块运动估计的研究进展

运动估计是根据视频序列中时间上相关的信息估计场景或目标的2维运动向量场的过程。运动估计在超分辨率图像复原中的作用是把所有低分辨率观测帧的像素映射到参考帧的相应位置。精确的运动估计是超分辨率图像复原的关键。基于块的模型是超分辨率图像复原中常用的运动估计方法。介绍了块运动估计的概念,概述了4类快速块运动估计的方法,详细描述了第1类方法中几种主要算法的搜索过程,并将多种快速块匹配算法进行了总结比较。     

一种快速全局运动估计算法

为了对全局运动进行准确快速的估计,在对全局运动估计算法进行研究的基础上,提出了一种快速全局运动估计算法。该算法基于非线性密度估计,采用了六参数的仿射模型。为了提高计算速度,采用了3层金字塔进行多分辨率计算,而且在每层迭代计算中,将基于块的外点去除算法与特征点提取算法相结合,这样既加快了算法的速度,又提高了计算结果的准确性。全局运动估计的实验结果表明,该算法在速度和准确性上具有一定的优势。     

基于小波域分形编码的图像插值

传统的图像插值方案(包括最近邻插值、双线性插值和B样条插值等),大多是基于图像分块的光滑连续模型。由于该模型不能很好地描述自然图像的特点,因此插值得到的图像质量不高。为寻求更有效的插值方案,首次提出了基于小波域分形编码的插值算法。该算法在FW编码的基础上,首先利用图像的局部自相似模型,通过小波树的膨胀,并借助小波域的第1级子带,对超分辨率级的第0级子带进行最优预测,再经过小波反变换来得到插值图像。标准测试图像的实验表明,该插值算法与传统的双线性插值相比,不仅可以获得清晰的纹理和边缘,而且峰值信噪比也更高,因此插值得到的图像更加精确、真实。     

MODIS及ASTER卫星数据在林火面积估算中的应用

用于火点监测的MODIS数据的分辨率是1km,不可避免地存在有混合像元。本文使用Dozier提出的方法,利用卫星数据不同波段对高温目标的不同响应来获取子像素火点温度及面积,并使用同时相高分辨率的ASTER数据进行了验证。结果表明在背景温度估计准确的情况下,MODIS数据可以比较准确地获得子像素火点的面积,对于判断火情火势具有重要意义。     

WinSEEDLE系统在测定刺萼龙葵种子维度中扫描分辨率的优化

WinSEEDLE系统是专门用于研究种子或病斑形态的专业图像分析系统。以外来杂草刺萼龙葵（Solanum rostratum Dunal）种子为材料,探讨了应用该系统进行种子维度测定的最佳方案,以期为相关研究提供借鉴。结果表明,扫描分辨率显著影响WinSEEDLE系统测量种子的准确度和精确度,当分辨率达到300 dpi时可准确计数所有刺萼龙葵种子。测定刺萼龙葵种子维度的推荐分辨率为600 dpi,此条件下单次扫描用时较短（27 s）,测量的准确度和精确度较其他分辨率高,测得种子平均长度2.61 mm、宽度2.11 mm,与人工实测值基本一致。     

用小波系数估计图像边缘方向的相干增强扩散图像降噪算法

在分析相干增强扩散方法和小波阈值收缩方法之间关系的基础上,给出了相干增强扩散在小波分析意义下的解释,同时解释了相干增强扩散方法与小波阈值收缩方法在图像性质上的等价性。针对相干增强扩散计算扩散矩阵较慢的缺点,提出了一种用小波系数估计图像边缘方向的相干增强扩散图像降噪算法。仿真试验结果表明,该扩散算子可以很好地定位图像边缘,较好地运用了小波的时频分析功能。     

基于混合蛙跳算法优化PCNN的马铃薯病害图像分割

为优化马铃薯病斑图像特征提取与病害识别的关键步骤——图像分割的精度,保证分割后的图像能够较好地保留原病斑图像的轮廓与细节,采用混合蛙跳算法优化脉冲耦合神经网络(pulse coupled neural network,PCNN)参数,建立一种高精度的用于马铃薯病斑图像分割的混合蛙跳算法(shuffled frog leaping algorithm,SFLA)-PCNN模型,该模型选用图像分割香农熵与图像分割紧凑度的加权和作为适用度函数,对马铃薯晚疫病害图像进行试探分割,分割正确率为95.41%,实现PCNN参数的自适应优化配置,并获得PCNN参数配置方案为:神经元交互连接系数β=0.38、脉冲激励衰减系数a_θ=0.24、激励脉冲幅度衰减系数V_θ=0.82。利用优化后的PCNN对马铃薯软腐病、环腐病、银腐病、粉痂病、灰霉病5种病害图像进行分割,分割正确率分别为94.41%、95.69%、93.89%、93.91%和93.21%,平均正确率为94.42%,证明SFLA-PCNN模型能有效地从背景区域提取马铃薯病斑,可用于马铃薯病斑检测。     

