Contour detection based on nonclassical receptive field inhibition

We propose a biologically motivated method, called nonclassical receptive field (non-CRF) inhibition (more generally, surround inhibition or suppression), to improve contour detection in machine vision. Non-CRF inhibition is exhibited by 80% of the orientation-selective neurons in the primary visual cortex of monkeys and has been shown to influence human visual perception as well. Essentially, the response of an edge detector at a certain point is suppressed by the responses of the operator in the region outside the supported area. We combine classical edge detection with isotropic and anisotropic inhibition, both of which have counterparts in biology. We also use a biologically motivated method (the Gabor energy operator) for edge detection. The resulting operator responds strongly to isolated lines, edges, and contours, but exhibits weak or no response to edges that are part of texture. We use natural images with associated ground truth contour maps to assess the performance of the proposed operator for detecting contours while suppressing texture edges. Our method enhances contour detection in cluttered visual scenes more effectively than classical edge detectors used in machine vision (Canny edge detector). Therefore, the proposed operator is more useful for contour-based object recognition tasks, such as shape comparison, than traditional edge detectors, which do not distinguish between contour and texture edges. Traditional edge detection algorithms can, however, also be extended with surround suppression. This study contributes also to the understanding of inhibitory mechanisms in biology.     

Integrating local distribution information with level set for boundary extraction

This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model.     

基于边缘多通道梯度模型的多运动目标检测

针对视频序列运动目标检测易受环境噪声干扰、提取目标轮廓困难的问题,提出了一种基于边缘多通道梯度改进模型的多运动目标检测算法。首先,利用Canny算子获取视频序列中目标的边缘信息,并根据人类视觉色彩的恒常特性,对目标边缘建立时间、空间、颜色多通道梯度模型;然后,利用该模型获取目标边缘像素点的运动状态描述信息,实现背景边缘和运动物体边缘的分离;最后,将间断边缘像素点与其邻域点的运动状态相关联,以连接目标间断边缘,实现运动目标轮廓的提取,并将连接后的轮廓进行形态学处理以分割出目标。实验结果表明,与同类型算法相比,本算法在运动目标检测中具有的实时性、准确性和鲁棒性更好。     

Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector

We propose a method for extracting the left ventricular (LV) contours from left ventriculograms by means of a neural edge detector (NED) in order to extract the contours which accord with those traced by a cardiologist. The NED is a supervised edge detector based on a modified multilayer neural network, and is trained by use of a modified back-propagation algorithm. The NED can acquire the function of a desired edge detector through training with a set of input images and the desired edges obtained from the contours traced by a cardiologist. The proposed contour-extraction method consists of 1) detection of "subjective edges" by use of the NED; 2) extraction of rough contours by use of low-pass filtering and edge enhancement; and 3) a contour-tracing method based on the contour candidates synthesized from the edges detected by the NED and the rough contours. Through experiments, it was shown that the proposed method was able to extract the contours in agreement with those traced by an experienced cardiologist, i.e., we achieved an average contour error of 6.2% for left ventriculograms at end-diastole and an average difference between the ejection fractions obtained from the manually traced contours and those obtained from the computer-extracted contours of 4.1%.     

A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations

In this paper, we develop a novel region-based approach to snakes designed to optimally separate the values of certain image statistics over a known number of region types. Multiple sets of contours deform according to a coupled set of curve evolution equations derived from a single global cost functional. The resulting active contour model, in contrast to many other edge and region based models, is fully global in that the evolution of each curve depends at all times upon every pixel in the image and is directly coupled to the evolution of every other curve regardless of their mutual proximity. As such evolving contours enjoy a very wide “field of view,” endowing the algorithm with a robustness to initial contour placement above and beyond the significant improvement exhibited by other region based snakes over earlier edge based snakes.     

