基于区域清晰度的纺织纤维图像融合

针对多焦面纺织纤维图像,提出一种基于区域清晰度的图像融合方法。用像素点灰度的模值衡量像素点的清晰度。首先通过对多焦面图像搜索像素点最大模值的方法,确定每个最清晰像素点(即灰度的模值最大)所在的图层号,并保存在图层号矩阵中。再针对图像中的噪声干扰,根据局部区域模值的最大值,确定区域阈值进行去噪处理,并修正图层号矩阵。然后根据图层号矩阵,用对应图层像素点的灰度值合成得到多焦面融合图像。最后对融合方法提出改进措施,以进一步提高图像处理的速度。实验表明所提出的多焦面图像融合方法行之有效。     

自然图象单像素灰度值的统计特性研究

本文针对自然图象中单像素灰度值的统计规律进行实验和分析，发现对数正态分布可以对之进行粗略、简单的建模。此外，本文还给出了判断所给定的数据序列是否为统计自相似数据的有效判断方法，并在此基础上断定自然图象具有明显的自相似特性，从而得到针对单像素灰度值且较对数正态分布更精确的建模。最后，本文还给出了对自相似参数日进行估计的两种有效策略。     

基于形态学重建的粘连物体分割

提出一种基于形态学重建（Morphological Reconstruction）的图像分割方法。该方法先对待分割图像进行预处理,使边界点具有局部极大的灰度值;然后利用灰度形态学重建提取穹顶（Dome）,并根据其特性利用阚值对穹顶进行二值化获得候选边界点集;再利用二值形态学重建确定候选边界点集中的边界点,得到分割边界。实验结果表明,本分割方法所得边界连续性好、假边界少;该方法受噪声和对象内部灰度变化的影响较小,适合用于分割含有粘连对象的图像。     

基于高斯混合模型和Renyi熵的图像分割方法*

采用高斯混合模型（GMM）来估计图像灰度值的空间坐标概率分布,再在二阶Renyi熵的基础上构造目标熵函数,在图像灰度范围内搜索各个灰度级别的坐标集合,使此目标熵函数最大的灰度值作为最佳分割阈值。实验结果表明此方法对图像分割的精度较高,适应性较好,具有较稳定的性能。     

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

This paper presents a novel idea of intracranial segmentation of magnetic resonance (MR) brain image using pixel intensity values by optimum boundary point detection (OBPD) method. The newly proposed (OBPD) method consists of three steps. Firstly, the brain only portion is extracted from the whole MR brain image. The brain only portion mainly contains three regions–gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). We need two boundary points to divide the brain pixels into three regions on the basis of their intensity. Secondly, the optimum boundary points are obtained using the newly proposed hybrid GA–BFO algorithm to compute final cluster centres of FCM method. For a comparison, other soft computing techniques GA, PSO and BFO are also used. Finally, FCM algorithm is executed only once to obtain the membership matrix. The brain image is then segmented using this final membership matrix. The key to our success is that we have proposed a technique where the final cluster centres for FCM are obtained using OBPD method. In addition, reformulated objective function for optimization is used. Initial values of boundary points are constrained to be in a range determined from the brain dataset. The boundary points violating imposed constraints are repaired. This method is validated by using simulated T1-weighted MR brain images from IBSR database with manual segmentation results. Further, we have used MR brain images from the Brainweb database with additional noise levels to validate the robustness of our proposed method. It is observed that our proposed method significantly improves segmentation results as compared to other methods.     

一种新的基于区域增长的ROI分割算法*

为克服现有算法带来边缘定位不精确和人工参与太多等缺点,提出了一种新的基于区域增长的适合医学图像中ROI的分割算法。该算法先利用改进的Canny边缘算子进行边缘粗检测,再利用给出的灰度和纹理等信息进行区域增长,最终得到分割图像。为了更好地进行区域增长,新算法通过对ROI中像素的灰度和纹理进行分析,给出结合点向量运算和灰度判断的增长准则。实验结果表明,该方法能对医学图像中复杂区域或畸形区域进行分割,具有很好的鲁棒性与实用性。     

A class of adaptive directional image smoothing filters

The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters.     

