一种有效的二值图像细化算法

基于对图像中目标边缘的分析，提出了一种行之有效的二值图像细化算法。实验证明，采用该算法细化后所得的图像骨架不仅对称性好，而且为单像素宽，并能够完整地保持原图像的连通性。     

Binary Image Thinning Using Autowaves Generated by PCNN

This paper proposes a novel binary image thinning algorithm by using the autowaves generated by Pulse Coupled Neural Network (PCNN). Once the autowaves travelling in different directions meet, the PCNN delivers the thinning results. Four meeting conditions are given for autowaves meeting. If a neuron satisfies one of the four conditions, the pixel corresponding to this neuron belongs to the thinning result. Moreover, the specification of the PCNNs parameters is given, which makes the implementation of the proposed thinning algorithm easy. Experimental results show that the proposed algorithm is efficient in extracting the skeleton of images (such as Chinese characters, alphabet letters, numbers, fingerprints, etc.). Finally, a rate called “R _MSkel” is given to evaluate the performance of different thinning algorithms, and comparisons show that the proposed algorithm has higher “R _MSkel” and costs less time.     

一种保形的快速图象形态细化算法

骨架是一种重要的图象目标几何特征,对不同形状的图象目标,如何快速地获得其非畸变骨架,是进行图象目标的形状分析、特征提取、模式识别等应用的前提。基于数字形态学的形态细化是获取图象目标骨架的有效细化方法之一。它采用具有一定形状的预定义结构元素,对图象进行形态薄化运算,仅需进行移位和逻辑运算就能完成。但是形态细化一般使用序贯细化算法,在每次细化迭代过程中,只能采用单一的结构元素对目标进行薄化,因此存在关     

综合加权骨架段特征的形状图像检索

提出了一种基于综合骨架段特征的形状图像检索方法。首先提取图像中目标的骨架,然后根据骨架上的特征点(端点、分叉点)把整个骨架分割成若干骨架段,用矩特征来描述各段骨架。最后通过MSHP(Most Similar Highest Priority)原理对两幅图像中的骨架段对进行相似性度量,从而得到整幅图像之间的距离。实验表明,该方法较传统的基于整个骨架特征的匹配方法能得到更好的检索结果。     

Image Compression by Layered Quantum Neural Networks

We have proposed the qubit neuron model as a new scheme in non-standard computing. Identification problems have been investigated on neural networks constructed by this qubit neuron model, and we have found high processing abilities of them. In this paper, we evaluate the performance of the quantum neural network of large size in image compression problems to estimate the utility for the practical applications comparing with the conventional network consists of formal neuron model.     

一种快速的扫描铅笔稿图细化算法

二维动画计算机输助制作系统中,在对扫描铅笔稿图进行矢量化后,可以大大提高描线上色的效率,并且能够提取线条和闭包,对便于自动上色和中间帧生成等更高级功能的实现。扫描铅笔搞图的细化是矢量化的第一步,在比较了大量现存的图象细化算法后,提出了一种非迭代线跟踪细化算法,该算法效率较高,只需对图象进行一遍扫描加两遍轮廓跟踪,就能较好地满足二维动画计算机辅助制作系统对细化效率的要求。该文还对算法的时间复杂性进行     

基于骨架接合节点特征的形状识别与分类

介绍了一种利用形状的骨架信息进行形状识别和分类的方法。利用从形状中提取的骨架接合节点信息,将形状相似性问题转化为骨架接合节点距离的计算。采用神经网络的方法,根据输入形状的骨架接合节点距离判断出形状所属类别。该方法可以很好地处理非刚性物体形状识别问题,与现有方法相比,具有更好的形状识别效果。     

一种基于Delaunay三角化的手写体文字细化方法

为了对手写体文字进行快速准确的识别，基于Delaunay三角化方法，提出了一种新的文字图象细化算法，该算法首先通过对文字图象边界的近似多边形进行Delaunay三角化，同时把其分成一系列保持拓扑关系的三角形，然后根据三角形的类型生成不同的局部骨架；最后连接生成整文字图象的骨架，由于该算法充分利用了图象的全局和局部信息，因此具有速度快，效果好等优点。     

The Applicability of Neural Networks to Non-linear Image Processing

In this paper, the applicability of neural networks to non-linear image processing problems is studied. As an example, the Kuwahara filtering for edge-preserving smoothing was chosen. This filter is interesting due to its non-linear nature and natural modularity. A number of modular networks were constructed and trained, incorporating prior knowledge in various degrees and their performance was compared to standard feed-forward neural networks (MLPs). Based on results obtained in these experiments, it is shown that several key factors influence neural network behaviour in this kind of task. First, it is demonstrated that the mean squared error criterion used in neural network training is not representative for the problem. To be able to discern performance differences better, a new error measure for edge-preserving smoothing operations is proposed. Secondly, using this measure, it is shown that modular networks perform better than standard feed-forward networks. The latter type often ends up in linear approximations to the filter. Finally, inspection of the modular networks shows that, although analysis is difficult due to their non-linearity, one can draw some conclusions regarding the effect of design and training choices. The main conclusion is that neural networks can be applied to non-linear image processing problems, provided that careful attention is paid to network architecture, training set sampling and parameter choice. Only if prior knowledge is used in constructing the networks and sampling the datasets can one expect to obtain a well performing neural network filter. Receiveed: 28 May 1998?,Received in revised form: 22 September 1998?Accepted: 16 October 1998     

基于神经网络的一种图象细化方法

提出了一种新的细化算法,它利用神经网络的分类辨识特性,对边缘点进行分类识别。实验结果表明这种方法具有速度快,省空间的优点。     

基于Matlab神经网络的图像识别

神经网络是信息科学、脑科学、神经心理学等诸多学科近年来共同关注的研究热点.由于神经网络具有良好的抽象分类特性,使其成为解决图像识别相关问题的有效工具.在简述图像识别过程的基础上重点讨论利用BP神经网络对图像进行识别,用Matlab完成对神经网络的训练和测试,获得满意的结果.     

