基于多尺度曲率植物叶片特征提取

基于B-样条小波计算边缘曲线多尺度曲率函数,根据多尺度信息筛选和定位超过一定曲率闲值的精角点,这样的点代表了边缘曲线的主要信息．文中使用了Canny边缘检测算子和数学形态学方法进行图像预处理,B-样条小波降低对噪声及扰动的灵敏性,以提高真实精角点定位水平．综合[1,2]给出新的角特征矢量,并生成角点特征序列CS和弧段特征序列SS．特征序列可作为自适应-时滞单元混合神经网络的输入,通过学习完成图像分类与识别,对基于植物叶片形状识别种类提供辅助。     

基于人工神经网络的叶脉信息提取——植物活体机器识别研究Ⅰ

叶片的识别是识别植物的重要组成部分,特别在野外识别植物活体尤其重要。叶脉的脉序是植物的内在特征,包含有重要的遗传信息。但由于叶脉本身的多样性,利用单一特征的图像处理方法难以有效地提取叶脉。为了充分利用图像的信息,本文提出了一种基于人工神经网络的叶脉提取方法。该方法利用边缘梯度、局部对比度和邻域统计特征等10个参数来描述像素的邻域特征,并将其作为神经网络的输入层。实验结果表明,与传统方法相比,经过训练的神经网络能够更准确地提取叶脉图像,为进一步的叶片识别打下了良好的基础。     

Extraction of aquatic plants based on continuous removal method and analysis of its temporal and spatial changes—A case study of Guanting Reservoir

光谱特征变量的筛选作为水生植物识别的重要手段之一, 在水生植物种类识别研究中应用广泛。该研究将实测光谱特征提取与多时相Landsat 8 OLI影像数据分析相结合, 找到一种有效识别不同种类水生植物的特征变量。在水生植物反射光谱特征分析中引入矿质分析中普遍使用的连续统去除法, 对光谱重采样结果作连续统去除处理后提取光谱吸收深度特征。采用单因素方差分析法对比7个光谱重采样波段和3个连续统去除吸收深度敏感波段, 发现经连续统去除处理的短波红外1波段(SWIR1CR)对于不同类型的水生植物区分效果最佳。将连续统去除法应用到遥感影像处理上, 发现SWIR1CR波段能较好区分沉水植物和挺水植物; 结合影像归一化植被指数和SWIR1CR波段可较好区分三类水生植物。结合特征波段筛选结果采用支持向量机分类方法, 得到水生植物的分类结果精度为86.33%, 对比全生长期12期影像提取的水生植物分布图, 发现水生植物主要分布于官厅水库库区南北岸浅水区, 水生植物面积最大时约占库区总面积的35.13%; 其中沉水植物年内生长分布变化幅度较大, 6月上旬开始迅速生长; 10月份水生植物开始衰减; 11月份水生植物占库区面积的20%, 沉水、浮水植物大幅衰减消失。     

基于连续统去除法的水生植物提取及其时空变化分析——以官厅水库库区为例

光谱特征变量的筛选作为水生植物识别的重要手段之一, 在水生植物种类识别研究中应用广泛。该研究将实测光谱特征提取与多时相Landsat 8 OLI影像数据分析相结合, 找到一种有效识别不同种类水生植物的特征变量。在水生植物反射光谱特征分析中引入矿质分析中普遍使用的连续统去除法, 对光谱重采样结果作连续统去除处理后提取光谱吸收深度特征。采用单因素方差分析法对比7个光谱重采样波段和3个连续统去除吸收深度敏感波段, 发现经连续统去除处理的短波红外1波段(SWIR1CR)对于不同类型的水生植物区分效果最佳。将连续统去除法应用到遥感影像处理上, 发现SWIR1CR波段能较好区分沉水植物和挺水植物; 结合影像归一化植被指数和SWIR1CR波段可较好区分三类水生植物。结合特征波段筛选结果采用支持向量机分类方法, 得到水生植物的分类结果精度为86.33%, 对比全生长期12期影像提取的水生植物分布图, 发现水生植物主要分布于官厅水库库区南北岸浅水区, 水生植物面积最大时约占库区总面积的35.13%; 其中沉水植物年内生长分布变化幅度较大, 6月上旬开始迅速生长; 10月份水生植物开始衰减; 11月份水生植物占库区面积的20%, 沉水、浮水植物大幅衰减消失。     

