融合两类跟踪框架优点的红外目标跟踪算法

基于粒子滤波的算法模型框架,针对红外的目标成像与背景对比度低、背景复杂等问题,进行提取目标灰度特征,通过对系统概率的密度函数的采样集进行了预测和更新,来逼近系统的后验密度概率,初步确定目标位置,再融合均值漂移算法进行小区域精确搜索,确定目标位置;同时,通过调整均值漂移的算法和函数的带宽,对于红外目标有阻挡情况下的识别能够得到有效处理;通过仿真得到该模型具有算法高效、去除粒子的退化,并且对于有遮挡的红外目标能够进行实时稳定地跟踪。     

基于局部均值和标准差的图像增强算法

针对传统图像局部增强算法,提出一种基于图像局部均值和标准差增强图像细节的算法。采用局部均值和标准差限定的方法,使低对比度和较暗的区域得到相应增强,对高对比度和较亮的区域保持不变。实验结果表明,该算法能够有效地增强低对比度低灰度的区域而保持其他区域不变,为图像的进一步处理提供满意的结果。     

基于空间关系几何约束的无人机景象匹配导航*

针对GPS的信号强度较弱、易受各种电磁干扰,提出一种基于空间关系几何约束的景象匹配导航算法。首先基于空间关系几何约束的多匹配区选择方法,将实时图划分为多个分区;然后采用基于边缘响应的加权Hausdorff距离景象匹配算法对各个匹配区进行定位计算;最后,通过最优配准点坐标估计策略,解算出无人机实时图中心在基准图上的精确定位坐标。多区域景象匹配采用并行计算方法,利用历史导航信息来辅助修正景象匹配导航误差。实验结果表明,该算法可较好地满足无人机对景象匹配导航算法实时性、精确性的性能要求。     

基于改进Fast MBD显著性检测和多特征融合匹配的靶纸区域快速检测算法

为了解决基于机器视觉的自动报靶系统快速、准确定位靶纸区域的问题,通过对靶纸图像的颜色和形状特性分析,提出一种基于改进Fast Minimum Barrier Distance显著性和多特征匹配的靶纸区域快速检测算法。该算法在原始Fast Minimum Barrier Distance显著区域提取算法的基础上,引入局部区域对比度先验和形状先验作为显著性区域提取的补充准则。同时,为了判断提取到的区域是否包含靶纸,再引入多特征匹配算法。首先,分别对图像边界连通先验、局部区域对比度先验和形状先验进行量化,形成距离图、对比度图和形状图,再结合三者分割出显著性目标区域,然后提取分割出的各目标区域的多种特征,并将其进行特征融合,最后利用1-范式将得到的目标特征与模板特征进行匹配,把匹配结果小于阈值的目标视为靶纸。在400张包含靶纸图像数据集上的实验结果显示了该算法的有效性。同时,在华为海思平台上,该算法处理速度能达到30帧/秒,足以证明该算法的实时性。     

基于可变带宽MeanShift的目标跟踪

针对Mean SlliR核函数带宽不能自适应改变的缺陷,提出一种用数据点的函数表示带的Mean Sllift跟踪算法。用数据点的概率密度来描述目标尺寸的变化,用比例系数和概率密度计算出带宽的实时变化,由Mean Shift算法的收敛性实现目标的跟踪。实验表明,该算法能更好地适应跟踪过程中目标尺寸的变化,提高了跟踪的稳定性。     

使用等比包序列的实时有效带宽测量算法

针对网络有效带宽的实时准确测量与测量数据的正确分析问题进行了研究,提出针对网络有效带宽的实时监测方法一使用等比包序列的实时有效带宽测量算法,该算法可以使用较少的网络流量和较短的测量时间得到较准确的测量结果,经过大量的实验数据分析,该实时有效带宽测量算法在不丧失准确性的基础上,能够很好的满足测量的实时性要求.     

一种改进的图像模糊增强算法

针对传统模糊增强算法的缺陷,提出了一种改进的算法。该算法中隶属函数和模糊增强算子均采用连续分段函数,利用OTSU法将图像分为高灰度和低灰度两个区域,对高灰度区域像素进行灰度增强运算,对低灰度区域像素则进行灰度衰减运算。仿真结果表明,该算法具有较强的模糊边缘和细小边缘分辨能力,是一种实用、高效的图像对比度增强算法。     

WiMAX系统的自适应实时带宽调度策略

提出一种自适应的实时轮询业务带宽分配机制,SS依据当前带宽需求和以往的实际分配带宽,提前预测实时业务数据包所需求的确切带宽,文中给出了数学分析模型和仿真,仿真结果表明,与传统的带宽分配机制和Mukul提出的自适应算法相比,该自适应带宽分配算法能更好地改善系统性能,提高吞吐量,减少时延和减少缓冲区需求.     