Thinning algorithms based on quadtree and octree representations

Thinning is a critical pre-processing step to obtain skeletons for pattern analysis. Quadtree and octree are hierarchical data representations in image processing and computer graphics. In this paper, we present new 2-D area-based and 3-D surface-based thinning algorithms for directly converting quadtree and octree representations to skeletons. The computational complexity of our thinning algorithm for a 2-D or a 3-D image with each length N is respectively O(N²) or O(N³), which is more efficient than the existing algorithms of O(N³) or O(N⁴). Furthermore, our thinning algorithms can lessen boundary noise spurs and are suited for parallel implementation.     

Eye Tracking in Image Sequences by Competitive Neural Networks

An automatic algorithm is presented for the eye tracking within face image sequences. It is based on the property of local Gabor filters to efficiently determine the location of eyes in face images. The proposed algorithm is implemented by a competitive neural network, that locates and tracks the eyes in a reliable manner as shown by experimental results.     

视觉导航的旋转不变性图象获取和神经元网络识别方法研究

本文报告了将旋转不变性图象和神经网络用于移动机器人室外道路识别和方向判别的研究结果。本方法采用图象分析技术与自适应的神经元网络相结合；用全方位图象传感器提取图象样本，然后从图象中提取旋转不变性的特征，使用分类网络对道路类型分类，并用其结果选择不同的方向估计神经元网络；分析了神经元网络在选择不同形式的输入数据和不同数目的隐含层结点时的性能。文章给出了在室外环境下拍摄的真实图象实验的结果。     

Neural Networks for Autonomous Path-Following with an Omnidirectional Image Sensor

This paper presents a path-following system implemented with two different types of neural networks, that enables an autonomous mobile robot to return along a previously learned path in a dynamic environment. The path-following is based on data provided by an omnidirectional conical visual system, derived from the COPIS sensor, but with different optical reflective properties. The system uses optical and software processing and a neural network to learn the path, described as a sequence of selected points. In the navigation phase it drives the robot along this learned path. Interesting results have been achieved using low cost equipment. Test and results are presented.     

形态学细化算法在印制电路板(PCB)定位中的应用

在印制电路板(PCB)的自动检测过程中，已经注意到准确的定位在生产过程中对自动安置电子器件是非常重要的。骨架是图像几何形态的一种重要拓扑描述，利用骨架表示原始图像，在保持图像拓扑特征的前提下，减少了冗余信息。本文通过介绍一种通用的细化算法，进而引入数学形态学的改进细化算法，利用提取的骨架对电路板进行定位。经比较，运用形态学方法的细化结果有着较好的定位效果。     

基于多层前馈神经网络的图像压缩的仿真研究

神经网络研究的再度兴起及其在图像编码中的应用，开辟了图像压缩的新途径。该文论述了多层前馈网络用于图像压缩的网络模型、原理、算法及关键技术，并通过大量的仿真实验说明了在BP网络图像压缩中，算法、激活函数和压缩率等参数的选择是至关重要的，它们与收敛时间以及重建图像的压缩性能息息相关。通过对实验结果的详细分析得知，BP网络图像压缩必须综合考虑压缩率、失真率和训练时间等因素，在学习规则、激活函数、隐层神经元数和压缩性能之间进行权衡，以满足实际应用。     

基于连接段数细化算法的脱机手写体数字识别

本文描述了已有的字符细化算法的思想及其缺陷，在分析细化形变根源的基础上给出了一种新的快速细化算法，并将该算法应用到脱机手写体数字识别过程申。该算法不会产生毛刺和伪分支点。细化後字符骨架形变小，而且速度也比较快，从而提高了手写体数字的识别速度。     

Thinning algorithms: A critique and a new methodology

This paper surveys the applications of thinning in image processing, and examines the difficulties that confront existing thinning algorithms. A fundamental problem is that an algorithm may not be guaranteed to operate successfully on all possible images: in particular, it may not discriminate properly between ‘noise spurs’ and valid limbs, and the skeleton produced may not accurately reflect the shape of the object under scrutiny. Analysis of the situation results in a new, systematic approach to thinning, leading to a family of algorithms able to achieve guaranteed standards of skeleton precision. One algorithm of this family is described in detail.

“There is still no definitely good method for thinning” - Nagao⁽²⁸⁾     

一种新的指纹图象细化算法

针对ＯＰＴＡ细化算法存在的不足,即细化不全,速度较慢的缺点进行分析和研究,提出了一种新的细化算法,该算法速度快,细化全,细化后的指纹骨架在纹线中心线,且光滑无毛刺。