基于区间分布密度的背景初始化方法

为了从有运动物体存在的监控视频中提取初始化背景,提出一种基于区间分布密度的背景建模方法。首先将背景训练序列中所有像素点的灰度值按大小归类,然后通过计算区间分布密度筛选出包含背景信息最为完整的灰度区间,完成背景初始化。考虑到初始背景的提取可能受到部分图像光线突变的影响,在背景建模之前采用最小均方误差理论对背景训练序列进行突变检测。实验结果表明,该方法简单易行,可以排除光线的干扰,具有较好的适应性,能够在较短时间内得到较为逼真的初始背景。     

A Randomized Approach for Patch-based Texture Synthesis using Wavelets

We present a wavelet‐based approach for selecting patches in patch‐based texture synthesis. We randomly select the first block that satisfies a minimum error criterion, computed from the wavelet coefficients (using 1D or 2D wavelets) for the overlapping region. We show that our wavelet‐based approach improves texture synthesis for samples where previous work fails, mainly textures with prominent aligned features. Also, it generates similar quality textures when compared against texture synthesis using feature maps with the advantage that our proposed method uses implicit edge information (since it is embedded in the wavelet coefficients) whereas feature maps rely explicitly on edge features. In previous work, the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel‐to‐pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures.     

基于区域的图像信息隐藏算法

提出一种空间域信息隐藏算法。该算法结合了LSB和MSB的优点，将灰度值划分成一个个等长度区间，在每个区间都嵌入信息。信息嵌入是通过修改灰度值，而灰度值的变化是在区间范围内的，由于区间的范围较小，从而能够保证引起的图像失真是不可觉察的。同时考虑人类视觉系统的相关特性，选择出可用像素位置来嵌入信息，放弃不可用像素位置，进一步提高不可觉察性这一技术指标。实验结果表明，该算法隐藏容量较大，有较好的鲁棒性且易于实现。     

基于区域的图像信息隐藏算法

提出一种空间域信息隐藏算法。该算法结合了LSB和MSB的优点,将灰度值划分成一个个等长度区间,在每个区间都嵌入信息。信息嵌入是通过修改灰度值,而灰度值的变化是在区间范围内的,由于区间的范围较小,从而能够保证引起的图像失真是不可觉察的。同时考虑人类视觉系统的相关特性,选择出可用像素位置来嵌入信息,放弃不可用像素位置,进一步提高不可觉察性这一技术指标。实验结果表明,该算法隐藏容量较大,有较好的鲁棒性且易于实现。     

一种基于Parzen窗法和Renyi熵的图像分割阈值选取新方法*

采用Parzen窗技术来估计图像灰度值的空间概率分布,再用Renyi熵来构造目标函数,最后在图像灰度范围内搜索,使此目标函数最小的灰度值作为最佳全局阈值。实验结果表明,此方法对图像分割的精度非常高,且适应性强。     

图像放大的偏微分方程方法

在分析一些常见的图像放大方法的基础上,根据图像像素值特点及近期偏微分方程在图像处理中的应用,将图像的像素值看作是平面物体的温度;利用偏微分方程理论中的热传导数学模型,提出了基于一种新颖的热传导方程初边值问题的图像放大法;并根据其物理意义,设计相应的差分算法．实验证明,这是一种有效的图像放大方法．     

融合SIFT特征的熵图估计医学图像非刚性配准

配准准确性是医学图像配准算法的一项重要指标,像素灰度是目前图像配准中广泛使用的特征,但是灰度特征来源单一,而且忽略空间信息,在一些情况下容易产生误配。针对这个问题,本文提出一种融合SIFT特征的熵图估计医学图像非刚性配准算法。该算法首先使用基于互信息的刚性配准算法对两幅待配准图像进行粗配;然后,在采样点上提取像素灰度和SIFT高维特征,并在此基础上构造k-最邻近图(kNNG);最后,使用k-最邻近图来估计α互信息(αMI)。实验结果表明:和传统的基于互信息和像素灰度的刚性配准算法,基于熵图估计和单一像素灰度特征的非刚性配准算法相比,本文提出的算法具有更高的配准准确性。     

Peak noise removal by a facet model

A peak noise removal method based on local gray tone statistics from the facet model is introduced. Each pixel in an image is statistically tested to determine whether it belongs to the same gray tone intensity surface as its neighborhood pixels. If its gray tone is outside the 95 per cent confidence interval estimated from the neighborhood gray tones, it is judged as peak noise and its value is replaced by an average of the gray tone values of the neighborhood pixels. In order to estimate the local gray tone statistics, an assumption is made that the neighborhood region is described by a linear or quadratic facet surface model. It is also shown that this method can be successfully applied to scan line noise removal by using a one-dimensional (horizontal or vertical) neighborhood.     

