基于多分辨率方法的主动轮廓线跟踪算法

由Ｋａｓｓ等１９８７年首次提出的主动次轮廓线模型，在数字图像分析和计算机视觉领域得到了越来越广泛的应用，基于单分辨率的主动轮廓线跟踪算法在一个共同的缺点，即：要求初始轮廓线离目标的真实轮廓线很近，这样的算法用于跟踪将只能跟踪缓慢运动的目标，从而限制了主动轮廓线跟踪算法的应用范围，本文提出了一种基于多分辨率的主动轮廓线跟踪算法，它不但继承了基于单分辨率的主动轮廓线算法的优点，而且降低了对初始轮廓线的     

基于主动轮廓线模型的海面运动目标跟踪

将主动轮廓线模型用于海面运动船只的跟踪，提出了一种自动选择主动轮廓线初始控制点的方法，增强了主动轮廓线模型的实用性：并将基于主动轮廓线模型的跟踪方法用于多个海面运动目标的跟踪。实验结果表明，提出的初始主动轮廓线自动选取方法可以准确地选择目标的轮廓线的特征点；基于主动轮廓线模型的跟踪方法可以比较准确地跟踪运动船只的主要轮廓特征。     

一种新的统计仿射不变量

本文在充分分析了现有各种仿射不变量的基础上，提出了一种新的统计仿射不变量，它对于发生尺度变化、旋转、扭曲和平移的目标具有不变性。实验结果表明，相对于传统的仿射不变量，在目标轮廓分割不完整或噪声污染的情况下，仍能够保持较高的稳健性，同时克服了基于轮廓的小波方法和傅立叶级数等方法对目标轮廓出现缺陷时的不足，
，在图像目标识别中具有实用价值。     

基于多尺度图像的主动轮廓线模型

主动轮廓线模型是广泛应用于数字图像处理的一种目标轮廓跟踪算法,但在实际使用过程中,现有模型易受干扰噪声及虚拟边缘的影响,且于凹陷轮廓的跟踪能力较差,在多尺度图像分析的基础上,引入梯度矢量流的概念,并改进其计算方法,提出了一种新的主动轮廓线模型,该模型利用梯度矢量流产生的引力,在图像的尺度空间中搜索目标轮廓,不仅能有效地排除干扰,搜索凹陷轮廓,而且便于引入新的约束条件,实验表明该模型有较好的鲁棒性和     

一种改进的多尺度梯度矢量流模型

梯度矢量流主动轮廓线模型是广泛应用于数字图像处理的一种目标轮廓跟踪算法,但易受干扰噪声及虚假边缘的影响,在结合多尺度图像分析的基础上,利用改进的梯度矢量流场,提出了一种改进的主动轮廓线模型。实验表明,该方法较好的限制了非目标边缘和噪声干扰的影响,提高了模型分割的精确性,具有较好的分割效果。     

基于NMI特征的目标识别与跟踪

本文提出一种全新的图像分割方法——连通线多级切割方法，并在此基础上建立图像NMI特征的目标识别与跟踪算法。文章给出了运用连通线多级切割方法实现阈值求取、图像分割、目标识别与跟踪的技术技巧。通过实验验证了NMI特征的缩放不变性、旋转不变性和平移不变性以及不同物体的NMI特征差异，表明本文算法具有良好的实时性和精确的目标识别与跟踪效果。     

一种新的主动轮廓线跟踪算法

主动轮廓线模型,又称"蛇",由Kass等于1987年首次提出后在数字图像分析和计算机视觉等领域正在得到越来越广泛的应用.不过,现有的主动轮廓线搜索与跟踪算法存在许多问题.本文提出一种新的主动轮廓线跟踪算法--new_snake算法.它不但继承了已有算法的优点,而且克服了它们的主要缺点.它具有下列特点:算法快速收敛;控制点的数目自适应地改变,保证稳定跟踪;将硬强制力直接引入能量公式;可以方便地添加色彩信息约束;削弱了各参数的改变对算法的影响.大量实验验证了该算法的鲁棒性和实用性.     

基于改进随机蕨的跟踪注册方法研究

针对无标识增强现实跟踪注册方法在复杂环境下存在跟踪注册失败、速度较慢的问题,提出一种基于改进随机蕨的无标识增强现实跟踪注册方法。该方法以真实场景中的目标物体图像作为模板,使用随机蕨分类器进行目标检测,解决环境光照变化或目标被遮挡跟踪注册失败的问题。使用FAST角点进行特征检测提升检测速度,对随机蕨的仿射过程进行改进来弥补特征点尺度不变性和仿射不变性缺失。通过该分类器进行特征匹配,进而估计三维位姿并渲染注册虚拟物体。实验结果证明,该方法具有较好的准确性、实时性和鲁棒性。     

一种新的图像局部仿射不变特征提取方法

提出了一种新的图像局部仿射不变特征的提取方法,首先利用阈值分割在图像中确定某个灰度取值范围的连通区域,文中证明了该区域的几何中心在图像仿射变换前后具有不变性,然后将几何中心作为局部仿射不变特征的"     

基于纹理图像分析的生物视觉模型不变性评价

基于纹理图像,从计算机视觉角度对生物视觉模型——视皮层目标识别的标准模型进行定量分析与评价。对原始图像分别进行尺度、旋转及仿射等变化,利用标准模型提取变化后图像的生物视觉特征,再根据提取的生物视觉特征对纹理图像进行分类,采用图像分类结果的曲线下面积来定量分析和评价生物视觉模型是否具有不变性。大量与局部二元模式特征的对比实验表明,该模型提取的生物视觉特征对于纹理图像具备优良的尺度、旋转与仿射不变性。     

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.     

