Texture analysis of poly-adenylated mRNA staining following global brain ischemia and reperfusion

Texture analysis provides a means to quantify complex changes in microscope images. We previously showed that cytoplasmic poly-adenylated mRNAs form mRNA granules in post-ischemic neurons and that these granules correlated with protein synthesis inhibition and hence cell death. Here we utilized the texture analysis software MaZda to quantify mRNA granules in photomicrographs of the pyramidal cell layer of rat hippocampal region CA3 around 1 h of reperfusion after 10 min of normothermic global cerebral ischemia. At 1 h reperfusion, we observed variations in the texture of mRNA granules amongst samples that were readily quantified by texture analysis. Individual sample variation was consistent with the interpretation that animal-to-animal variations in mRNA granules reflected the time-course of mRNA granule formation. We also used texture analysis to quantify the effect of cycloheximide, given either before or after brain ischemia, on mRNA granules. If administered before ischemia, cycloheximide inhibited mRNA granule formation, but if administered after ischemia did not prevent mRNA granulation, indicating mRNA granule formation is dependent on dissociation of polysomes. We conclude that texture analysis is an effective means for quantifying the complex morphological changes induced in neurons by brain ischemia and reperfusion.     

Supervised polarimetric SAR classification method based on Fisher linear discriminant

A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to land covers to be classified.Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be combined together for classification use,without consideration of the dimension difference of each feature parameter and the joint probability density function of those parameters.Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94.33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.     

结构健康监测中传感器优化设置方法研究

首先探讨了3种基于数据信息最大化准则的传统传感器优化布置方法,即有效独立法、有效独立驱动点-残差法及运动能法.其中前2种基于Fisher信息阵最大化,而第3种基于运动能最大化.然后运用结构振动模态和三样条插值模态之间的均方差最小和抗噪性能最好,这两种比较准则来确定最优的传感器设置方法.数值分析表明:这3种传感器优化布置法都是有效的,而有效独立-驱动点残差法的效果最好.     

一种实用的Gabor滤波器组参数设置方法

Gabor滤波器的参数设置是Gabor特征提取过程中的一个重要问题。文中针对现有方法(实验法和优化法)的优缺点，提出一种实用的Gaobr 滤波器组参数设置方法，该方法根据Gabor特征与滤波器方向参数之间具有垂直关系的性质，人为确定方向参数。在每个特定方向，以滤波器的频率带宽参数为指导，基于Fisher准则，进行最佳单Gabor滤波器的设计。确定的滤波器组在性能上是接近最优的，同时算法实现简单，具有较好的实用性和数据相关的特点。在车辆检测和车型识别两类实验中验证了文中方法的有效性和可行性。     

一种基于Gabor小波的制导精度预测方法

在无人驾驶飞行器的自主制导系统中，一般采用下视景像匹配制导技术对飞行器的具体位置进行精确自主定位。为了提高制导精度，减少误匹配，飞行前需要在基准图上进行航迹规划，就是在飞行区域中选择出一些匹配概率肓的匹配区。在选择过程中，匹配概率的计算尤为重要，用相关匹配定义计算虽然准确但耗时巨大。文章提出了一种基于Gabor小波特征的匹配概率预测方法。实验表明，该方法能较快地预测出匹配概率，精度也较高。     

一种融合PCA 和KFDA 的人脸识别方法

提出一种融合PCA和KFDA的人脸识别方法，即在进行非线性映射之前，首先利用经典的主分量分析(C—PCA)进行降维，然后执行KFDA．为进一步降低整个算法的计算时问，又提出一种I—PCA KFDA方法，它直接基于图像矩阵的主分量分析(I—PCA)．ORL标准人脸库的试验结果表明，与现有的核Fisher鉴别分析方法相比，两种方法可将特征抽取的速度分别提高3倍和7倍，其识别精度没有丝毫的降低．     

Genetic algorithms combined with discriminant analysis for key variable identification