基于颜色名和OpponentSIFT特征的鳞翅目昆虫图像识别

【目的】本研究旨在探索使用先进的计算机视觉技术实现对昆虫图像的自动分类方法。【方法】通过预处理对采集的昆虫标本图像去除背景,获得昆虫图像的前景蒙板,并由蒙板确定的轮廓计算出前景图像的最小包围盒,剪切出由最小包围盒确定的前景有效区域,然后对剪切得到的图像进行特征提取。首先提取颜色名特征,把原来的RGB（Red-Green-Blue）图像的像素值映射到11种颜色名空间,其值表示RGB值属于该颜色名的概率,每个颜色名平面划分成3×3像素大小的网格,用每格的概率均值作为网格中心点的描述子,最后用空阈金字塔直方图统计的方式形成颜色名视觉词袋特征;其次提取OpponentSIFT(Opponent Scale Invariant Feature Transform)特征,首先把RGB图像变换到对立色空间,对该空间每通道提取SIFT特征,最后用空域池化和直方图统计方法形成OpponentSIFT视觉词袋。将两种词袋特征串接后得到该昆虫图像的特征向量。使用昆虫图像样本训练集提取到的特征向量训练SVM（Support Vector Machine）分类器,使用这些训练得到的分类器即可实现对鳞翅目昆虫的分类识别。【结果】该方法在包含10种576个样本的昆虫图像数据库中进行了测试,取得了100%的识别正确率。【结论】试验结果证明基于颜色名和OpponentSIFT特征可以有效实现对鳞翅目昆虫图像的识别。     

BLeafNet: A Bonferroni mean operator based fusion of CNN models for plant identification using leaf image classification

Plants, the only natural source of oxygen, are the most important resources for every species in the world. A proper identification of plants is important for different fields. The observation of leaf characteristics is a popular method as leaves are easily available for examination. Researchers are increasingly applying image processing techniques for the identification of plants based on leaf images. In this paper, we have proposed a leaf image classification model, called BLeafNet, for plant identification, where the concept of deep learning is combined with Bonferroni fusion learning. Initially, we have designed five classification models, using ResNet-50 architecture, where five different inputs are separately used in the models. The inputs are the five variants of the leaf grayscale images, RGB, and three individual channels of RGB - red, green, and blue. For fusion of the five ResNet-50 outputs, we have used the Bonferroni mean operator as it expresses better connectivity among the confidence scores, and it also obtains better results than the individual models. We have also proposed a two-tier training method for properly training the end-to-end model. To evaluate the proposed model, we have used the Malayakew dataset, collected at the Royal Botanic Gardens in New England, which is a very challenging dataset as many leaves from different species have a very similar appearance. Besides, the proposed method is evaluated using the Leafsnap and the Flavia datasets. The obtained results on both the datasets confirm the superiority of the model as it outperforms the results achieved by many state-of-the-art models.     

