以数字搜索树表示的二值图象的几何变换

本文介绍以数字搜索树表示的二值图象的一种几何变换算法，文中讨论了四分区与正四边区的基本关系，输出ＤＳＴ结点成立的充分条件，给出了完整的几何变换算法并对算法的复杂性作了分析。     

Efficient embedded wavelet codec based on list structure

A new efficient image codec called embedded wavelet coding based on list structure (EWCBL) is proposed in this paper. By combining the techniques of morphological representation and quadtree partitioning, EWCBL effectively exploits both within-subband clustering and cross-subband similarity of wavelet coefficients. Based on the list structure, a fine fractional bit-plane coding is employed to achieve excellent rate-distortion performance. The patterned morphological dilation is introduced to greatly reduce the redundant test operations. Experimental results show that the proposed EWCBL outperforms state-of-the-art embedded codecs for both lossy and lossless compression. Moreover, the good performance and features of embeddedness and scalability are achieved with relatively low complexity.     

改进的NAMK彩色图像表示算法

利用格雷码可以扩展或保持二值图像块状性的特点,提出一种改进的NAMK彩色图像表示算法NAMKG,给出算法的形式化描述及其存储结构,对其总数据量进行分析。理论分析和实验结果表明,相比NAMK算法和线性四元树算法,NAMKG可以有效减少子模式数,减小存储空间。     

基于四叉树的自适应门限分形图象IFS压缩方法

为了克服四叉树分块ＩＦＳ编码过程中,排列块与区域块的匹配误差不考虑输入图象特点这一缺点,提出了一种新的基于迭代函数系统（ＩＦＳ）进行静止灰度图象压缩的方法。该方法是根据当前排列块的方差来确定它与区域块的匹配门限。经分析证明,这种基于自适应门限的ＩＦＳ编码方法与人类视觉系统的特性基本相符,而且实验也证明,这一自适应门限的编码方法提高了ＩＦＳ图象压缩的适应性。     

Efficient Regular Data Structures and Algorithms for Dilation, Location, and Proximity Problems

In this paper we investigate data structures obtained by a recursive partitioning of the multi- dimensional input domain into regions of equal size. One of the best known examples of such a structure is the quadtree . It is used here as a basis for more complex data structures. We also provide multidimensional versions of the stratified tree by van Emde Boas [vEB]. We show that under the assumption that the input points have limited precision (i.e., are drawn from the integer grid of size u ) these data structures yield efficient solutions to many important problems. In particular, they allow us to achieve O(log log u) time per operation for dynamic approximate nearest neighbor (under insertions and deletions) and exact on-line closest pair (under insertions only) in any constant number of dimensions. They allow O(log log u) point location in a given planar shape or in its expansion (dilation by a ball of a given radius). Finally, we provide a linear time (optimal) algorithm for computing the expansion of a shape represented by a region quadtree. This result shows that the spatial order imposed by this regular data structure is sufficient to optimize the operation of dilation by a ball. Received January 19, 1999; revised November 4, 1999.     

四叉树分形编码算法研究

基于分形理论，提出了邻域四叉树算法，设置门槛值，将定义域块与值域块的误差值与门槛值比较，直到满足误差要求为止，控制图像分割子块的大小。该算法提高了恢复图像质量和压缩比。并用C^ 语言模拟其算法。     

图象处理中边界转换的并行算法及其实现

本文提出了一种把图象中边界转换成区域四分树的并行方法.该方法基于MIMD模型,并在曙光1000上实际运行.整个算法用P个处理器可以在时间O((B×logB)/P)内完成其中B是循环代码长度.该算法可应用于图象处理、计算机图形学、模式识别等领域.     

光电成像干扰图像质量评估

干扰图像质量评估是光电成像干扰的一个难点问题,评估结果应与人类主观视觉感受相匹配.该文分析了光电干扰图像的特点和普通图像质量评估方法的局限性.然后,依据人眼主观视觉特性,利用四叉树数据结构划分方法,将干扰图像划分为边缘部分和非边缘部分.对于边缘部分,构造边缘比较函数评估干扰前后图像边缘变化的情况;对于非边缘部分,引入基...     

Improved quadtree image segmentation approach to region information

Images are full of information and most often, little information is desired for subsequent processing. Hence, region of interest has key importance in image processing. Quadtree image segmentation has been widely used in many image processing applications to locate the region of interest for further processing. There are also variable block-size image coding techniques to effectively reduce the number of transmitted parts. This paper presents quadtree partition technique as a pre-processing step in image processing to determine what part should be more heterogeneous than the others. It also introduces an idea to solve the problem of squared images. Finally, proposed approach is implemented and analysed. The simulation of the Matlab code of the quadtree is represented by all algorithms and the figures. Thus, achieved results are promising in the state of the art.     

动态场景下移动机器人视觉SLAM

为实现动态场景下移动机器人自主定位和建图,解决传统视觉里程计方法跟踪效果差及累积误差问题,提升闭环检测的准确性和鲁棒性,提出融合深度学习的同时定位与地图构建方法。采用四叉树算法均匀化特征分布,解决动态场景特征聚集问题;通过优化的目标检测网络识别场景动态语义信息,剔除动态物体对位姿估计的干扰;充分提取场景空间结构信息,结合点特征和线特征实现位姿跟踪及回环检测,构建全局一致的环境地图。TUM数据集和真实场景实验结果表明：改进方法提升了移动机器人定位和建图的准确性和鲁棒性。