融合凹点检测与仿射变换的活动轮廓模型

目的 针对基于矢量场的活动轮廓模型,如经典的梯度矢量流（GVF）模型、矢量场卷积（VFC）模型等,在提取凹形物体时矢量场常出现平衡点,不能较好地收敛到凹陷区域、尤其是深而窄的凹形及复杂凹陷区域的问题。提出一种融合凹点检测与仿射变换的活动轮廓模型。方法 首先利用活动轮廓模型进行曲线演化,得到演化后轮廓曲线上各点的坐标并求出各点的法线方向;然后基于凹点检测的方法,判断各点的凹凸性,利用梯度判断法,检测出未收敛到目标边界的凹点;其次对各凹点进行法向方向的仿射变换。在接近且不越过目标边界的情况下求出可变换的最大距离,变换后的点穿越了平衡点区域,让变换后的点代替原来的点形成新的轮廓曲线;最后为保证提取边界的精确性,将变换后的轮廓曲线再次演化并最终收敛到目标边界。结果 通过对具有凹陷区域的合成图像进行分割,计算提出模型分割结果的平均Jaccard相似系数（JS）值为95.51%,相比目前先进的GVF模型,VFC模型和自适应扩散流（ADF）模型分别提高了15.08%,12.09%和10.70%,整体效果上优于几种先进的模型。然后又对单/多目标真实图像及含噪的图像进行分割,证实提出模型分割性能的鲁棒性。结论 提出的模型有效地避免了凹形区域内的平衡点问题,可以对深凹形及复杂凹形图像进行有效分割,并且提高了分割精度。此外,该模型能融合到任何基于矢量场的活动轮廓模型中,具有广泛的普适性。     

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.     

Object's contour detection scheme based on image fusion

Three novel object's contour detection schemes based on image fusion are proposed in this paper. In these schemes an active contour model is applied to detect the object's contour edge. Since an object's contour in an infrared (IR) image is usually clearer than that in a visible image, the convergent active contour in a visible image is improved with that in an IR image. The first contour detection scheme is realized by revising the shape-preserving active contour model. The second scheme minimizes the B-spline L ₂ norm's square of the difference of the B-spline control point vectors in two modal images. Contour tracking and extraction experiments indicate that the first scheme outperforms the second one. Moreover, a third scheme based on the active contour and pixel-level image fusion is proposed for images with incomplete but complementary scene information. An example using contour extraction of a partially hidden tank proves its efficacy.     

Robust tracking of position and velocity with Kalman snakes

A new Kalman-filter based active contour model is proposed for tracking of nonrigid objects in combined spatio-velocity space. The model employs measurements of gradient-based image potential and of optical-flow along the contour as system measurements. In order to improve robustness to image clutter and to occlusions an optical-flow based detection mechanism is proposed. The method detects and rejects spurious measurements which are not consistent with previous estimation of image motion     

Moment-based alignment for shape prior with variational B-spline level set

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.     

一种新结构下的主动轮廓算法

经典主动轮廓模型是采用由内部能量和外部能量共同组成的能量函数来进行收敛计算的，其中外部能量对精确收敛到目标轮廓起决定性作用，而内部能量在保证轮廓线进行合理变动的同时，却对收敛精确性产生了负作用。为克服这一问题，因而给出一种新的主动轮廓结构，即先把内部能量从能量函数中分离出来，仅采用外部能量进行收敛计算，然后在此结构下重新定义图像能量和控制能量，同时引入了轮廓修正函数，把连续性和光滑性约束应用到Snake曲线的变动过程之中。仿真实验表明，新结构下的主动轮廓算法对初始轮廓要求不高，具有收敛速度快、跟踪精度高等优点。     

Fusing Visual and Inertial Sensing to Recover Robot Ego‐motion

A method for estimating mobile robot ego‐motion is presented, which relies on tracking contours in real‐time images acquired with a calibrated monocular video system. After fitting an active contour to an object in the image, 3D motion is derived from the affine deformations suffered by the contour in an image sequence. More than one object can be tracked at the same time, yielding some different pose estimations. Then, improvements in pose determination are achieved by fusing all these different estimations. Inertial information is used to obtain better estimates, as it introduces in the tracking algorithm a measure of the real velocity. Inertial information is also used to eliminate some ambiguities arising from the use of a monocular image sequence. As the algorithms developed are intended to be used in real‐time control systems, considerations on computation costs are taken into account. © 2004 Wiley Periodicals, Inc.