请教高手帮我翻译以下关于人脸识别的文章,十万火急,太感谢了!

请教高手帮我翻译以下关于人脸识别的文章,十万火急,太感谢了!
Abstract—Snakes, or active contours, are used extensively in
computer vision and image processing applications, particularly
to locate object boundaries. Problems associated with initialization
and poor convergence to boundary concavities, however,
have limited their utility. This paper presents a new external force
for active contours, largely solving both problems. This external
force, which we call gradient vector flow (GVF), is computed
as a diffusion of the gradient vectors of a gray-level or binary
edge map derived from the image. It differs fundamentally from
traditional snake external forces in that it cannot be written as the
negative gradient of a potential function, and the corresponding
snake is formulated directly from a force balance condition rather
than a variational formulation. Using several two-dimensional
(2-D) examples and one three-dimensional (3-D) example, we
show that GVF has a large capture range and is able to move
snakes into boundary concavities.
Index Terms—Active contour models, deformable surface models,
edge detection, gradient vector flow, image segmentation,
shape representation and recovery, snakes.
I. INTRODUCTION
SNAKES [1], or active contours, are curves defined within
an image domain that can move under the influence of
internal forces coming from within the curve itself and external
forces computed from the image data. The internal and external
forces are defined so that the snake will conform to an object
boundary or other desired features within an image. Snakes
are widely used in many applications, including edge detection
[1], shape modeling [2], [3], segmentation [4], [5], and motion
tracking [4], [6].
There are two general types of active contour models
in the literature today: parametric active contours [1] and
geometric active contours [7]–[9]. In this paper, we focus on
parametric active contours, although we expect our results
to have applications in geometric active contours as well.
Parametric active contours synthesize parametric curves within
an image domain and allow them to move toward desired
features, usually edges. Typically, the curves are drawn toward
the edges by potential forces, which are defined to be the
negative gradient of a potential function. Additional forces,
such as pressure forces [10], together with the potential forces
comprise the external forces. There are also internal forces
designed to hold the curve together (elasticity forces) and to
keep it from bending too much (bending forces).
Manuscript received November 1, 1996; revised March 17, 1997. This work
was supported by NSF Presidential Faculty Fellow Award MIP93-50336. The
associate editor coordinating the review of this manuscript and approving it
for publication was Dr. Guillermo Sapiro.
The authors are with the Image Analysis and Communications Laboratory,

与要点#8212;蛇,或积极的轮廓,特别发现东西的界限的
地方以computer vision和印象processing应用软件很宽地
被使用.然而,有关对境界洼坑的初始化不完全的集
合的问题限制了那些的实用程序.主要解决双方的问题,
这个纸出示为了积极的轮廓的的新的外部的力量.这个外部的
力量(我们称呼为倾斜矢量流动(GVF))作为从印象中能获得了的灰色
水平或者2进缘的地图的倾斜矢量的扩散被计算.与在那个不能作
为潜在力函数的否定的倾斜写那个这样的点中基本地传统性
的蛇外部的力量不同,并且,对应的蛇直接自事变份儿的定
式化从席子力的平衡状态被定式化做.是使用的几个二元.
(2-D)例子1个立体的(立体)例子,我们表示GVF有大的c
apture·有效距离,能在境界洼坑里(上)动蛇.
禁书目录Terms#8212;活跃的轮廓模型,变形可能surface模型
,缘的查出,倾斜矢量流动,印象分割,形式的表现,和复苏
弯弯曲曲.
I.是
在能序论SNAKES1,或积极的轮廓从弯曲自己受到来的内部
的力量和被图像数据计算了的外部的力量的影响变动的印
象域中被定义了的弯曲.内部的,并且,外部的力量,象蛇在印象
中遵从东西的界限或者其他必要的特征一样地,被定义.蛇包含缘
的查出的多的应用软件宽广地被使用.
[1],2,3,分割4,5,和模型化做追踪4的运动的形式,6.
今天,文学有2人的普通型活跃的轮廓模型的:parametric的积极的
轮廓1和几何学上积极的轮廓7和#8211;9.这个纸,我们合起为
parametric的积极的轮廓焦点,我们,预想我们的结果又,
以几何学上积极的轮廓持(有)着应用软件.
parametric的积极的轮廓,在印象域中合并parametric曲
线,必要的容许去特征,通常缘.通常,弯曲根据潜在
的力去缘被设置.(力量,象成为潜在力函数的否定的倾斜一样地被
定义).由于潜在的力伴随的压力的力量10等的追加力包括外部的力量.同时
,有象妨碍结合弯曲(弹性力),那个弯曲过多一样地被设计了的内部
的力量(弯曲力).
原稿1996年11月1日被接收了.1997年3月17日,被修订.这个工
作根据NSF Presidential Faculty Fellow Award MIP93-50336被支撑了.调
整这个原稿的批评,为了公布承认了那个的副编辑器是jeru
moSapiro博士.
作者在Image Analysis和Communications研究所在.