Multiscale Active Contours

We propose a new multiscale image segmentation model, based on the active contour/snake model and the Polyakov action. The concept of scale, general issue in physics and signal processing, is introduced in the active contour model, which is a well-known image segmentation model that consists of evolving a contour in images toward the boundaries of objects. The Polyakov action, introduced in image processing by Sochen-Kimmel-Malladi in Sochen et al. (1998), provides an efficient mathematical framework to define a multiscale segmentation model because it generalizes the concept of harmonic maps embedded in higher-dimensional Riemannian manifolds such as multiscale images. Our multiscale segmentation model, unlike classical multiscale segmentations which work scale by scale to speed up the segmentation process, uses all scales simultaneously, i.e. the whole scale space, to introduce the geometry of multiscale images in the segmentation process. The extracted multiscale structures will be useful to efficiently improve the robustness and the performance of standard shape analysis techniques such as shape recognition and shape registration. Another advantage of our method is to use not only the Gaussian scale space but also many other multiscale spaces such as the Perona-Malik scale space, the curvature scale space or the Beltrami scale space. Finally, this multiscale segmentation technique is coupled with a multiscale edge detecting function based on the gradient vector flow model, which is able to extract convex and concave object boundaries independent of the initial condition. We apply our multiscale segmentation model on a synthetic image and a medical image.     

Elastic-model driven analysis of several views of a deformablecylindrical object

This paper proposes a method to extract regions of a deformable object from several views of it while finding the correspondence of the object among the views. The method has been developed to analyze X-ray images of a stomach. Owing to the physical (not physiological) deformation of the stomach and changes of the camera angle, the shape of the stomach regions are fairly different among the images. In order to collectively analyze these images, we use an elastic stomach model. Firstly, our method builds an elastic stomach model based on the stomach shape in one image. Considering each photographing condition, the deformation of the stomach in each image is simulated with the elastic model. Referring to the predicted contour which is obtained by projecting the deformed model from the camera angle of each image, the contour is robustly extracted from noisy images in a model-driven way. Since the predicted contour registered in each image corresponds with the elastic model, the position of each stomach part in the image is simultaneously obtained; corresponding parts can be found among the images through the model. Experimental results of analyzing several types of stomach X-ray images are shown and discussed     

A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images.     

Elastic Tubes: Modeling Elastic Deformation of Hollow Tubes

The Cosserat theory of elastic rods has been used in a wide range of application domains to model and simulate the elastic deformation of thin rods. It is physically accurate and its implementations are efficient for interactive simulation. However, one requirement of using Cosserat rod theory is that the tubular object must have rigid cross‐sections that are small compared to its length. This requirement make it difficult for the approach to model elastic deformation of rods with large, non‐rigid cross‐sections that can change shape during rod deformation, in particular, hollow tubes. Our approach achieves this task using a hybrid model that binds a mesh elastically to a reference Cosserat rod. The mesh represents the surface of the hollow tube while the reference rod models bending, twisting, shearing and stretching of the tube. The cross‐sections of the tube may take on any arbitrary shape. The binding is established by a mapping between mesh vertices and the rod's directors. Deformation of the elastic tube is accomplished in two phases. First, the reference rod is deformed according to Cosserat theory. Next, the mesh is deformed using Laplacian deformation according to its mapping to the rod and its surface elastic energy. This hybrid approach allows the tube to deform in a physically correct manner in relation to the bending, twisting, shearing, and stretching of the reference rod. It also allows the surface to deform realistically and efficiently according to surface elastic energy and the shape of the reference rod. In this way, the deformation of elastic hollow tubes with large, non‐rigid cross‐sections can be simulated accurately and efficiently.     

基于先验形状的人工目标识别与轮廓恢复

以具有典型形状特征的操场为例,探讨了形状特征在人工目标自动识别中的应用。基于形状特征的人工目标识别,一般首先将图像分割为多个基元,然后依据人工目标有较规则形状的特点,以形状特征作为指标进行识别。由于高分辨率遥感图像细节信息丰富,这种方法所提取的目标轮廓往往不完整。本文提出了"图像-基元-目标-轮廓恢复"的识别模式,在目标识别后采用加入形状先验知识的主动轮廓模型对目标进行轮廓恢复。实验结果表明,这种方法可以有效地修正目标提取结果中的轮廓缺失。     

