基于最小生成树的加权中值滤波算法

根据图像纹理分布特点,提出一种基于最小生成树的加权中值滤波算法。依据最小生成树计算像素点的相关度,对像素点进行初次分类。对初次分类中不能确定性质的像素点,采用模糊理论进行二次分类。根据像素点的分类结果,保持像素点的原有灰度值或采用不同的滤波方法进行滤波处理。仿真实验结果表明,在去除噪声和图像细节保持方面,该算法优于其他中值滤波算法。     

基于混合颜色空间的免疫组化图像分割研究

根据大鼠肝脏免疫组化彩色图像的特征,提出一种阳性产物分割算法。在任意3幅图像中选择一批样本点,通过对这些样本点的分析找到阳性像素在RGB和HSI空间中的特征,根据这些特征提取阳性产物所在区域,采用中值滤波方法去除噪声。实验结果表明,该算法约有82.9%的分割结果与病理医生目视鉴别结果一致,比现有算法更适合大鼠肝脏免疫组化彩色图像的分割。     

Single-allocation ordered median hub location problems

The discrete ordered median location model is a powerful tool in modeling classic and alternative location problems that have been applied with success to a large variety of discrete location problems. Nevertheless, although hub location models have been analyzed from the sum, maximum and coverage point of views, as far as we know, they have never been considered under an alternative unifying point of view. In this paper we consider new formulations, based on the ordered median objective function, for hub location problems with new distribution patterns induced by the different users’ roles within the supply chain network. This approach introduces some penalty factors associated with the position of an allocation cost with respect to the sorted sequence of these costs. First we present basic formulations for this problem, and then develop stronger formulations by exploiting properties of the model. The performance of all these formulations is compared by means of a computational analysis.     

改进的二值图像复原方法研究

为了解决传统基于GBB方法的正则化二值图像复原算法放大背景噪声的问题,提出了一种基于GBB方法的改进的正则化二值图像复原算法。根据提出的算法进行实验,定性的对两种算法的图像复原结果进行了比较。同时,利用ISNR选取最佳参数,且在不同程度噪声的影响下定量的评价了两种复原方法的结果。实验结果表明,该方法对于模糊二值图像的复原效果有显著提升,有效降低了噪声对图像复原的影响。     

综放采煤自动化煤岩识别传感器的研究

在实现综放采煤自动化的过程中,能准确检测区分出煤和岩石是实现无人作业工作面的难点.在高速数字信号处理器(DSP)平台上建立CCD数字处理系统,通过DSP对图像的离散脉冲噪声进行中值滤波,并实时计算出每一张图像的灰度直方图,提取煤炭和岩石的特征区别,从而实现对综放工作面的控制.通过Matlab仿真和实际测试结果表明:检测...     

Nearest neighbor editing aided by unlabeled data

This paper proposes a novel method for nearest neighbor editing. Nearest neighbor editing aims to increase the classifier’s generalization ability by removing noisy instances from the training set. Traditionally nearest neighbor editing edits (removes/retains) each instance by the voting of the instances in the training set (labeled instances). However, motivated by semi-supervised learning, we propose a novel editing methodology which edits each training instance by the voting of all the available instances (both labeled and unlabeled instances). We expect that the editing performance could be boosted by appropriately using unlabeled data. Our idea relies on the fact that in many applications, in addition to the training instances, many unlabeled instances are also available since they do not need human annotation effort. Three popular data editing methods, including edited nearest neighbor, repeated edited nearest neighbor and All k-NN are adopted to verify our idea. They are tested on a set of UCI data sets. Experimental results indicate that all the three editing methods can achieve improved performance with the aid of unlabeled data. Moreover, the improvement is more remarkable when the ratio of training data to unlabeled data is small.     

Median MSD-based method for face recognition

An improved maximum scatter difference (MSD) criterion is proposed in this paper. A weakness of existing MSD model is that the class mean vector in the expressions of within-class scatter matrix and between-class scatter matrix is estimated by class sample average. Under the non-ideal conditions such as variations of expression, illumination, pose, and so on, there will be some outliers in the sample set, so the class sample average is not sufficient to provide an accurate estimate of the class mean using a few of given samples. As a result, the recognition performance of traditional MSD model will decrease. To address this problem, also to render MSD model rather robust, within-class median vector rather than within-class mean vector is used in the original MSD method. The results of experiments conducted on CAS-PEAL and FERET face database indicate the effectiveness of the proposed approach.     

基于STM32的手持式测氧仪设计

针对国内传统手持式测氧仪精度低、功耗高的问题,提出了采用嵌入式微控制器STM32F103RBT6作为控制芯片的手持式测氧仪.该仪器通过控制元器件的开断来降低待机功耗,根据所测浓度人工切换系统放大倍数,通过键盘设置时间和报警值等相关参数.算法上采用中位值平均滤波算法进行软件滤波,结合传统两点标定法和多点标定法的优缺点,采用多点标定法进行氧浓度标定,采用分段斜率拟合算法计算氧浓度值.经实验测试证明设计的手持测氧仪具有高精度和高稳定性.     

基于误差补偿的复杂场景下背景建模方法

在基于子空间学习的背景建模方法中,利用背景信息对前景误差进行补偿有助于建立准确的背景模型.然而,当动态背景（摇曳的树枝、波动的水面等）和复杂前景等干扰因素存在时,补偿过程的准确性和稳定性会受到一定的影响.针对这些问题,本文提出了一种基于误差补偿的增量子空间背景建模方法.该方法可以实现复杂场景下的背景建模.首先,本文在误差补偿的过程中考虑了前景的空间连续性约束,在补偿前景信息的同时减少了动态背景的干扰,提高了背景建模的准确性.其次,本文将误差估计过程归结为一个凸优化问题,并根据不同的应用场合设计了相应的精确求解算法和快速求解方法.再次,本文设计了一种基于Alpha通道的误差补偿策略,提高了算法对复杂前景的抗干扰能力.最后,本文构建了不依赖于子空间模型的背景模板,减少了由前景信息反馈引起的背景更新失效,提高了算法的鲁棒性.多项对比实验表明,本文算法在干扰因素存在的情况下仍然可以实现对背景的准确建模,表现出较强的抗扰性和鲁棒性.     

基于多结构形态学的港口船舶检测方法

针对港口监控摄像机与船舶目标距离远,船舶目标成像小,海面噪声干扰大等特点,采用一种基于多结构元素形态学滤波的背景抑制算法.根据船舶的几何特征,采用多组结构元素的加权形态学滤波,将船舶目标与海面背景分离,然后利用基于邻域的自适应快速中值滤波滤除脉冲噪声,最后根据海面杂波在帧间运动不连续且面积较小等特点,利用连通域计算建立船舶的形态特征模型,来排除海面杂波对船舶检测的干扰.实验结果证明,该检测方法在实际港口获取的视频中,可以有效的抑制背景噪声和海面杂波,快速的检测出船舶目标.