适用于地形复杂地区水土流失评价的高分辨率DEM建立方法

选择地形复杂、水土流失严重的东北黑土漫岗区和南方红壤丘陵区为研究区,利用AUNDEM软件和1∶1万数字地形图,对两地区高分辨率水文地貌关系正确DEM的建立方法进行研究,并对所建DEM的质量进行了评价。结果表明,利用ANUDEM和1∶1万地形图插值建立的两个样区DEM三个主要参数分别为:东北黑土漫岗区分辨率1 m,迭代次数40,第二糙度系数0.5;南方红壤丘陵区分辨率5 m,迭代次数40,第二糙度系数0.6。通过与基础数据比较、DEM对地形的表现能力及水系网络分析三个方面对DEM的质量进行了评价,结果表明所建DEM能正确表现地形的形态及其与水系网络的关系,其派生等高线与原等高线符合度高。利用ANUDEM和数字地形图,可建立两类型区水文地貌关系正确的DEM,为水文和土壤侵蚀模拟分析提供支持。     

基于无人机数字图像与高光谱数据融合的小麦全蚀病等级的快速分类技术

小麦全蚀病是检疫性的土传病害,对小麦生产危害极大,对其发生的监测是治理的根本。遥感技术可实时、宏观地监测病害发生发展,尤其是将光谱信息与高分辨率数字图像进行融合,可直观、精准地对病害识别和分类。本文基于计算机视觉技术,通过光谱数据与高分辨率数字图像结合的方法,对小麦全蚀病等级进行快速分类。首先,通过ASD非成像光谱仪获取小麦全蚀病的光谱信息,提取全蚀病特征光谱,建立光谱比。其次,利用无人机获取的实时田间数码图像,对其颜色特征进行重量化。最后,利用基于支持向量机的决策树分类对图像视场中的不同全蚀病等级进行分类。结果表明,4个全蚀病等级的分类精度均大于86%(Kappa0.81),平均运算时间小于30s。通过与实地调查的小麦全蚀病的白穗率等级做比对,验证分类结果的准确性,结果表明该方法基本可以实现对小麦全蚀病等级的实时监测。     

边界层参数化方案对一次西北地区沙尘天气过程影响的数值模拟研究

使用耦合化学模块的高分辨率中尺度数值模式WRF-Chem3.4,结合近地层观测资料评估YSU、MYJ、QNSE、MYNN2.5和BouLac共5种边界层参数化方案对2007年3月27日西北地区一次沙尘天气过程模拟效果的影响,结果显示5种边界层参数化方案均可模拟出此次沙尘天气的发展演变过程,其中YSU和BouLac方案模...     

多源遥感数据融合及其在土地资源调查中的应用

多源信息融合的目的就是要充分集成不同来源数据的优点,尽可能多地获取地物信息,以大大提高解译精度和可信度。本文结合国土资源部“典型县耕地资源分布与生态退耕遥感监测”规划的前期预研究项目,利用ETM和SPOT等多源遥感数据,探讨不同遥感信息融合过程中的处理方法与关键技术,在对比分析所使用的各种数据优缺点的基础上,阐述了数据纠正、配准、融合的技术过程和出现的问题,列出本项目为遥感影像解译所归纳的主要地类的特征图谱。利用高分辨率遥感数据融合较低分辨率数据,进行土地利用变化分析,及时准确获取变化信息,为有关部门进行土地的规划、管理提供科学依据就有着十分重要的意义。     

Plant classification for weed detection using hyperspectral imaging with wavelet analysis

The goal of this study is to develop a new weed detection method that can be applied for automatic mechanical weed control. For successful weed detection, plants must be classified into crops and weeds according to their species. In this study, we employed a portable hyperspectral imaging system. The hyperspectral camera can capture landscape images that include crops, weeds, and the soil surface, and can provide more extensive information than conventional red, green, and blue (RGB) images. Although RGB images consist of red, green, and blue wavebands, the obtained hyperspectral images consist of 240 wavebands of spectral information. Hyperspectral imaging is expected to provide powerful technology for agricultural sensing. In the initial step of this study, the image pixels of the plants (crop or weeds) were segmented from the background soil surface using Euclidean distance as the discriminant function. In the next step, the image pixels of the crop (sugarbeet) and weeds (four species) were classified using the difference in the spectral characteristics of the plant species. In this process, classification variables were generated using wavelet transformation for data compression, noise reduction, and feature extraction, and then stepwise linear discriminant analysis was applied. The validation results indicate that the developed classification method has potential for practical use.     

