基于四阶偏微分方程平滑的图像分割新方法

在方向曲率模值描述图像平滑度的泛函基础上，推导出一种四阶偏微分方程(PDE)图像降噪模型，在有效降噪的同时，较好地保持了边缘。该方法处理结果为分段线性图像，且在目标边缘处梯度存在阶跃。利用降噪结果的这一特点，提出一种新测地线主动轮廓模型。实验表明，新模型不但稳定，轮廓提取能力强，而且收敛速度快。     

Detection and recognition of contour parts based on shape similarity

Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, it is usually impossible to extract complete object contours or to segment the whole objects. However, in many cases parts of contours can be correctly reconstructed either by performing edge grouping or as parts of boundaries of segmented regions. Therefore, recognition of objects based on their contour parts seems to be a promising as well as a necessary research direction.The main contribution of this paper is a system for detection and recognition of contour parts in digital images. Both detection and recognition are based on shape similarity of contour parts. For each contour part produced by contour grouping, we use shape similarity to retrieve the most similar contour parts in a database of known contour segments. A shape-based classification of the retrieved contour parts performs then a simultaneous detection and recognition.An important step in our approach is the construction of the database of known contour segments. First complete contours of known objects are decomposed into parts using discrete curve evolution. Then, their representation is constructed that is invariant to scaling, rotation, and translation.     

几何活动轮廓模型中停止速度函数的尺度变换

近年来,通过水平集方法实现的几何活动轮廓模型(GAC)已成为图像处理和计算机视觉领域里十分流行的图像分割方法。几乎所有的GAC模型都依赖于停止速度函数,该函数通常是基于图像梯度定义的,其作用是使活动轮廓(演化曲线)停止在所希望的目标边界上。为了加快活动轮廓的演化速度,提出对停止速度函数进行尺度变换的方法。对4幅人工和自然图像的实验结果显示,所提出的方案能够大大减少分割时间,同时,对于凹陷边界和弱边界的分割取得了更好的效果。     

Polygonal Representations of Digital Sets

In the context of discrete curve evolution the following problem is of relevance: decompose the boundary of a plane digital object into convex and concave parts. Such a decomposition is very useful for describing the form of an object, e.g. for shape databases. Although the problem is relatively trivial in ordinary plane geometry, in digital geometry its statement becomes a very difficult task due to the fact that in digital geometry there is no simple set-complement duality. The paper is based on results given by Hübler et al. The main new contribution of the paper is the generalization of the concepts introduced by these authors to nonconvex sets. The digital geometric low level segmentation of the boundary of a digital object can be used as a starting basis for further reduction of the boundary by means of discrete evolution.     

无需重新初始化的自适应快速水平集演化模型

水平集方法已被广泛地应用在图像分割中,传统的水平集方法需要通过周期性的初始化水平集函数使得它一直保持在符号距离函数附近,然而初始化与水平集理论和实现相违背。最近,Li C等人提出一种完全不需要初始化的变分模型,该模型的主要不足就是单方向演化,即演化曲线或收缩或扩张到目标边界。针对二值图像提出一种新的基于距离保持水平集方法的活动轮廓模型,它不依赖于初始位置,演化曲线准确地收敛在目标边界,更重要的是曲线演化只需一次迭代。     

Coupled parametric active contours

We propose an extension of parametric active contours designed to track nonoccluding objects transiently touching each other, a task where both parametric and single level set-based methods usually fail. Our technique minimizes a cost functional that depends on all contours simultaneously and includes a penalty for contour overlaps. This scheme allows us to take advantage of known constraints on object topology, namely, that objects cannot merge. The coupled contours preserve the identity of previously isolated objects during and after a contact event, thus allowing segmentation and tracking to proceed as desired.     

采用测地线活动轮廓模型检测与跟踪运动目标

水平集几何活动轮廓模型能较好地适应曲线的拓扑变化.为了跟踪和获取刚体和非刚体运动目标的轮廓信息,提出了一种基于改进测地线活动轮廓(GAC)模型和Kalman滤波相结合的算法以检测和跟踪运动目标.该算法首先采用高斯混合模型和背景差分获取目标的运动区域,在运动区域内采用引入距离规则化项的GAC模型进行曲线演化,使改进GAC模型在运动目标的真实轮廓处收敛;然后通过结合Kalman滤波预测目标下一帧的位置,实现对目标轮廓跟踪.实验结果表明,该方法适用于刚体和非刚体目标,在部分遮挡的情况下也能保持良好的检测和跟踪效果.     

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)。     

Isophotes Selection and Reaction-Diffusion Model for Object Boundaries Estimation

This paper investigates generic region-based segmentation schemes using area-minimization constraint and background modeling, and develops a computationally efficient framework based on level lines selection coupled with biased anisotropic diffusion. A common approach to image segmentation is to construct a cost function whose minima yield the segmented image. This is generally achieved by competition of two terms in the cost function, one that punishes deviations from the original image and another that acts as a regularization term. We propose a variational framework for characterizing global minimizers of a particular segmentation energy that can generates irregular object boundaries in image segmentation. Our motivation comes from the observation that energy functionals are traditionally complex, for which it is usually difficult to precise global minimizers corresponding to best segmentations. In this paper, we prove that the set of curves that minimizes the basic energy model under concern is a subset of level lines or isophotes, i.e. the boundaries of image level sets. The connections of our approach with region-growing techniques, snakes and geodesic active contours are also discussed. Moreover, it is absolutely necessary to regularize isophotes delimiting object boundaries and to determine piecewise smooth or constant approximations of the image data inside the objects boundaries for vizualization and pattern recognition purposes. Thus, we have constructed a reaction-diffusion process based on the Perona-Malik anisotropic diffusion equation. In particular, a reaction term has been added to force the solution to remain close to the data inside object boundaries and to be constant in non-informative regions, that is the background region. In the overall approach, diffusion requires the design of the background and foreground regions obtained by segmentation, and segmentation of the adaptively smoothed image is performed after each iteration of the diffusion process. From an application point of view, the sound initialization-free algorithm is shown to perform well in a variety of imaging contexts with variable texture, noise and lighting conditions, including optical imaging, medical imaging and meteorological imaging. Depending on the context, it yields either a reliable segmentation or a good pre-segmentation that can be used as initialization for more sophisticated, application-dependent segmentation models.