基于初级视皮层抑制的轮廓检测方法

轮廓检测是基于形状目标识别任务的关键,然而从自然场景中自动地检测出目标的轮廓是非常困难的,因为背景中存在着大量的无关干扰成分.生理学与解剖学的研究表明初级视皮层中具有方位选择性的神经元的响应受到其周围环境中同方位刺激的抑制,因此方位发生变化的地方受到的抑制程度相对较少,这使得孤立的边缘或区域的边界更为显著.基于此我们提出了一个视觉生理机制的轮廓检测模型,其目的是减少背景纹理的干扰,同时保留感兴趣的对象.针对模拟及真实图像的实验结果表明这种抑制措施有效地抑制了纹理边缘并减少了轮廓自身的破坏,极大地提高了复杂背景中轮廓检测的性能.     

Adaptive pseudo dilation for gestalt edge grouping and contour detection

We consider the problem of detecting object contours in natural images. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. We introduce a new morphological operator, called adaptive pseudo-dilation (APD), which uses context dependent structuring elements in order to identify long curvilinear structure in the edge map. We show that grouping edge pixels as the connected components of the output of APD results in a good agreement with the gestalt law of good continuation. The novelty of this operator is that dilation is limited to the Voronoi cell of each edge pixel. An efficient implementation of APD is presented. The grouping algorithm is then embedded in a multithreshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. The use of different thresholds makes the algorithm much less sensitive to the values of the input parameters. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contours.     

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%.     

基于Hessian矩阵的边缘检测方法

边缘是一幅图像最基本也最重要的特征,边缘检测是机器视觉和图像处理的关键环节和经典课题。本文结合人眼的视觉特性,利用彩色图像自身梯度方向信息,对彩色图像进行多通道边缘检测,得到图像的伪色彩边缘图。对边缘信息增强和去相关后提取亮度分量,用OTSU法二值化,最后利用Hessian矩阵提取边缘图像的中心路径,去除边缘毛糙和冗余的背景纹理,使图像边缘光滑连续。通过实验验证了本文算法的有效性,实验通过与两种经典算子Canny和Sobel以及基于小波的边缘检测方法做比较,结果表明,本文算法能够提高边缘检测的有效性和清晰度。     

基于复合感受野的轮廓检测算法

自然场景自动检测目标轮廓是非常困难的。该文提出了一种基于复合感受野的图像轮廓检测仿生模型。在传统的基于抑制模型的基础上引入去抑制机制,在减少背景纹理干扰的同时,保护较弱的轮廓,从而尽量保持轮廓的完整性。实验结果表明,这种仿生模型可以有效地抑制纹理边缘,减少对轮廓的破坏,提高了自然背景中轮廓检测的性能。     

Edge detection of intestinal parasites in stool microscopic images using multi-scale wavelet transform

Biomedical image processing is experiencing a significant progress with many applications. However, automatic recognition of microscopic pathogens from their images remains a challenge that will allow clinical laboratories to increase both the speed of tests and the reliability of diagnoses. We present an algorithm for edge detection of parasites in microscopic images of stools, using the multi-scale wavelet transform. This method is an evolution of the Canny–Mallat detector which gives the possibility to vary the frequency of the analysis in order to find the outlines of the most significant edges. The various contours obtained are chained across the scales from the coarsest to the finest. Using this algorithm, we were able to correctly represent the contours of the features of parasites found in microscopic images. The results obtained were compared with those produced by classical edge detectors on the same images. It comes out from both subjective and objective quantitative performances evaluation that our detector, better than all others, can clearly mark the outlines of the structures of the pathogen on an image of stools.     

Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.     

基于图像的角度检测系统研究

对基于图像的角度检测系统进行了研究,提出了一种基于图像的多角度交互式检测方法.根据获取的图像特性,交互式地选择检测区域,对检测区域采用中值滤波和Canny算子检测物体的轮廓;提出了一种交互式的边缘点搜索新方法,将经过边缘检测的图像进行轮廓矢量化得到待检物体角度参数,并与给定的设计参数进行比较,判断物体的形状误差.实验结果显示每个角度检测的时间为3～5 s.

Abstract：

. Presented is a image-based machine vision system for visual angle inspection, with the goal of reducing labor cost and ensuring consistent production quality. An integrated edge detection algorithm, which integrates media filter with Canny algorithm, is proposed based on analyzing the properties of the images of the inspected parts and comparing several classical detecting algorithms. Then the angle parameters of edge detection parts are obtained by adopting the contour vectorization algorithm, and a comparison of them with the given ones is carried out to evaluate the quality of the inspecting system. Experimental results show that it spends 3-5 seconds detecting each angle.     

基于MFC的航海视景系统实时碰撞检测技术

针对在航海视景帧流速较大、计算量增加时,会使虚拟场景中人机交互响应时间长,导致航海视景系统实时碰撞检测技术耗时长、准确率低的问题,设计一种基于MFC的航海视景系统实时碰撞检测技术.引入Sobel边缘算子精准提取发生畸变的边缘像素,得到单像素级别的图像边缘,建立基于MFC的航海视景系统框架,通过纹理映射消减航海视景复杂度...     

受噪声污染图象中的基于排序统计的非线性边缘检测器

本文主要研究了如何对受混合噪声污染的图象进行边缘检测,并提出了一种基于排序统计的非线性边缘检测算子。该算子将输入样点集划分成两个具有不同灰度值的子集,通过子集的运算值之差判断边缘是否存在。子集的划分和输出形式的选择是减少噪声对边缘检测器影响的关键。计算机模拟实验表明,基于排序统计的边缘检测算子能较好地同时消除高斯噪声和脉冲噪声对边缘检测器的影响,并且能精细地提取图象的边缘,效果优于其它边缘检测算子。     

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.     

