图像,并且具有较快的分割速度。