Many trouble-shooting problems in process industries are related to key variable identification for classifications. The contribution charts, based on principal component analysis (PCA), can be applied for this purpose. Genetic algorithms (GAs) have been proposed recently for many applications including variable selection for multivariate calibration, molecular modeling, regression analysis, model identification, curve fitting, and classification. In this paper, GAs are incorporated with Fisher discriminant analysis (FDA) for key variable identification. GAs are used as an optimization tool to determine variables that maximize the FDA classification success rate for two given data sets. GA/FDA is a proposed solution for the variable selection problem in discriminant analysis. The Tennessee Eastman process (TEP) simulator was used to generate the data sets to evaluate the correctness of the key variable selection using GA/FDA, and the T² and Q statistic contribution charts. GA/FDA correctly identifies the key variables for the TEP case studies that were tested. For one case study where the correlation changes in two data sets, the contribution charts incorrectly suggest that the operating conditions are similar. On the other hand, GA/FDA not only determines that the operating conditions are different, but also identifies the key variables for the change. For another case study where many key variables are responsible for the changes in the two data sets, the contribution charts only identifies a fraction of the key variables, while GA/FDA correctly identifies all of the key variables. GA/FDA is a promising technique for key variable identification, as is evidenced in successful applications at The Dow Chemical Company.     

基于Fisher准则函数的二维阈值图像分割算法

文章针对图像分割中小目标物体识别困难和抗噪声性能差的问题，引入模式识别中的Fisher评价函数作为图像分割准则函数，基于Fisher准则函数提出二维阈值分割新技术，结合二维直方图抗噪声性能强和Fisher准则函数受目标和背景比例影响小的特点，对图像进行分割。实验结果表明，方法具有较好的识别能力和较强的抗噪声性能。     

Automatic localization of craniofacial landmarks for assisted cephalometry

In this paper we propose a system for localization of cephalometric landmarks. The process of localization is carried out in two steps: deriving a smaller expectation window for each landmark using a trained neuro-fuzzy system (NFS) then applying a template-matching algorithm to pin point the exact location of the landmark. Four points are located on each image using edge detection. The four points are used to extract more features such as distances, shifts and rotation angles of the skull. Limited numbers of representative groups that will be used for training are selected based on k-means clustering. The most effective features are selected based on a Fisher discriminant for each feature set. Using fuzzy linguistics if-then rules, membership degree is assigned to each of the selected features and fed to the FNS. The FNS is trained, utilizing gradient descent, to learn the relation between the sizes, rotations and translations of landmarks and their locations. The data for training is obtained manually from one image from each cluster. Images whose features are located closer to the center of their cluster are used for extracting data for the training set. The expected locations on target images can then be predicted using the trained FNS. For each landmark a parametric template space is constructed from a set of templates extracted from several images based on the clarity of the landmark in that image. The template is matched to the search windows to find the exact location of the landmark. Decomposition of landmark shapes is used to desensitize the algorithm to size differences. The system is trained to locate 20 landmarks on a database of 565 images. Preliminary results show a recognition rate of more than 90%.     

Use of a choice-based survey approach to characterise fishing behaviour in a scallop fishery

The predictability of fisher behaviour is an area of considerable uncertainty in fisheries management models. Fisher-derived data could underpin a better understanding, and more realistic predictions of fishing behaviour. Face to face interviews and a choice-based survey were conducted with scallop fishers to collect foraging parameters that could inform a model of fishing behaviour, and to better understand patch choice behaviour. Importantly, we validated survey data against vessel monitoring system and logbook data where possible, demonstrating a good level of accuracy. Environmental parameters central to patch choice were determined (e.g. wave height, distance to port), and three strategies of patch choice behaviour were identified, termed quantity maximiser, quality maximiser, and efficient fisher. Individuals' VMS and logbook data further confirmed and explained these behavioural patterns. This approach provided reliable, highly relevant data for the parameterisation of a fisheries behavioural model, which could lead to more robust and realistic predictive fisheries models.