医学图像精细化分割方法研究

指出了深度学习方法在医学图像分割中取得较大的进展,但医学图像处理的复杂性使得全自动分割方法难以取得较好的分割效果。在卷积网络分割的基础上,结合应用场景使用适当的后处理手段来提升图像的分割效果是一种比较有临床意义的研究方法。主要研究了基于概率图的全连接条件随机场模型和基于用户交互的深度编辑网络,并分析总结了这两种方法的实现原理和各自优势,并对未来的研究工作进行了一些展望。     

基于卷积神经网络的小麦产量预估方法

小麦产量是评估农业生产力的重要指标之一,针对小麦产量人工预估困难,提出将卷积神经网络运用于小麦产量预估,为农业生产力的预估提供参考,指导农业生产管理决策。利用无人机分别在河南省新乡、漯河两地进行图片采集,并以之构建麦穗数据集,分为正样本(麦穗)和负样本(叶子和背景)。针对小麦常规的生理形态和生长环境,设计卷积神经网络识别模型,以图像金字塔构建多尺度滑动窗口,以非极大值抑制(NMS)去除重叠率较高的目标框,实现对单位面积内麦穗的计数,并利用随机采样的方式对大田麦穗进行单位面积图像采样,以采样图像中麦穗数量的平均值作为产量预估基准,进一步实现麦穗产量预估。随机抽取100幅不同小麦图片进行测试,与人工计数结果进行对比,准确率达到97.30%,漏检率为0.34%,误检率为2.36%,误差率为2.70%。试验结果表明,此方法能够克服环境中的多种噪声干扰,能够在不同光照条件下对麦穗进行计数和产量预估。     

改进SSD的灵武长枣图像轻量化目标检测方法

针对加载预训练模型的传统SSD（Single Shot MultiBox Detector）模型不能更改网络结构，设备内存资源有限时便无法使用，该研究提出一种不使用预训练模型也能达到较高检测精度的灵武长枣图像轻量化目标检测方法。首先，建立灵武长枣目标检测数据集。其次，以提出的改进DenseNet网络为主干网络，并将Inception模块替换SSD模型中的前3个额外层，同时结合多级融合结构，得到改进SSD模型。然后，通过对比试验证明改进DenseNet网络和改进SSD模型的有效性。在灵武长枣数据集上的试验结果表明，不加载预训练模型的情况下，改进SSD模型的平均准确率（mAP，mean Average Precision）为96.60%，检测速度为28.05帧/s，参数量为1.99×106，比SSD模型和SSD模型（预训练）的mAP分别高出2.02个百分点和0.05个百分点，网络结构参数量比SSD模型少11.14×106，满足轻量化网络的要求。即使在不加载预训练模型的情况下，改进SSD模型也能够很好地完成灵武长枣图像的目标检测任务，研究结果也可为其他无法加载预训练模型的目标检测任务提供新方法和新思路。     

A computer vision system for oocyte counting using images captured by smartphone

This work proposes a computer vision procedure for counting Twospot astyanax (Astyanax bimaculatus) oocytes in Petri dishes using images captured by smartphone. First, the proposed procedure uses simple linear iterative clustering (SLIC) to divide the images into groups of pixels (superpixels). Then, based on their color and space characteristics, the images are classified into light background, dark background, dirt, or oocyte by a machine learning algorithm. Five different types of machine learning algorithms were tested: support vector machines (SVM), decision trees using the algorithm J48 and random forest, k-nearest neighbors (k-NN), and Naive Bayes. To train the algorithms, 8.578 superpixels were classified by an expert into oocyte (n = 354), dirtiness (n = 651), dark background (n = 3.622), and light background (n = 3.951). Of the five learning algorithms, SVM obtained the best result with 97% correct oocyte recognition. Given the wide availability of smartphones, we therefore conclude that the presented procedure can be a valuable tool in future experiments and studies on fertilization and hatching success in Twospot astyanax.     

Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques

The objective of this study was to compare the performance of two different remotely sensed techniques in detecting the effects of terminal heat stress and N fertilization on final maize aerial biomass (AB) and grain yield (GY). The study was conducted under field conditions for two consecutive growing seasons. Six N treatments combining three doses [0, 100, 200 Kg N ha⁻¹] and two timings [at V4 and at 15 days before silking] were applied. Within each N treatment three heat treatments were applied (pre-flowering, post-flowering and the control treatment at ambient air temperature). Remote sensing measurements were taken with a multispectral band camera to measure the normalized difference vegetation index (NDVI) and a digital Red/Green/Blue (RGB) camera to measure the normalized green red difference index (NGRDI). Both indices failed to predict the GY of pre-flowering heat-treated plants due to grain set establishment problems that could not be detected by vegetation indices which are designed to capture differences in green canopy area. In contrast, both the NGRDI and the NDVI correlated positively with GY and AB in the control heat treatment and to a lesser extent in the post-flowering heat treatment. Under the control heat treatment, the NGRDI exhibited higher correlations with AB and GY than the NDVI across the N fertilization treatments. Since the NGRDI is formulated based only on the reflectance in the visible regions (VIS) of the spectrum (Green and Red) without dependence on the near infrared regions (NIR), it performs better than the NDVI. This is because it overcame the reported saturation patterns at high leaf area index and was more efficient at capturing even small differences in leaf colour (chlorophyll content) due to the different applied N treatments. Also, the NGRDI seemed to be a more seasonally independent parameter than the NDVI, which is more affected by temporal variability within the field, and thus the NGRDI predicted AB and GY better than the NDVI when combining the data of the two growing seasons.     

吉林省西部新开发水田遥感动态监测研究

为了响应土地整理开发项目的政策,吉林省西部地区开发了大面积的水田。为掌握新开发水田的动态变化,有必要对吉林省地区进行土地利用分类。以吉林省前郭县为例,利用2010年环境卫星数据进行不同土地利用分类方法的比较,进而对另外3景图像进行信息提取,分析4个年份新开发水田的分布及变化情况。结果表明,支持向量机法比最大似然法的耕地分类精度高约5%,其产品和用户精度分别为95%和84%。加入纹理信息没有显著提高分类精度。2009~2012年水田面积分别增加-67.7 km2,111.7 km2和265.01 km2。     

寻路算法在中文金融机构名匹配中的应用

《岳阳楼记》对韩国岳阳楼题诗的影响，主要体现在其体现的以天下为己任的儒家社会责任感。通过对韩国岳阳楼题诗中意象的统计学分析，我们还看到了《岳阳楼记》在文本营建层面对题诗的影响及其流变。     

遥感数字图像的无缝镶嵌

在遥感图像的应用中,当研究区处于几幅图像的交界处或研究区较大需多幅图像才能覆盖时,常常需要把覆盖研究区的那些图像进行配准并把这些图像镶嵌起来,才能更好地统一处理、解译分析和研究.以航空像片为例,利用图像处理软件ERMAPPER 6.2对图像进行数字镶嵌并作无缝接边处理.结果表明:这样,大大改善了镶嵌后图像的质量,解决了手工镶嵌图像中接缝偏移、色调不均匀、地质界线延伸、影像错位等许多问题 (大幅图像尤其明显),为后期的图像处理创造良好的基础.目前出现的米级高分辨率的卫星图像同本次采用的航空像片在波段和分辨率上具有相似性,因此本研究对这些高分辨率卫星图像的镶嵌同样适用.     

基于神经网络的树叶识别系统研究

在林业系统注册树木种类时,通常采用大量树叶上的可见外形特点来辨别数目繁多的树木种类。针对此问题,本文提出了基于神经网络的树叶识别方法,该方法建立并管理一个分等级的树叶图像体系,通过对不同种类树叶的边缘提取来识别每种树叶的特征点,从而得到树叶的外部轮廓来区分树木种类:给出了相应的改进型神经网络算法,并给出详细的论述过程:最后,通过Java语言给出了系统实现并做了大量的数值仿真。结果证明本文方法是可行有效的。图4表1参8。     

沙化土地信息提取研究

采用LandsatETM 多时相影像,在地类光谱特征分析的基础上,用分层分离的方法,提取出沙化土地信息。以全国沙漠化普查地类划分标准中的植被覆盖度为确定沙化土地程度的依据,通过建立NDVI和实测植被覆盖度的回归模型,确定不同程度沙化土地对应的植被覆盖度与NDVI的对应关系,用NDVI阈值进行划分,得到沙化土地程度图。