稀疏活动轮廓扩展形状脚本模型目标检测算法

传统的稀疏活动轮廓模型可以较好地解决目标微小形变情况下的定位问题,但是对训练样本要求比较严格,且在目标发生较大形变情况下采用学习到的可变形模板对目标进行定位会产生一定偏差。针对该问题,提出一种稀疏活动轮廓扩展形状脚本模型的目标检测算法。利用勾画样本通过扩展活动轮廓模型学习到组成目标的可变形形状图案,这些形状图案构成的形状脚本模型能够清晰地定义目标模式;采用递归sum-max maps结构进行搜索,用形状脚本模型匹配测试图像实现目标定位。经过多组实验,结果表明所提算法能较好地解决目标在发生较大形变、存在遮挡以及复杂背景下的定位问题。     

基于刚体运动与弹性运动的机械系统动力学建模研究

讨论了具有刚体运动与柔性变形的机械系统的动力学建模,将刚体自由度与弹性变形自由度看作广义坐标,利用有限元法对具有刚性运动与弹性变形的机械系统的运动与变形进行了描述,得到了以刚体位移与弹性变形位移表示的单元的广义惯性力;从应力应变入手,得到了表示单元弹性变形与几何非线性变形的结构刚度矩阵与几何非线性刚度矩阵,使用Kane方程推导了弹性连杆机构的单元运动方程,这种建模方法,可以使用在任意结构的机械系统。     

Contour based object detection using part bundles

In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.     

The use of active deformable models in model-based robotic visual servoing

This paper presents a new approach for visual tracking and servoing in robotics. We introduce deformable active models as a powerful means for tracking a rigid or semi-rigid (possibly partially occluded) object in movement within the manipulator's workspace. Deformable models imitate, in real-time, the dynamic behavior of elastic structures. These computer-generated models are designed to capture the silhouette of objects with well-defined boundaries, in terms of image gradient. By means of an eye-in-hand robot arm configuration, the desired motion of the end-effector is computed with the objective of keeping the target's position and shape invariant with respect to the camera frame. Optimal estimation and control techniques (LQG regulator) have been successfully implemented in order to deal with noisy measurements provided by our vision sensor. Experimental results are presented for the tracking of a rigid or semi-rigid object. The experiments performed in a real-time environment show the effectiveness and robustness of the proposed method for servoing tasks based on visual feedback.     

A shape prior constraint for implicit active contours

We present a shape prior constraint to guide the evolution of implicit active contours. Our method includes three core techniques. Firstly, a rigid registration is introduced, using a line search method within a level set framework. The method automatically finds the time step for the iterative optimization processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances the impact of the shape prior versus the image guidance of the active contour. A mixed stopping condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our method is completely non-parametric and avoids taking linear combinations of non-linear signed distance functions, which would cause problems because distance functions are not closed under linear operations. Experimental results show that our method is able to extract the desired objects in several circumstances, namely when noise is present in the image, when the objects are in slightly different poses and when parts of the object are invisible in the image.     

Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images

We present a variational framework for naturally incorporating prior shape knowledge in guidance of active contours for boundary extraction in images. This framework is especially suitable for images collected outside the visible spectrum, where boundary estimation is difficult due to low contrast, low resolution, and presence of noise and clutter. Accordingly, we illustrate this approach using the segmentation of various objects in synthetic aperture sonar (SAS) images of underwater terrains. We use elastic shape analysis of planar curves in which the shapes are considered as elements of a quotient space of an infinite dimensional, non-linear Riemannian manifold. Using geodesic paths under the elastic Riemannian metric, one computes sample mean and covariances of training shapes in each classes and derives statistical models for capturing class-specific shape variability. These models are then used as shape priors in a variational setting to solve for Bayesian estimation of desired contours as follows. In traditional active contour models curves are driven towards minimum of an energy composed of image and smoothing terms. We introduce an additional shape term based on shape models of relevant shape classes. The minimization of this total energy, using iterated gradient-based updates of curves, leads to an improved segmentation of object boundaries. This is demonstrated using a number of shape classes in two large SAS image datasets.     

Reconstruction of 3D contour with an active laser‐vision robotic system

This paper presents a 3D contour reconstruction approach employing a wheeled mobile robot equipped with an active laser‐vision system. With observation from an onboard CCD camera, a laser line projector fixed‐mounted below the camera is used for detecting the bottom shape of an object while an actively‐controlled upper laser line projector is utilized for 3D contour reconstruction. The mobile robot is driven to move around the object by a visual servoing and localization technique while the 3D contour of the object is being reconstructed based on the 2D image of the projected laser line. Asymptotical convergence of the closed‐loop system has been established. The proposed algorithm also has been used experimentally with a Dr Robot X80sv mobile robot upgraded with the low‐cost active laser‐vision system, thereby demonstrating effective real‐time performance. This seemingly novel laser‐vision robotic system can be applied further in unknown environments for obstacle avoidance and guidance control tasks. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

先验形状力场参数活动轮廓模型及其医学图像分割

先验形状参数活动轮廓模型是一种抗噪声干扰稳定的图像分割方法．它具有对弱边缘、凹区域进行分割的能力,同时有较大的边缘捕捉范围．通过引入一种非距离性的先验形状力场,构建一种新的能反映先验形状的参数活动轮廓模型．新的先验形状活动轮廓模型避免了曲线之间距离的计算,减少了模型的复杂性．新的方法可以较好地解决传统型参数活动轮廓模型的一些本质缺陷.实验对带噪声且为弱边缘的医学CT图像和超声图像进行分割能得到理想的边缘轮廓.     