低对比度火焰图像增强和分割算法研究

针对火焰与背景对比度不明显情况下的低对比度火焰目标提取问题,提出了一种Retinex和CV(Chan-Vese)模型相结合的火焰图像增强和分割算法。首先在YCb Cr颜色空间利用Retinex算法构造彩色双边滤波器,根据分量Cr和Cb的关系设计对比度调节函数调节像素点亮度,以凸显原图像中火焰明亮、鲜艳的颜色特征,细节信息也更清晰;再通过帧间差分法和建立的火焰颜色模型获取疑似火焰区域,根据该区域所得中心坐标点设置CV模型的初始轮廓曲线,进一步分割得到火焰目标。仿真实验表明:所提算法不但能够提取出简单和复杂背景环境下低对比度火焰图像,而且目标边缘不规则信息保留完整,误分率比已有算法有明显降低,表明了算法的先进性和有效性。     

基于WiMAX的实时业务带宽优化调度策略

WiMAX是一项无线城域网(WMAN)技术,支持实时与非实时等多种业务,SS可以向BS动态申请所需求的带宽,由IEEE 802.16协议定义.但协议中没有具体定义实时服务QoS所需要的带宽调度策略.因此,如何高效地分配使用带宽,成为一个亟需解决的问题.本文提出一种自适应的实时轮询业务带宽分配策略,SS依据当前带宽需求和以往的实际分配带宽,提前预测实时业务数据包所需求的确切带宽.文中给出了数学分析模型和仿真.仿真结果表明,与传统的带宽分配机制和其他自适应算法相比,该自适应的带宽分配算法能更好地改善系统性能,提高吞吐量,减少时延和减少缓冲区需求.     

Real-time saliency-aware video abstraction

Existing real-time automatic video abstraction systems rely on local contrast only for identifying perceptually important information and abstract imagery by reducing contrast in low-contrast regions while artificially increasing contrast in higher contrast regions. These methods, however, may fail to accentuate an object against its background for the images with objects of low contrast over background of high contrast. To solve this problem, we propose a progressive abstraction method based on a region-of-interest function derived from an elaborate perception model. Visual contents in perceptually salient regions are emphasized, whereas the background is abstracted appropriately. In addition, the edge-preserving smoothing and line drawing algorithms in this paper are guided by a vector field which describes the flow of salient features of the input image. The whole pipeline can be executed automatically in real time on the GPU, without requiring any user intervention. Several experimental examples are shown to demonstrate the effectiveness of our approach.     

非真实感绘制漫画艺术效果的研究与实现

提出了一种基于非真实感绘制的漫画艺术效果绘制算法。算法首先将输入图像从 RGB 色彩空间转换为LAB 色彩空间,通过双边滤波及色彩量化的方法对输入图像进行处理,获 得连续的、局部区域色彩一致的结果图像,可通过权值等参数对滤波及量化程度进行控制。其次, 采用DOG 滤波器和阈值处理求取输入图像的边缘信息,获得连续的、清晰的边缘。最后将边缘 图像融合到量化结果图像中,获得最终的漫画艺术效果。从最终的漫画艺术绘制效果看出,该算 法简单、易于实现。     

基于人眼视觉感知驱动的井下图像增强算法

为解决传统的对比度增强方法在对井下图像进行处理时不能兼顾压缩动态范围、调整亮度以及增强图像对比度等问题,提出一种基于人眼视觉感知特性的井下图像对比度增强算法。首先根据人眼亮度掩蔽特性对图像进行区域划分,然后基于非线性亮度映射模型,对图像的不同区域进行不同尺度的非线性调整,最后再将亮度调整后的不同区域组合成新的图像。实验结果表明,该方法能有效增强井下低照度图像的对比度,提升图像的视觉效果。     

Novel mean-shift based histogram equalization using textured regions

This paper presents a novel mean-shift based histogram equalization method called the MSHE method. The key insight of the proposed MSHE method is that the basis of histogram equalization could be based on textured regions in an image, while impact of smoother regions should be suppressed. Using a mean-shift based approach, the sets of textured regions in an image are determined by finding regions which have a high density of edge concentration. In addition, a new cost function is presented to balance the image quality and contrast enhancement effect for search termination in the proposed algorithm. Based on three typical test images, experimental results show that our proposed MSHE method is quite competitive with the previous eleven methods, such as the HE, BBHE, DSIHE, POHE, RSWHE, DHE, BPDHE, SRHE, GHE, FHE, and THShap.     