Segmentation and tracking in echocardiographic sequences: activecontours guided by optical flow estimates

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability     

基于Corvis ST医学影像的角膜轮廓提取算法

目前已有的轮廓提取算法在提取可视化角膜生物力学分析仪(corneal visualization scheimpflug technology, Corvis ST)影像的角膜轮廓中,由于角膜边缘的局部图像灰度分布相近这一特点,提取出的角膜轮廓普遍不完整或者提取的角膜轮廓边缘出现细小突出。这会使得角膜轮廓的完整性遭到破坏,提取到的角膜轮廓与实际的角膜原始图像严重不符。本文针对Corvis ST采集的角膜图像的轮廓提取问题,基于最大类间方差法(OTSU)算法设计一种高效的图像处理方法。首先,将角膜图像进行除杂、灰度化以及图像降噪等处理,达到减少图像计算量和降低数字图像噪声干扰的目的;其次,基于OTSU算法对图像进行分割,并在此基础上加入数学形态学运算,达到平滑图像边界和填充细小“孔洞”的目的;最后,采用Canny边缘检测算法提取图像中的角膜轮廓,达成提取出高完整性角膜轮廓的目的。在相同的图像数据集上,与最新的纽扣轮廓瑕疵检测系统中设计的轮廓提取算法(B-OTSU algorithm)进行了对比实验。实验结果表明,从轮廓完整性以及准确性的角度,应用本文方法提取的角膜轮廓明显优于最新的纽扣轮...     

局部标准差滤波在图像处理中的应用

为了在抑制噪声的同时尽可能保留图像边缘等重要信息,提出了一种局域标准差滤波器设计方法.该方法通过计算滤波器窗口中每一像素点的局域标准差,然后通过确定最小局域标准差对应像素点,使之作为滤波器的输出.仿真实验表明,提出的局域标准差滤波器具有很强的噪声抑制和边缘保持能力.     

基于变形模板技术的目标轮廓跟踪算法

为了适应目标旋转、尺度、场景光照等变化,利用B样条曲线表达目标轮廓,结合变形模板技术对运动目标的轮廓进行跟踪.在跟踪过程中,沿着模板曲线的法线方向检测目标轮廓,提高了检测效率.根据同一曲线上相邻点间的相关性,对检测所得的轮廓点集的坐标序列进行中值滤波,有效降低噪声干扰.将检测到的轮廓点集在形状空间匹配,使目标轮廓的形变限制在一定范围之内,有效抑制噪声和背景边缘特征的干扰.仿真试验表明,该算法能够有效得到目标轮廓,且具有较好的实时性.     

基于边缘特征的自动运动对象分割

为了准确分割出视频场景中的运动对象,该文提出了一种基于边缘特征的运动对象分割及跟踪算法。首先对相邻帧进行自适应变化检测,得到相邻帧二值差分图像。结合当前帧Canny算子检测的边缘图像,获得运动对象的初始边缘模板。其次对运动对象的运动分为快变和慢变两部分进行跟踪并更新运动对象的边缘模板。最后对运动对象的边缘模板进行数学形态学处理得到运动对象的外轮廓,使用梯度向量流场作为外力的改进活动轮廓算法收缩获得运动对象准确的闭合轮廓曲线。该算法对运动对象的整体运动和局部形变都有很强的鲁棒性, 能够得到运动对象准确的轮廓,并且对复杂背景有很好的适应性。     

Subpixel edge localization and the interpolation of still images

In this paper, we present a nonlinear interpolation scheme for still image resolution enhancement. The algorithm is based on a source model emphasizing the visual integrity of detected edges and incorporates a novel edge fitting operator that has been developed for this application. A small neighborhood about each pixel in the low-resolution image is first mapped to a best-fit continuous space step edge. The bilevel approximation serves as a local template on which the higher resolution sampling grid can then be superimposed (where disputed values in regions of local window overlap are averaged to smooth errors). The result is an image of increased resolution with noticeably sharper edges and, in all tried cases, lower mean-squared reconstruction error than that produced by linear techniques.     

基于颜色与线段的图像轮廓提取算法

传统二维图像轮廓识别算法通常是在图像中找到边缘,根据设定的条件将边缘像素组合起来形成轮廓。针对边缘检测算法通常需要对不同的图像设定不同的阈值,对不同类型的图像很难找出统一的阈值的问题,文中提出了一种同时利用图像中颜色与线段信息的彩色图像轮廓提取算法,算法采用自顶向下的颜色空间融合和自底向上的线段检测的方法,在初步获取边缘信息之后,综合利用检测结果生成目标的轮廓。算法的优点在于不需要进行阈值调整,实验表明:该方法可以有效地提取彩色图像中的目标轮廓。     

