基于区域的二叉树图象编码新方法

本文提出了一种基于区域的图象编码新技术，它根据图象区域灰度分布特点，以灰度误差最小平方和为准则，采用四向递归二分法逐渐图象表面划分为若干凸多边形，使之逼近原始图象。软件模拟实验表明，当头肩灰 度图象压缩比超过２０：１时，重重建图象主以质量仍然较好。文中介绍了四向递归二分法的基本算法和一叉树图象编码方法，同时给若干实验结果。     

基于直线边缘识别的图象区域定位算法

介绍了一种基于直线边缘识别的图象区域定位算法。算法主要分为两个部分,其一为基于样本线对边缘点进行搜索的边缘检测算法,其二为利用层次逼近的霍夫变换对所得到的边缘图象中车牌区域的边界直线段进行提取以对其定位的算法。算法充分利用了原始图象的灰度分布特征,同时在霍夫变换中采取了深度优先的递归搜索策略,取得了较好的实现结果。     

复杂背景下的人脸检测

提出了一种亲折复杂背景下的人脸检测方法。首先,根据图象的灰度直方图,把图象分割成若干子图象区域,然后分别在这些子图象区域中搜索可能的人脸,进行全局特征－奇异值特征的提取,最后根据特征匹配检验的结果作出决策。     

线性四元树到边界链码的一种新转换算法

在图象编码过程中，往往需要将四元树转换为边界链码，针对这一问题，本文提出了一种线性四元树到边界链码的新转换算法，与以往一些算法相比，该算法实现起来非常简单而又不失通用性，在该算法中，四元树上所有大于最小黑结点的黑结点被递归划分为最小黑结点大小，随后采用一种堆栈的方式实现结点的邻接寻找，最后通过求得组成区域边界的有向线段从而得到区域的边界链码。     

基于图象内容的链码检索方法

针对数字化图象检索问题,提出了一种基于图象内容的检索方法。这种检索方法是基于图象的灰度特征值,按照一定的分割方法,将图象区域化,并获取该区域的灰度特征描述值;然后把各个区域的灰度描述值按着规定的方向连接起来,生成一个用于检索图象的链码。实验结果表明这种基于图象内容的检索方法,较方便和准确地达到了图象检索之目的。     

基于等势线特征的图象特征域抽取研究

图象识别检索的关键步骤是提取出稳定特征,引申了物理学中等势线的概念到图象特征区域中,并提出了两种方法：图象重排法和基于极小值灰度点的区域生长法来抽取这类图象特征区域。理论分析表明图象重排法在算法时空间复杂度上相对较优于基于极小值灰度点的区域生长法。实验表明,图象重排法可取得较高的运行处理速度,部分抽取特征域甚至具备语义特征。     

一种航空照片中的小目标识别方法

提出了一种能够从具有复杂背景的航空照片中识别出小目标的模式识别方法。此方法先对图象进行二值化，其中二值化所需的门限值是通过对图象的灰度直方图进行形态滤波并结合小波变换而得到的，然后从二值图象中提取出各非零区域，接下来对原始图象中与这些区域位置对应的灰度区域进行第二次门限化，最后对各小二值图象块进行平移、尺度和旋转不变性变换，求出每个变换图象块的ｎ阶几何矩做为其不变性特征输入ＢＰ网完成各目标的识别。     

一种提取图象细节边缘的新方法

边缘是图象的基本特征，边缘信息是进行图象分析和识别的重要属性，但由于常用的边缘提取方法在提取边缘的同时，容易丢失图象的细节边缘信息，为此提出了一种基于灰度形态学和图象分解技术相结合的图象细节边缘提取方法，该方法首先运用灰度形态学方法检测出包含图象细节的边缘图象并去除部分背景和噪声，然后进行区域分解，再通过对不同的区域选取不同的阈值来保证边缘提取的完整性．仿真结果表明，与传统方法相比，该方法能有效地提取一般图象的细节边缘，甚至能提取被噪声污染图象的边缘．     

自动图象边缘检测方法的研究

本文提出了一种新的自动图象边缘检测方法,该方法使用新的隶属函数将图象转化为等效的图象模糊特征平面,在此基础上进行模糊增强,然后再转换为空域图象,顾后进行边缘提取,模糊增强提高了低灰度区域和高硬功率和高灰度区域之间的对比度,从而使提取的边缘效果更好,最后本文对具有多峰直方图分布图象的模糊边缘检测问题进行了
了推广。     

