一种快速彩色图像中复杂背景下人脸检测方法

本文提出了一种快速的彩色图像中复杂背景下人脸检测方法.该方法首先利用进化Agent计算对彩色图像中与人的肤色相似的像素进行聚类和区域分割.然后利用小波分解对每一个侯选区域进行人脸特征分析,如所检测到的区域特征分布相似于某一预先定义的人脸模型,则确认该区域代表人脸.实验结果表明,该方法不仅速度快、效率高,而且正确检测率高.     

基于肤色分割与Adaboost融合鲁棒人脸检测方法

人脸检测广泛应用到人脸识别、数字视频处理、安全访问控制、视觉监测、基于内容的检索等领域.比较众多人脸检测算法,文章提出了一种改进的基于Adaboost算法的人脸检测算法.该算法的核心是肤色分割结合基于Adaboost算法的人脸检测.首先,对彩色图像进行肤色分割,通过肤色区域的大小和长宽比等规则去除部分类肤色区域,得到可疑的人脸区域.其次,基于Adaboost算法的灰度特征得到最终的人脸.通过大量彩色图像的实验,证实了该方法的准确性和鲁棒性.     

Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction

This paper proposes an unsupervised image segmentation approach aimed at salient object extraction. Starting from an over-segmentation result of a color image, region merging is performed using a novel dissimilarity measure considering the impact of color difference, area factor and adjacency degree, and a binary partition tree (BPT) is generated to record the whole merging sequence. Then based on a systematic analysis of the evaluated BPT, an appropriate subset of nodes is selected from the BPT to represent a meaningful segmentation result with a small number of segmented regions. Experimental results demonstrate that the proposed approach can obtain a better segmentation performance from the perspective of salient object extraction.     

Binary partition tree analysis based on region evolution and its application to tree simplification.

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation.     

基于均值漂移滤波及谱分类的海面舰船红外目标分割

提出了一种有效的海面舰船红外目标分割方法.利用均值漂移方法的不连续保持性滤波特性,滤除海面的强杂波干扰,同时又不损失舰船目标的信息.根据滤波得到的区域构建区域邻接图,采用基于最大最小SST图划分算法对区域邻接图的节点进行划分.划分结果最终将图像分为天空背景、海面背景以及舰船目标3个部分.由于采用区域节点来表征图像,较之采用原始图像象素节点表示,其节点个数大大减少,从而使算法的计算效率得到很大提高.实验结果也表明提出两步算法具有优越的性能,能够在海面强杂波干扰的情况下有效提取舰船红外目标.     

Efficient morphological segmentation using a new connected operator

An efficient segmentation algorithm for region-based coding is proposed. A new connected operator based on both size and contrast is presented and an efficient region merging algorithm which preserves high quality in terms of the number of regions is proposed. Experimental results show that the proposed algorithm achieves a good performance in terms of the image quality and the number of regions     

一种基于形态学的红外目标分割方法

研究自然背景下红外图像中目标分割的问题,提出了一种基于形态学的红外目标分割方法．该方法先利用形态学滤波,对红外目标图像中的噪声和微小的干扰区域进行滤除,接着根据提出的计算图像形态梯度的多尺度算法提取图像梯度,而后用改进的分水岭算法对图像进行分割,最后针对过分割问题提出了一种新的区域融合方法．实验结果表明,该算法能较好地解决红外图像中的目标分割问题．     

基于边缘流与区域归并的彩色图像分割方法

为了克服边缘流各向异性扩散（EAD,edgeflow-dfiven anistropic diffusion）过分割和最小生成树（MST,minimum spanning tree）方法计算复杂度高的缺点,提出了结合边缘流与区域归并的彩色图像分割方法。首先利用EAD方法对图像进行预分割,然后利用MST方法依据全局最优化...     

边缘惩罚层次区域合并SAR图像分割算法

该文利用方向边缘强度信息,建立一种新的边缘惩罚SAR图像分割模型,提出一种最小化该模型的层次区域合并算法。利用多方向比例边缘检测算子(MDRED)提取边缘强度信息,并结合分水岭变换获得高质量的初始过分割结果。利用多边形近似区域边缘,提取边缘的方向,将方向边缘强度映射(OESM)融入边缘惩罚中,获得惩罚强度与边缘强度呈反比的惩罚项。逐渐增大边缘惩罚项的强度,获得由图像特征驱动的层次区域合并算法。利用区域邻接图(RAG)表示图像分割,提高区域合并的速度。实验表明:该文方法与其它方法相比在性能和效率上都有优势,获得更好的分割结果。     

Reconstruction of coarse depth estimates using hierarchical image partitions

This paper presents a novel depth map enhancement method which takes as inputs a single view and an associated coarse depth estimate. The depth estimate is reconstructed according to the region boundaries proposed within a set of image partitions of the associated view. Image partitions are determined through a color-based region merging procedure and organized within a hierarchy. Novel depth estimates are computed according to plane-fitting procedures using the proposed regions as support and selected coarse depth estimates. The use of partition hierarchies provides an efficient mechanism for evaluating plane-fitting results over various scales and, in this way, reduce the dependency of the results on the quality of a single color-based segmentation. Experimental results on publicly available test sequences illustrate the potential of the algorithm in significantly improving low quality depth estimates.     

