特征级联在车辆检测中的应用与研究

对现有的采用机器学习算法检测车辆进行研究,分析其存在的不足;表现在特征或者算法单一,对光照等条件变化鲁棒性不够;针对这些问题,提出一种融合LBP特征与HOG特征,并结合Adaboost与SVM的车辆检测算法;借鉴级联的思想,首先采用AdaBoost对训练样本提取LBP特征进行训练,得到的分类器用于初步筛选并将其分类结果作为下一层分类器的输入;然后采用SVM算法对训练样本提取HOG特征进行训练,得到的分类器用于二次筛选上一层分类器的分类结果;实验结果证明Adaboost-SVM相结合的办法检测结果精度高,准确率和召回率均达到95%以上,FPPW与FPPI的值均在5%左右;同时由于算法采用的特征对光照条件具有较强的鲁棒性,因此光照条件的变化对算法的识别结果影响较低;实时性方面,每帧图像的处理时间为75 ms,满足实时性要求。     

枸杞多糖对白血病细胞作用热化学特征研究

用微量热法研究了枸杞多糖对小鼠白血病细胞L1210代谢作用的热化学特征。结果表明，枸杞多糖对L1210细胞代谢有明显的抑制作用，为枸杞在防治肿瘤中的应用提供了可靠的信息。     

融合声纹信息的能量谱图在鸟类识别中的研究

常用的梅尔倒谱系数结合高斯混合模型(MFCC+GMM)方法的鸟鸣声识别技术难适应噪声环境,模型难以收敛,且计算复杂度高。该文提出一种融合声纹信息的能量谱图的鸟类识别方法(VPS-BR),该方法利用鸟类鸣声在能量谱图上所表现的多维差异性,定量识别鸣声声纹特征。通过对分贝能量进行颜色映射得到能量谱图,提取其视觉特征所表达的声学特征,分析归纳得到鸟类特有鸣声模式。在特征提取步骤中,选用识别速度快的局部二值模式、识别鲁棒性高的方向梯度直方图两个参数表征鸟鸣声谱图的边缘声纹;在识别步骤中,用局部二值模式和方向梯度直方图两种特征分别与支持向量机、K最近邻和随机森林3种分类器算法进行两两组合构建识别模型测试。对15种原始带噪鸟类鸣声数据集进行交叉验证,VPS-BR模型的平均识别率比MFCC+GMM组合模型高出11.3%,方向梯度直方图特征与K最近邻分类器的组合模型识别率达90.5%,表现出较好的抗噪性能和识别性能。最后针对样本数据集缺乏问题,使用生成对抗网络进行图像增强,进一步将识别率提升1.48%。     

Affine invariant feature extraction algorithm based on multiscale autoconvolution combining with texture structure analysis

Affine invariant feature extraction has been one of the key issues for object recognition, especially for the images captured under the variable environments. Considering that multiscale autoconvolution feature (MSA), which has the prominent comprehensive performance, is very sensitive to illumination change, a novel algorithm of extracting affine invariant feature is proposed based on the MSA transform combining with texture structure analysis. Firstly, a new MSA feature is extracted from texture structure map of the image which is computed based on local binary pattern theory. And then the original image based MSA and the texture map based MSA are combined to a new feature using generalized canonical correlation analysis, called TFMSA. This new feature represents much more image information than the traditional one and is performed in various object recognition tasks. The experimental results indicate that the new TFMSA not only conquers the defect of the traditional MSA, but also has good adaptability for a certain range of viewing angles, partial occlusion, uniform and non-uniform illumination changes. The recognition accuracy of the new feature is superior to MSA and other improved methods.     

纯品枸杞多糖对小鼠抗疲劳的效果

将纯品枸杞多糖分为５,１０,２０,５０,１００ ｍ ｇ·ｋｇ－ １ ·ｄ－ １等５ 个不同剂量的实验组,灌喂小鼠,观察其对小鼠抗疲劳的效果纯品枸杞多糖能显著地增加小鼠肌糖原、肝糖原储备量;提高运动前及游泳后 ９０ ｍ ｉｎ 及 １５０ｍ ｉｎ Ｌ Ｄ Ｈ 总活力;降低小鼠剧烈运动后血尿素氮增量,加快运动后血尿素氮的恢复速率;以１０ ｍ ｇ·ｋｇ－ １ ·ｄ－ １ 剂量组效果最佳 提示纯品枸杞多糖在提高机体对运动员负荷的适应能力,抵抗疲劳产生和加速疲劳的消除具有十分明显的作用     

Robust automatic facial expression detection method based on sparse representation plus LBP map

Recently the sparse representation based classification (SRC) is successfully used to automatically recognize facial expression, well-known for its ability to solve occlusion and corruption problems. The results of those methods which using different features conjunction with SRC framework show state of the art performance on clean or noised facial expression images. Therefore, the role of feature extraction for SRC framework will greatly affect the success of facial expression recognition (FER). In this paper, we select a new feature which called LBP map. This feature is generated using local binary pattern (LBP) operator. It is not only robust to gray-scale variation, but also extracts sufficient texture information for SRC to deal with FER problem. Then we proposed a new method using the LBP map conjunction with the SRC framework. Firstly, we compared our method with state of the art published work. Then experiments on the Cohn–Kanade database show that the LBP map + SRC can reach the highest accuracy with the lowest time-consuming on clean face images than those methods which use different features such as raw image, Downsample image, Eigenfaces, Laplacianfaces and Gabor conjunction with SRC. We also experiment the LBP map + SRC to recognize face image with partial occluded and corrupted, the result shows that this method is more robust to occlusion and corruption than existing methods based on SRC framework.     

基于LBP和多层次结构的异质手背静脉身份识别

针对多源异质的手背静脉异质图像的识别研究,提出了基于LBP和多层次结构的识别算法;首先对图像做适当的预处理,然后将LBP特征提取算法编码的手背静脉纹理特征图像作为多层次结构的输入,通过多层次结构的逐层由具体到抽象的特征提取,得到的特征具有更大的鲁棒性;最后该算法在多源异质的手背静脉图像库得到的识别率比传统的算法识别率高,达到96.57%;进一步表明该算法能够较好地解决由于多源异质问题对手背静脉识别所造成的识别率低的影响。     

一种基于纹理特征的红外成像目标跟踪方法

提出了一种基于LBP（Local Binary Pattern)纹理特征的红外成像目标跟踪方法,将LBP纹理特征集成到了核跟踪方法中.根据目标各区域对背景的区分能力不同,提出了目标各区域置信度的评价方法,用基于区域置信度及空间距离核加权的LBP特征概率密度函数,构造了目标及候选目标的特征模型.通过相似性度量,利用均值漂移方法实现了基于纹理特征的红外成像目标跟踪.实验结果验证了该算法在红外成像目标跟踪中较基于灰度的均值漂移跟踪算法更为鲁棒.