Automatic sex detection of individuals of Ceratitis capitata by means of computer vision in a biofactory

BACKGROUND: The sterile insect technique (SIT) is acknowledged around the world as an effective method for biological pest control of Ceratitis capitata (Wiedemann). Sterile insects are produced in biofactories where one key issue is the selection of the progenitors that have to transmit specific genetic characteristics. Recombinant individuals must be removed as this colony is renewed. Nowadays, this task is performed manually, in a process that is extremely slow, painstaking and labour intensive, in which the sex of individuals must be identified. The paper explores the possibility of using vision sensors and pattern recognition algorithms for automated detection of recombinants. RESULTS: An automatic system is proposed and tested to inspect individual specimens of C. capitata using machine vision. It includes a backlighting system and image processing algorithms for determining the sex of live flies in five high-resolution images of each insect. The system is capable of identifying the sex of the flies by means of a program that analyses the contour of the abdomen, using fast Fourier transform features, to detect the presence of the ovipositor. Moreover, it can find the characteristic spatulate setae of males. Simulation tests with 1000 insects (5000 images) had 100% success in identifying male flies, with an error rate of 0.6% for female flies. CONCLUSION: This work establishes the basis for building a machine for the automatic detection and removal of recombinant individuals in the selection of progenitors for biofactories, which would have huge benefits for SIT around the globe.     

Identification of mungbean lines with tolerance or resistance to yellow mosaic in fields in India where different begomovirus species and different <Emphasis Type="Italic">Bemisia tabaci</Emphasis> cryptic species predominate

The segmentation of symptoms during image analysis of diseased plant leaves is an essential process for detection and classification of diseases. However, there are challenges involved in the task, many of them related to the variability of image and host/symptom characteristics and conditions. As a result of those challenges, the methods proposed in the literature so far focus on a specific problem and are usually bounded by tight constraints regarding image capture conditions. This research explores a new automatic method for segmenting disease symptoms on plant leaves that was designed to be applicable in a wide range of situations. The proposed technique employs only color channel manipulations and Boolean operations applied on binary masks, thus being simpler and more robust compared to many previously described automatic methods. Its effectiveness is demonstrated by tests performed over a large database containing images of 77 different diseases of 11 plant species. A comparison with manual segmentation is also presented, further reinforcing the advantages of the proposed approach.     

Image recognition and empirical application of desert plant species based on convolutional neural network

In recent years, deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact. Traditional plant taxonomic identification requires high expertise, which is time-consuming. Most nature reserves have problems such as incomplete species surveys, inaccurate taxonomic identification, and untimely updating of status data. Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model. Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects, this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang, such as species investigation and monitoring, by using deep learning. Since desert plant species were not included in the public dataset, the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China (PPBC). After the sorting process and statistical analysis, a total of 2331 plant images were finally collected (2071 images from field collection and 260 images from the PPBC), including 24 plant species belonging to 14 families and 22 genera. A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance, from different perspectives, to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang. The results revealed 24 models with a recognition Accuracy, of greater than 70.000%. Among which, Residual Network X_8GF (RegNetX_8GF) performs the best, with Accuracy, Precision, Recall, and F1 (which refers to the harmonic mean of the Precision and Recall values) values of 78.33%, 77.65%, 69.55%, and 71.26%, respectively. Considering the demand factors of hardware equipment and inference time, Mobile NetworkV2 achieves the best balance among the Accuracy, the number of parameters and the number of floating-point operations. The number of parameters for Mobile Network V2 (MobileNetV2) is 1/16 of RegNetX_8GF, and the number of floating-point operations is 1/24. Our findings can facilitate efficient decision-making for the management of species survey, cataloging, inspection, and monitoring in the nature reserves in Xinjiang, providing a scientific basis for the protection and utilization of natural plant resources.     