基于机器视觉的精密钻孔法向检测算法研究

目前在自动化检测领域,机器视觉检测技术开始广泛地应用,这种技术的特征表现为高精度、非接触、适用性强,操作方便.在智能制造需求的促进作用下,很多工业生产中开始应用到机器视觉技术.本文对精密钻孔法向检测进行研究,根据检测要求而选择合适的检测方法.首选用Canny边缘检测算法,但是在实际的应用中这种算子会引入很多非目标轮廓线...     

Orientation contrast model for boundary detection

The boundary detection task has been extensively studied in the field of computer vision and pattern recognition. Recently, researchers have formulated this task as supervised or unsupervised learning problems to leverage machine learning methods to improve detection accuracy. However, texture suppression, which is important for boundary detection, is not incorporated in this framework. To address this limitation, and also motivated by psychophysical and neurophysiological findings, we propose an orientation contrast model for boundary detection, which combines machine learning technique and texture suppression in a unified framework. Thus, the model is especially suited for detecting object boundaries surrounded by natural textures. Extensive experiments on several benchmarks demonstrate the improved boundary detection performance of the model. Specifically, its detection accuracy was improved by 10% on the Rug dataset compared with state-of-the-art unsupervised boundary detection algorithm, and its performance is also better or at least comparable with previous supervised boundary detection algorithms.     

一种新型非线性边缘检测算法

提出一种新型的非线性边缘检测技术。首先利用标准的Sobel梯度算子对图像的边缘点进行初选，然后用双同心圆窗口算子在候选边缘点集中寻找真正的边缘。通过与其它成熟的边缘检测技术进行比较。结果显示：该算法获得的边缘图像质量优于传统的Sobel算法；检测出的边缘与Canny和LoG算法可比，计算量却比二者有明显降低。     

Edge detection in noisy images by neuro-fuzzy processing

A novel neuro-fuzzy (NF) operator for edge detection in digital images corrupted by impulse noise is presented. The proposed operator is constructed by combining a desired number of NF subdetectors with a postprocessor. Each NF subdetector in the structure evaluates a different pixel neighborhood relation. Hence, the number of NF subdetectors in the structure may be varied to obtain the desired edge detection performance. Internal parameters of the NF subdetectors are adaptively optimized by training by using simple artificial training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed NF operator outperforms competing edge detectors and offers superior performance in edge detection in digital images corrupted by impulse noise.     

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。     

Edge-enhancement postprocessing using artificial dissipation.

We present a method to enhance, by postprocessing, the performance of gradient-based edge detectors. It improves the performance of the edge detector by adding terms which are similar to the artificial dissipation that appear in the numerical solution of hyperbolic PDEs. This term is added to the output of the edge detector. The edges that are missed or blurred by the edge detector are reconstructed through the addition of the artificial dissipation terms. Edges that are detected correctly by the edge detector are preserved. We present the theory of the artificial dissipation and its improvement of the quality of the detected edges. We demonstrate the performance of the algorithm on diverse images.     

基于最优离散滤波器的边缘检测算法

边缘检测已经成为图像理解和计算机视觉中的一个主要领域,其检测的质量直接影响后期理解的效果。寻找一种对噪声不敏感、定位精确、能有效区分真假边缘的方法,一直是研究的重点。从理论上来说．Canny算子是连续域最优的边缘检测算子,但在离散域中却并非最优,对于数字图像可能存在误差。为此,提出一套完整的离散域边缘检测算法。在最优滤波器理论基础上,推导出离散域的最优平滑算子,抑制了图像的分割错误、噪声和伪边缘的影响。实验表明,该算法能有效实现灰度图像的多尺度边缘检测。具有高的信噪比．更清楚和更符合计算机识别要求,是一个理想的边缘检测方法。     

可变形物体的轮廓的提取

边缘信息一直被认为是计算机视觉的重要特性。因而，边缘的检测与轮廓的提取是图象分析的重要步骤。对于边缘的检测，近年来，人们的研究兴趣更多的转向了局部能量的方法，这是一种基于局部相位的方法，它的特点是通过局部能量的最大值可以同时得到不同类型的边缘。在初步得到物体边缘后，本文应用Ｍ．Ｋａｓｓ１９８７年提出的蛇行模型的方法获取物体轮廓，蛇行模型比较适合于可变形物体的轮廓的提取，如：红血球。     

基于双视通路交互感知的轮廓检测方法

基于生物视觉系统存在双视通路(VP)交互感知的机制,该文提出一种图像轮廓检测的新方法。首先针对皮层下视通路中视觉刺激流经多级不同尺度的感受野,提出一种多尺度轮廓融合的轮廓感知模型;接着基于皮层上视通路的对比度适应机制和方向敏感特性,获取显著性视觉特征;然后模拟双视通路的交互感知机制,分别在V1皮层中,构建一种信息流交互引导的脉冲编码模型,提取显著性轮廓;在上丘(SC)浅层提出一种特征调制的非经典感受野侧抑制模型,实现纹理抑制;最后对双视通路中的轮廓响应结果进行修正融合,得到最终轮廓响应。针对RUG40图像库的测试,整个数据集的最优平均P指标和每张图的最优平均P指标分别为0.51和0.57;针对BSDS500图像库的测试,数据集尺度上最优(ODS)为0.68。结果表明该文方法能有效突显主体轮廓并且抑制纹理背景。通过该文提出的轮廓感知方法,为后续基于视觉机制的图像理解和分析提供了一种新的思路。