Geodesic active contours and level sets for the detection andtracking of moving objects

This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method. A new approach named Hermes is proposed, which exploits aspects from the well-known front propagation algorithms and compares favorably to them. Very promising experimental results are provided using real video sequences     

The Velocity Snake: Deformable Contour for Tracking in Spatio-Velocity Space

We present a new active contour model for boundary tracking and position prediction of nonrigid objects, which results from applying a velocity control to the class of elastodynamical contour models, known as snakes. The proposed control term minimizes an energy dissipation function which measures the difference between the contour velocity and the apparent velocity of the image. Treating the image video-sequence as continuous measurements along time, it is shown that the proposed control results in robust tracking. This is in contrast to the original snake model which is proven to have tracking errors relative to image (object) velocity, thus resulting in high sensitivity to image clutter. The motion estimation further allows for position prediction of nonrigid boundaries. Based on the proposed control approach, we propose a new class of real time tracking contours, varying from models with batch-mode control estimation to models with real time adaptive controllers.     

Object matching using deformable templates

We propose a general object localization and retrieval scheme based on object shape using deformable templates. Prior knowledge of an object shape is described by a prototype template which consists of the representative contour/edges, and a set of probabilistic deformation transformations on the template. A Bayesian scheme, which is based on this prior knowledge and the edge information in the input image, is employed to find a match between the deformed template and objects in the image. Computational efficiency is achieved via a coarse-to-fine implementation of the matching algorithm. Our method has been applied to retrieve objects with a variety of shapes from images with complex background. The proposed scheme is invariant to location, rotation, and moderate scale changes of the template     

基于DRLSE模型的运动目标跟踪

高斯粒子滤波不能处理曲线的拓扑变化,而基于水平集的几何活动轮廓模型能较好地适应拓扑变化,为了跟踪和提取刚体和非刚体运动目标精确的轮廓信息,提出基于距离规则化的水平集演化(DRLSE)模型和高斯粒子滤波(GPF)相结合的运动目标跟踪方法.首先用高斯粒子滤波对目标跟踪得到目标的运动区域,然后把水平集规则项引入到测地线活动轮廓模型中,以外接轮廓的中心为基础进行DRLSE.距离规则化不仅消除了水平集重新初始化的需要,而且避免了因此而导致的数值错误,在水平集演化过程中保持了水平集函数的规则化.最后,将获得的精确轮廓信息反馈到跟踪框架.实验结果表明,该方法适用于刚体和非刚体目标,在实际交通环境中跟踪结果更加精确.     

Incorporating shape prior into geodesic active contours for detecting partially occluded object

A new method to incorporate shape prior knowledge into geodesic active contours for detecting partially occluded object is proposed in this paper. The level set functions of the collected shapes are used as training data. They are projected onto a low dimensional subspace using PCA and their distribution is approximated by a Gaussian function. A shape prior model is constructed and is incorporated into the geodesic active contour formulation to constrain the contour evolution process. To balance the strength between the image gradient force and the shape prior force, a weighting factor is introduced to adaptively guide the evolving curve to move under both forces. The curve converges with due consideration of both local shape variations and global shape consistency. Experimental results demonstrate that the proposed method makes object detection robust against partial occlusions.     

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.     

Recovery of nonrigid motion and structure

The authors introduce a physically correct model of elastic nonrigid motion. This model is based on the finite element method, but decouples the degrees of freedom by breaking down object motion into rigid and nonrigid vibration or deformation modes. The result is an accurate representation for both rigid and nonrigid motion that has greatly reduced dimensionality, capturing the intuition that nonrigid motion is normally coherent and not chaotic. Because of the small number of parameters involved, this representation is used to obtain accurate overstrained estimates of both rigid and nonrigid global motion. It is also shown that these estimates can be integrated over time by use of an extended Kalman filter, resulting in stable and accurate estimates of both three-dimensional shape and three-dimensional velocity. The formulation is then extended to include constrained nonrigid motion. Examples of tracking single nonrigid objects and multiple constrained objects are presented