Contrast‐Enhanced Black and White Images

This paper investigates contrast enhancement as an approach to tone reduction, aiming to convert a photograph to black and white. Using a filter‐based approach to strengthen contrast, we avoid making a hard decision about how to assign tones to segmented regions. Our method is inspired by sticks filtering, used to enhance medical images but not previously used in non‐photorealistic rendering. We amplify contrast of pixels along the direction of greatest local difference from the mean, strengthening even weak features if they are most prominent. A final thresholding step converts the contrast‐enhanced image to black and white. Local smoothing and contrast enhancement balances abstraction and structure preservation; the main advantage of our method is its faithful depiction of image detail. Our method can create a set of effects: line drawing, hatching, and black and white, all having superior details to previous black and white methods.     

Human visual perception‐based localized backlight scaling method for high dynamic range LCDs

This paper presents a backlight control algorithm for liquid crystal display devices, which considers the human visual properties that we are usually attracted to the saliencies in a scene. The saliency means regions or objects that have different contrast or color from the surrounding, and thus attract our attention, which can be measured in various ways from each pixel value and its relation with the surrounding ones. Hence, by keeping or boosting the backlight of salient regions while suppressing in others, the quality of salient regions and overall contrast are enhanced. In addition, power can be saved by backlight dimming in non‐salient regions, without loss of overall quality in terms of human visual perception. In this backlight control process, the amount of energy consumption is regulated so that the proposed method consumes less than or equal energy as before, by developing a power management algorithm based on the bit rate control strategies of MPEG2 video encoders. Precisely, the energy consumption in each backlight unit is controlled by a parameter, so that the sum of energies of overall backlight units is kept within a limit.     

Extraction of diffuse objects from images by means of watershed and region merging: example of solar images.

This paper presents a new method for extraction of diffuse objects from images, which was developed for segmentation of solar images obtained from extreme-UV imaging telescope (EIT) experiments of the satellite SOHO mission. As a particular type of objects to be extracted coronal holes in EIT images have been chosen. The method described is based on the use of a watershed algorithm. The result of the watershed segmentation is a partition of the whole domain of the image into a large number of small regions. These regions are then combined in a region merging process. The proposed region merging algorithm iteratively adds the darkest regions and maximizes the average contrast between a current mask and a set of its neighboring regions. We show that the maximization of the average contrast gives segmentation results that are visually acceptable. Furthermore, this approach allows us to conduct the segmentation of EIT images independently of any explicit fine-tuning parameters. The proposed method was extensively tested on EIT images obtained at various times and various levels of solar activity, and we will show that it can be used independently of the local brightness level and the extent of coronal holes.     

A novel algebraic method for kernel-based object tracking

In the present paper, a new tracking method based on kernel tracking is proposed. The proposed method employs a novel algebraic algorithm to get the kernel movement. In contrast to the mean-shift method which uses a weighted kernel to reduce the effect of the background, the algebraic algorithm of the proposed method allows dividing the candidate area into two parts in order to identify the object and background regions. To detect the object and background regions, we propose measuring the similarity of weighted histogram for each part. The experiments show the superiority of the proposed method for the removal of the background. The effect of noise and background clutter is reduced by segmentation of the object which produces the narrow histogram. In conclusion, the ability of the proposed method for tracking in crowded and cluttered scenes is demonstrated.     

基于局部最大方差比的自适应边缘检测算法

本文提出一种新的边缘检测算法。对子图像进行局部直方图计算。由最大方差比例阈值确定方法计算出子图像的最佳阈值。根据局部直方图中波峰数决定是否增强子图像中被最佳阈值划分出的低灰度区域和高灰度区的对比度。然后对已经选择性对比增强的图像进行边缘检测。实验证明图像的区域层次更清楚，提取的边缘细节也更丰富。自适应算法则最大限度地突出了边缘而同时保持了图像中相近灰度级的平滑性和连续性。     

An improved binarization algorithm based on a water flow model for document image with inhomogeneous backgrounds

A segmentation algorithm using a water flow model [Kim et al., Pattern Recognition 35 (2002) 265–277] has already been presented where a document image can be efficiently divided into two regions, characters and background, due to the property of locally adaptive thresholding. However, this method has not decided when to stop the iterative process and required long processing time. Plus, characters on poor contrast backgrounds often fail to be separated successfully. Accordingly, to overcome the above drawbacks to the existing method, the current paper presents an improved approach that includes extraction of regions of interest (ROIs), an automatic stopping criterion, and hierarchical thresholding. Experimental results show that the proposed method can achieve a satisfactory binarization quality, especially for document images with a poor contrast background, and is significantly faster than the existing method.