Image segmentation by unifying region and boundary information

A two-stage method of image segmentation based on gray level cooccurrence matrices is described. An analysis of the distributions within a cooccurrence matrix defines an initial pixel classification into both region and interior or boundary designations. Local consistency of pixel classification is then implemented by minimizing the entropy of local information, where region information is expressed via conditional probabilities estimated from the cooccurrence matrices, and boundary information via conditional probabilities which are determined a priori. The method robustly segments an image into homogeneous areas and generates an edge map. The technique extends easily to general edge operators. An example is given for the Canny operator. Applications to synthetic and forward-looking infrared (FLIR) images are given     

Color image segmentation by fixation-based active learning with ELM

The human vision observes an image by making a series of fixations. In fixation, our eyes continually tremble, which is called the microsaccades that may reflect an optimal sampling strategy and spatiotemporal characteristics. Although the decrease in microsaccade magnitude leads to visual fading in our brain that may provide a mechanism to shift fixation. This paper proposes an iterative framework for figure-ground segmentation by sampling-learning via simulating human vision. First, fixation-based sampling is utilized to get a few positive and negative samples. A pixels classifier based on the RGB color could be trained by ELM (extreme learning machine) algorithm, which not only extracts object regions, but also provides a reference boundary of objects. Then, the boundary of object region could be refined by minimizing graph cut. The region of refined object can be re-sampled to provide more accurate samples/pixels involved object and background for the next training. The iteration would convergence when the pixel classifier gets stable segmentation result continually. Based on the ELM algorithm, the proposed method run faster than state-of-the-art method, and can cope with the complexity and uncertainty of the scene. Experimental results demonstrate the learning-based method could reliably segment multiple-color objects from complex scenes.     

Boundary detection by constrained optimization

A statistical framework is used for finding boundaries and for partitioning scenes into homogeneous regions. The model is a joint probability distribution for the array of pixel gray levels and an array of labels. In boundary finding, the labels are binary, zero, or one, representing the absence or presence of boundary elements. In partitioning, the label values are generic: two labels are the same when the corresponding scene locations are considered to belong to the same region. The distribution incorporates a measure of disparity between certain spatial features of block pairs of pixel gray levels, using the Kolmogorov-Smirnov nonparametric measures of difference between the distributions of these features. The number of model parameters is minimized by forbidding label configurations, which are assigned probability zero. The maximum a posteriori estimator of boundary placements and partitionings is examined. The forbidden states introduce constraints into the calculation of these configurations. Stochastic relaxation methods are extended to accommodate constrained optimization.<>     

Efficient computation of adaptive threshold surfaces for image binarization

The problem of binarization of gray level images, acquired under non-uniform illumination is reconsidered. Yanowitz and Bruckstein proposed to use for image binarization an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that high image gradient indicates probable object edges, and there the image values are between the object and the background gray levels. The threshold surface was determined by successive over-relaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multiresolution approximation, the threshold surface is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and often better noise robustness.     

基于在线光度标定的单目直接视觉SLAM

为了减少场景点的光照变化对直接视觉SLAM （simultaneous localization and mapping,SLAM）的影响,在稀疏直接法（direct sparse odometry with loop closure,LDSO）的基础上对其优化,结合光度标定来增强系统的整体性能。直接视觉SLAM,其图像配对的基本假设是建立在灰度一致性之上的,即同一个场景点在多个图像中是以恒定亮度值出现的。为了更好地满足这一假设,采用KLT （Kanade-Lucas-Tracker）来跟踪关键点,利用带光度参数的像素跟踪模型建立优化方程,优化序列的光度参数（曝光时间、晕影和响应函数）,从而实现实时曝光补偿,增强了系统视觉前端的跟踪稳定性。最后,在公开数据集上进行实验。实验结果标明所提方法在部分序列上能有效地提高系统性能。