模M多维差分变换和数据压缩

本文提出了模M多维差分变换的图象表示和压缩方法.变换是在对N维图象数组的2~N元分割所产生的2~N元树上进行,其效果是利用了图象的N维数据相关性,使得图象灰度值分布得到了显著的改善,它既适合于二值图象,也适合于多值图象,对于线图、文本以及具有块状或体状的图象或数组尤为有效,变换算法的复杂度非常简单,并且是递归可执行的。     

Replicated partitioning for undirected hypergraphs

Hypergraph partitioning (HP) and replication are diverse but powerful tools that are traditionally applied separately to minimize the costs of parallel and sequential systems that access related data or process related tasks. When combined together, these two techniques have the potential of achieving significant improvements in performance of many applications. In this study, we provide an approach involving a tool that simultaneously performs replication and partitioning of the vertices of an undirected hypergraph whose vertices represent data and nets represent task dependencies among these data. In this approach, we propose an iterative-improvement-based replicated bipartitioning heuristic, which is capable of move, replication, and unreplication of vertices. In order to utilize our replicated bipartitioning heuristic in a recursive bipartitioning framework, we also propose appropriate cut-net removal, cut-net splitting, and pin selection algorithms to correctly encapsulate the two most commonly used cutsize metrics. We embed our replicated bipartitioning scheme into the state-of-the-art multilevel HP tool PaToH to provide an effective and efficient replicated HP tool, rpPaToH. The performance of the techniques proposed and the tools developed is tested over the undirected hypergraphs that model the communication costs of parallel query processing in information retrieval systems. Our experimental analysis indicates that the proposed technique provides significant improvements in the quality of the partitions, especially under low replication ratios.     

Clustering spatial networks for aggregate query processing: A hypergraph approach

In spatial networks, clustering adjacent data to disk pages is highly likely to reduce the number of disk page accesses made by the aggregate network operations during query processing. For this purpose, different techniques based on the clustering graph model are proposed in the literature. In this work, we show that the state-of-the-art clustering graph model is not able to correctly capture the disk access costs of aggregate network operations. Moreover, we propose a novel clustering hypergraph model that correctly captures the disk access costs of these operations. The proposed model aims to minimize the total number of disk page accesses in aggregate network operations. Based on this model, we further propose two adaptive recursive bipartitioning schemes to reduce the number of allocated disk pages while trying to minimize the number of disk page accesses. We evaluate our clustering hypergraph model and recursive bipartitioning schemes on a wide range of road network datasets. The results of the conducted experiments show that the proposed model is quite effective in reducing the number of disk accesses incurred by the network operations.     

An in-depth study of graph partitioning measures for perceptual organization

In recent years, one of the effective engines for perceptual organization of low-level image features is based on the partitioning of a graph representation that captures Gestalt inspired local structures, such as similarity, proximity, continuity, parallelism, and perpendicularity, over the low-level image features. Mainly motivated by computational efficiency considerations, this graph partitioning process is usually implemented as a recursive bipartitioning process, where, at each step, the graph is broken into two parts based on a partitioning measure. We focus on three such measures, namely, the minimum, average, and normalized cuts. The minimum cut partition seeks to minimize the total link weights cut. The average cut measure is proportional to the total link weight cut, normalized by the sizes of the partitions. The normalized cut measure is normalized by the product of the total connectivity (valencies) of the nodes in each partition. We provide theoretical and empirical insight into the nature of the three partitioning measures in terms of the underlying image statistics. In particular, we consider for what kinds of image statistics would optimizing a measure, irrespective of the particular algorithm used, result in correct partitioning. Are the quality of the groups significantly different for each cut measure? Are there classes of images for which grouping by partitioning does not work well? Also, can the recursive bipartitioning strategy separate out groups corresponding to K objects from each other? In the analysis, we draw from probability theory and the rich body of work on stochastic ordering of random variables. Our major conclusion is that optimization of none of the three measures is guaranteed to result in the correct partitioning of K objects, in the strict stochastic order sense, for all image statistics.     