一种鲁棒的非均匀灰度图像分割算法

针对非均匀灰度图像分割困难及分割效率低下的问题,该文提出了一种基于活动轮廓模型的高效图像分割算法。不同于传统水平集方法中仅用单一信息定义的能量泛函,该算法结合图像的边缘信息和区域统计信息定义了一个新的能量泛函。边缘信息的利用便于演化轮廓线快速精确地定位至物体边缘;区域统计信息由局部统计信息和全局统计信息构成,一方面,局部统计信息的利用能够有效处理图像的灰度分布不均匀现象,另一方面,全局统计信息的利用避免了轮廓线陷入局部极小值。最后,在轮廓线演化过程中,通过高斯卷积核实现快速规则化,避免了传统模型计算代价高昂的重新初始化或规则化。合成图像和真实图像的实验结果表明,该文算法不仅能够快速有效分割灰度分布不均匀的弱边缘物体,而且对于多灰阶复杂结构物体也能够精确分割;同时,该算法对噪声和初始轮廓线具有较好的鲁棒性。     

A curve evolution approach to object-based tomographic reconstruction

We develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. Instead of reconstructing pixel values on a fixed rectangular grid, we then find a reconstruction by jointly estimating these unknown contours and texture coefficients of the object and background. By designing a new "tomographic flow", the resulting problem is recast into a curve evolution problem and an efficient algorithm based on level set techniques is developed. The performance of the curve evolution method is demonstrated using examples with noisy limited-view Radon transformed data and noisy ground-penetrating radar data. The reconstruction results and computational cost are compared with those of conventional, pixel-based regularization methods. The results indicate that the curve evolution methods achieve improved shape reconstruction and have potential computation and memory advantages over conventional regularized inversion methods.     

Improvement on dynamic elastic interpolation technique for reconstructing 3-D objects from serial cross sections [biomedical application

An improved method for automatically reconstructing a three-dimensional object from serial cross sections is presented. The method improves the dynamic elastic contour interpolation technique. There are two major improvements: (1) in the case of branching that involves concave contours, instead of pairwise interpolation between the start contour and each goal contour, the goal image is considered globally and local constraints are imposed on the forces exerting upon the vertices; and (2) it takes the continuity of high-order derivatives into consideration and incorporates the schemes of spline theory, quadratic-variation-based surface interpolation, and finite-element-based multilevel surface interpolation to create smoother surfaces of the reconstructed object. Based on the output from the preliminary processing, two alternatives, a quadratic-variation-based surface interpolation algorithm and a finite-element-based multilevel surface interpolation algorithm, can be adopted to obtain the final surface representation     

Thresholding in edge detection: a statistical approach

Many edge detectors are available in image processing literature where the choices of input parameters are to be made by the user. Most of the time, such choices are made on an ad-hoc basis. In this article, an edge detector is proposed where thresholding is performed using statistical principles. Local standardization of thresholds for each individual pixel (local thresholding), which depends upon the statistical variability of the gradient vector at that pixel, is done. Such a standardized statistic based on the gradient vector at each pixel is used to determine the eligibility of the pixel to be an edge pixel. The results obtained from the proposed method are found to be comparable to those from many well-known edge detectors. However, the values of the input parameters providing the appreciable results in the proposed detector are found to be more stable than other edge detectors and possess statistical interpretation.     

An interactive authoring system for video object segmentation and annotation

An interactive authoring system is proposed for semi-automatic video object (VO) segmentation and annotation. This system features a new contour interpolation algorithm, which enables the user to define the contour of a VO on multiple frames while the computer interpolates the missing contours of this object on every frame automatically. Typical active contour (snake) model is adapted and the contour interpolation problem is decomposed into a two-directional contour tracking problem and a merging problem. In addition, new user interaction models are designed for the user to interact with the computer. Experiments indicate that this system offers a good balance between algorithm complexity and user interaction efficiency.     

SAR图像直线提取

针对合成孔径雷达(SAR)图像中相干斑的统计特性,设计了对应的直线提取算法.首先组合运用Canny算子和Ratio算子得到边缘点及其边缘方向,然后根据边缘方向一致性原理得到初始直线图;最后,通过高层编组方法连接由于噪声引起的直线缺损.本文提出的边缘检测方法克服了边缘方向量化带来的直线断裂,并且具有恒虚警的特性.高层编组过程通过对初始直线图的分析确定可能扩展的直线区域,之后在原始图像中进行统计证实,得到完整的直线图.直线提取算法在X波段机载SAR图像上进行了试验,得到了满意的效果.提取的直线图可以用于遥感图像矢量化、自动目标识别等方面.