Real-time vision-based detection of Rumex obtusifolius in grassland

Rumex obtusifolius is a common grassland weed that is hard to control in a non-chemical way. The objective of our research was to automate the detection of R. obtusifolius as a step towards fully automated mechanical control of the weed. We have developed a vision-based system that uses textural analysis to detect R. obtusifolius against a grass background. Image sections are classified as grass or weed using 2-D Fourier analysis. We conducted two experiments. In the first (laboratory) experiment, we collected 28 images containing R. obtusifolius and 28 images containing only grass. Between 23 and 25 of 28 images were correctly classified (82–89%) as showing R. obtusifolius ; all grass images were correctly classified as such. In the second (field) experiment, a self-propelled platform was used to obtain five sequences of images of R. obtusifolius plants. We used the parameters that gave the best classification results in the first experiment. We found, after changing one of the algorithm's parameters in response to prevailing light conditions, that we were able to detect R. obtusifolius in each image of each sequence. The algorithm scans a ground area of 1.5 m² in 30 ms. We conclude that the algorithm developed is sufficiently fast and robust to eventually serve as a basis for a practical robot to detect and control R. obtusifolius in grassland.     

采样式多通道Gabor滤波对掌纹的特征提取和分类

掌纹识别已被证实为最方便和有效的身份识别方法之一。根据掌纹的性质提出了一种掌纹方向特征提取的新方法,该方法首先利用选取掌纹中最拟合椭圆的方法寻找感兴趣区域,然后利用适应人感官系统的多通道采样式Gabor滤波器进行滤波,并提出用根据掌纹纹理和方向特性动态选取Gabor滤波器参数的方法来设计滤波器。在滤波过程中,从不同分辨率入手,利用不同方向和宽度的滤波器分别对掌纹的主线、褶皱、嵴线进行提取,在极坐标系下用改进的环行方向投影算法计算块能量,并且进行编码。经过模糊C均值聚类方法验证,结果表明,该方法对于掌纹具有很强的识别能力。     

近地高光谱和低空航拍数字图像遥感 监测小麦条锈病的比较研究

 分别利用近地高光谱和低空航拍数字图像同时对田间小麦条锈病的发生情况进行监测,结果表明近地高光谱遥感参数DVI、NDVI、GNDVI和低空航拍数字图像颜色特征值R、G、B与病情指数存在极显著相关性,整体上,所选近地高光谱参数与病情指数的相关性要优于低空航拍数字图像参数与病情指数的相关性,而且近地高光谱参数DVI、NDVI、GNDVI与低空航拍数字图像参数R、G、B之间均存在极显著负相关关系。分别建立了基于近地高光谱参数GNDVI和低空航拍数字图像参数R的田间小麦条锈病病情估计模型,模型均达到较好的拟合效果,其中近地高光谱参数GNDVI对小麦条锈病的监测效果好于低空航拍数字图像参数R,而低空航拍数字图像具有可以进行大面积快速监测的优势,因此在实际应用中可以根据需要选择其中一种方法或参数来估计田间小麦条锈病的发生和流行程度。     

Positive plant–soil feedbacks of the invasive Impatiens glandulifera and their effects on above‐ground microbial communities

Impatiens glandulifera is one of the most widespread invasive plant species in the UK. Although aspects of its biology are known, there is little information about its association with microbial communities, both above ground and below ground. Furthermore, it is unknown whether this species exhibits any form of plant–soil feedback (PSF), commonly seen in other invasive weeds. We conducted a PSF experiment, in which plants of I. glandulifera were grown in soil that supported the species and compared with plants grown in a control soil from the same locality. Soil nutrients were measured, and the soil and foliar microbial communities were assessed. Impatiens glandulifera grew larger and faster in conditioned soil compared with the control. Higher levels of phosphate were also found in conditioned soils. Arbuscular mycorrhizal fungal (AMF) colonisation was lower in conditioned soils, suggesting that I. glandulifera may rapidly alter AMF communities in invaded areas. PSFs had a significant effect on the foliar endophyte community, with clear separation of species between conditioned and control soils. These results show that I. glandulifera displayed a positive PSF and the PSF mechanism extended beyond the soil microbial community to affect foliar endophytes. The observed increase in endophytes in plants grown in conditioned soil could enhance resistance to herbivory, thus further accentuating the invasive properties of this species.     

一种基于agent随机扩散的图像分割方法

图像分割是图像处理的关键环节,直接影响以后的分析、识别和解译。根据进化agent具有自适应性、非线性映射和高度并行处理能力等优点,提出了一种基于agent随机扩散的图像分割方法。在该方法中,agent点随机地撒在网格单元上,并在满足一致性标准的区域用标签标定。agent点有复制和扩散两种行为扩散模式,当一个agent成功的找到一个像素满足一致性标准,它将在周围区域复制一系列后代,因此这些后代更容易找到那些满足一致性条件的像素,而对于那些超过生命周期的agent点将停止搜索,从环境中消失。利用医学胸部的CT图像和脑部的磁共振图像进行的实验结果表明,该方法能较好地从图像中提出感兴趣的区域。