Image segmentation with ratio cut

This paper proposes a new cost function, cut ratio, for segmenting images using graph-based methods. The cut ratio is defined as the ratio of the corresponding sums of two different weights of edges along the cut boundary and models the mean affinity between the segments separated by the boundary per unit boundary length. This new cost function allows the image perimeter to be segmented, guarantees that the segments produced by bipartitioning are connected, and does not introduce a size, shape, smoothness, or boundary-length bias. The latter allows it to produce segmentations where boundaries are aligned with image edges. Furthermore, the cut-ratio cost function allows efficient iterated region-based segmentation as well as pixel-based segmentation. These properties may be useful for some image-segmentation applications. While the problem of finding a minimum ratio cut in an arbitrary graph is NP-hard, one can find a minimum ratio cut in the connected planar graphs that arise during image segmentation in polynomial time. While the cut ratio, alone, is not sufficient as a baseline method for image segmentation, it forms a good basis for an extended method of image segmentation when combined with a small number of standard techniques. We present an implemented algorithm for finding a minimum ratio cut, prove its correctness, discuss its application to image segmentation, and present the results of segmenting a number of medical and natural images using our techniques.     

基于神经网络的纹理和灰度信息融合方法

文章从信息融合的角度出发,利用神经网络的方法将纹理和灰度信息有机地融合起来,设计并实现了一种基于子区域的区域增长分割算法.并将该方法应用到医学图像的分割问题中,取得了较好的效果.实验表明,该方法针对一类图像能够得到较好的结果.     

Gray level difference-based transition region extraction and thresholding

Thresholding method based on transition region is a newly developed approach for image segmentation in recent years. In this paper, a novel transition region extraction and thresholding method based on gray level difference is proposed by analyzing properties of transition region. The gray level difference can effectively represent the essence of transition region. Hence, the proposed algorithm can accurately extract transition region of an image and get ideal segmentation result. The proposed algorithm was compared with two classic transition region-based methods on a variety of synthetic and real world images, and the experimental results show the effectiveness and efficiency of the algorithm.     

小波变换及在图象处理中的小波特性分析

小波变换是近几年发展起来的集数学、科学计算及信号处理于一体的新理论，其核心是多分辨率分析。本文全面综述了小波变换作为多分辨率图象处理新工具的发展过程、小波变换的定义、信号／图象的多分辨率小波分解及图象多分辨率分解所表现的多尺度特性、局部化分析特性及方向选择性等。     

Recursive pyramids and their use for image coding

Recursive pyramidal image representation in image coding algorithms through the use of the pyramid structure, and recursive two-dimensional scannings are introduced. Two approaches to the decorrelation of the picture elements in such a data structure are considered. Properties of different two-dimensional scannings as well as statistical properties of the recursive pyramid and their use for the development of image coding algorithms are shown. Experimental results demonstrating the achieved compression ratio and distortions are provided.     

Recursive operations in image algebra

The image algebra, developed by Ritter et al. at the University of Florida, is an algebraic structure for image processing. The three commonly used high-level image-template operations provided by the image algebra are the generalized convolution , the additive maximum or generalized lattice convolution {ie23-1}, and the multiplicative maximum {ie23-2}. These are used to realize various nonrecursive image transformations, including morphological transformations. Along with nonrecursive transformations, a class of recursive transformations, such as IIR filters, adaptive dithering, and predictive coding, are also widely used in signal and image processing. In this paper the notions of recursive templates and recursive template operations are introduced; these allow the image algebra to express a set of linear and nonlinear recursive transformations. Algebraic properties of these recursive operations are given, providing a mathematical basis for recursive template composition and decomposition. Finally, applications of recursive template operations in specifying some image processing algorithms are presented.     

A region-based H.263+ codec and its rate control for low VBR video

This paper presents a region-based video codec, which is compatible with the H.263+ standard, and its associated rate control algorithm for low variable-bit-rate (VBR) video. The proposed region-based coding scheme is a hybrid method that incorporates traditional block DCT coding as well as object-based coding. To achieve this, we adopt H.263+ as the platform, and develop a fast macroblock-based segmentation method to implement the new region-based codec. The associated rate control solution includes rate control in three levels: encoding frame selection, frame-layer rate control and macroblock-layer rate control. The goal is to improve human visual perceptual quality at low bit rates. The efficiency of the proposed rate control algorithm applied to the region-based H.263+ codec is demonstrated via several typical test sequences.