A SAR Image Segment Method Using Gray Level Reduction Based on Graph in ICA Space

This paper proposes a new SAR image seg-mentation method based on graph and gray level reduction in Independent component analysis (ICA) space. Firstly, according to the grayscale information of SAR image, ef-fective use of gray level reduction for initial segmentation can group the pixels with same or similar values to the same homogeneous region, which can address the problem of over-segmentation. Secondly, the features of regions are extracted in ICA space, and then the similarity degree can be calculated by Euclidean distance. The initial regions are merged in fully connected graph based on minimum spanning tree in ICA space. The process of region merging is divided into two phases; the first phrase is merging the different regions with the largest similarity degree, the sec-ond will focus on updating the fully connected graph for iteration. Finally, experimental and comparative results on synthetic and real SAR images verify the efficiency of the proposed algorithm.     

一种新的基于网格编码和区域合并的SAR图像快速分割算法

该文建立一种新的基于八邻域网格编码的SAR图像分割模型,并用区域合并技术实现了模型的快速求解。利用多方向比例边缘检测算子提取SAR图像的比例边缘强度映射(RESM),提出一种新的阈值处理方法抑制RESM均质区域内部的极小值,进而减少了对阈值处理后的RESM进行分水岭变换获得的初始分割的区域个数。递归地合并相邻区域来求取分割模型的次优解。利用区域邻接图(RAG)及其最近邻图(NNG)特性来加速区域合并过程。引入精确度(P)和召回率(R)来评价分割算法的边缘定位精度。与常用方法相比,该文方法具有高的边缘定位精度和低的时间复杂度。     

基于分水岭变换和区域融合的建筑物彩色图像分割

提出了一种彩色建筑物图像分割方法,该算法采用区域面积控制预处理技术和分水岭算法生成初始分割,结合区域对称性等多种属性作为区域相似性的度量,由区域融合得到最终分割结果.同时又提出了一种基于融合代价函数值直方图分布的终止阈值自动设定法.实验证明,本文提出的算法可生成有意义的建筑物各区域,并能有效地降低噪声对分割的影响,对三维重建等研究领域的建筑物图像分割有着十分重要的意义.     

Extensive partition operators, gray-level connected operators, andregion merging/classification segmentation algorithms: theoretical links

The relation between morphological gray-level connected operators and segmentation algorithms based on region merging/classification strategies has been pointed out several times in the literature. However, to the best of our knowledge, the formal relation between them has not been established. This paper presents the link between the two domains based on the observation that both connected operators and segmentation algorithms share a key mechanism: they simultaneously operate on images and on partitions, and therefore they can be described as operations on a joint image-partition model. As a result, we analyze both segmentation algorithms and connected operators by defining operators on complete product lattices, that explicitly model gray-level and partition attributes. In the first place, starting with a complete lattice of partitions, we initially define the concept of the segmentation model as a mapping in a product lattice, whose elements are three-tuples consisting of a partition, an image that models the partition attributes, and an image that represents the gray-level model associated to the segmentation. Then, assuming a conditional ordering relation, we show that any region merging/classification segmentation algorithm can be defined as an extensive operator in such a complete product lattice, in the second place, we proposed a very similar lattice-based extended representation of gray-level functions in the context of connected operators, that highlights the mathematical analogy with segmentation algorithms, but in which the ordering relation is different. We use this framework to show that every region merging/classification segmentation algorithm indeed corresponds to a connected operator. While this result provides an explanation to previous work in the area, it also opens possibilities for further analysis in the two domains. From this perspective, we additionally study some theoretical properties of a general region merging segmentation algorithm.     

Segmentation of video sequences for partition tree generation

In this paper we propose a segmentation structure to generate a partition tree with multiple partition proposals. These multiple partitions are obtained for every image in a hierarchical way, from the coarsest one to the finest one. Time evolution of the regions is defined through a projection step which relates the previous coded partition with the current partition tree. Finer partitions than the projected one are defined segmenting the projected partition using texture criteria, while coarser partitions are obtained merging regions with motion criteria. Morphological tools are used both in the projection and the re-segmentation steps.     

一种边界引导的多尺度高分辨率遥感图像分割方法

针对高分辨率遥感图像分割过程中区域合并复杂性问题,提出了一种基于边界引导的多尺度遥感图像分割算法.一方面应用SUSAN算子提取高分辨率遥感图像中地物的边界用于限制区域增长过程;另一方面进行两阶段增长,首先应用基于图的分割算法进行基于像素的区域生长,然后进行考虑对象特征信息的区域合并.对宜昌城区某处融合后的QuickBird彩色图像进行了实验,并分别与无边界引导分割以及eCognition平台下图像分割效果进行了对比.结果表明,该方法可以有效抑制传统图像分割算法在低对比度区的区域融合问题,突破了分割尺度参数不能在全图取得合理分割的局限性.     

A tree-structured Markov random field model for Bayesian image segmentation

We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.     

Binary partition tree as an efficient representation for imageprocessing, segmentation, and information retrieval

This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.     

A fully unsupervised color textured image segmentation algorithm using weighted mean histograms features

A new integrated feature distribution-based color textured image segmentation algorithm has been proposed. Two novel histogram-based inherent color texture feature extraction methods have been presented. From the histogram features, mean color texture histogram is calculated. Instead of concatenating the feature channels, a multichannel nonparametric Bayesean clustering is employed for primary segmentation. A region homogeneity-based merging algorithm is used for final segmentation. The proposed feature extraction techniques inherently combine color texture features rather then explicitly extracting it. Use of nonparametric Bayesean clustering makes the segmentation framework fully unsupervised where no a priori knowledge about the number of color texture regions is required. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments using color textured and natural images. The experimental results reveal that superior segmentation results can be obtained through the proposed unsupervised segmentation framework.