A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images

Plant diseases cause significant food loss and hence economic loss around the globe. Therefore, automatic plant disease identification is a primary task to take proper medications for controlling the spread of the diseases. Large variety of plants species and their dissimilar phytopathological symptoms call for the implementation of supervised machine learning techniques for efficient and reliable disease identification and classification. With the development of deep learning strategies, convolutional neural network (CNN) has paved its way for classification of multiple plant diseases by extracting rich features. However, several characteristics of the input images especially captured in real world environment, viz. complex or indistinguishable background, presence of multiple leaves with the diseased leaf, small lesion area, solemnly affect the robustness and accuracy of the CNN modules. Available strategies usually applied standard CNN architectures on the images captured in the laboratory environment and very few have considered practical in-field leaf images for their studies. However, those studies are limited with very limited number of plant species. Therefore, there is need of a robust CNN module which can successfully recognize and classify the dissimilar leaf health conditions of non-identical plants from the in-field RGB images. To achieve the above goal, an attention dense learning (ADL) mechanism is proposed in this article by merging mixed sigmoid attention learning with the basic dense learning process of deep CNN. The basic dense learning process derives new features at higher layer considering all lower layer features and that provides fast and efficient training process. Further, the attention learning process amplifies the learning ability of the dense block by discriminating the meaningful lesion portions of the images from the background areas. Other than adding an extra layer for attention learning, in the proposed ADL block the output features from higher layer dense learning are used as an attention mask to the lower layers. For an effective and fast classification process, five ADL blocks are stacked to build a new CNN architecture named DADCNN-5 for obtaining classification robustness and higher testing accuracy. Initially, the proposed DADCNN-5 module is applied on publicly available extended PlantVillage dataset to classify 38 different health conditions of 14 plant species from 54,305 images. Classification accuracy of 99.93% proves that the proposed CNN module can be used for successful leaf disease identification. Further, the efficacy of the DADCNN-5 model is checked after performing stringent experiments on a new real world plant leaf database, created by the authors. The new leaf database contains 10,851 real-world RGB leaf images of 17 plant species for classifying their 44 distinguished health conditions. Experimental outcomes reveal that the proposed DADCNN-5 outperforms the existing machine learning and standard CNN architectures, and achieved 97.33% accuracy. The obtained sensitivity, specificity and false positive rate values are 96.57%, 99.94% and 0.063% respectively. The module takes approximately 3235 min for training process and achieves 99.86% of training accuracy. Visualization of Class activation mapping (CAM) depicts that DADCNN-5 is able to learn distinguishable features from semantically important regions (i.e. lesion regions) on the leaves. Further, the robustness of the DADCNN-5 is established after experimenting with augmented and noise contaminated images of the practical database.     

基于稀疏编码和SCG BPNN的鳞翅目昆虫图像识别

【目的】为了给林业、 农业或植物检疫等行业人员提供一种方便快捷的昆虫种类识别方法, 本文提出了一种新颖的鳞翅目昆虫图像自动识别方法。【方法】首先通过预处理对采集的昆虫标本图像去除背景, 分割出双翅, 并对翅图像的位置进行校正。然后把校正后的翅面分割成多个超像素, 用每个超像素的l, a, b颜色及x, y坐标平均值作为其特征数据。接下来用稀疏编码(SC)算法训练码本、 生成编码并汇集成特征向量训练量化共轭梯度反向传播神经网络（SCG BPNN）, 并用得到的BPNN进行分类识别。【结果】该方法对包含576个样本的昆虫图像的数据库进行了测试, 取得了高于99%的识别正确率, 并有理想的时间性能、 鲁棒性及稳定性。【结论】实验结果证明了本文方法在识别鳞翅目昆虫图像上的有效性。     

基于人工神经网络的叶脉信息提取--植物活体机器识别研究I

叶片的识别是识别植物的重要组成部分,特别在野外识别植物活体尤其重要.叶脉的脉序是植物的内在特征,包含有重要的遗传信息.但由于叶脉本身的多样性,利用单一特征的图像处理方法难以有效地提取叶脉.为了充分利用图像的信息,本文提出了一种基于人工神经网络的叶脉提取方法.该方法利用边缘梯度、局部对比度和邻域统计特征等10个参数来描述像素的邻域特征,并将其作为神经网络的输入层.实验结果表明,与传统方法相比,经过训练的神经网络能够更准确地提取叶脉图像,为进一步的叶片识别打下了良好的基础.     

中国水青冈属 （壳斗科） 叶结构及分类学意义

叶结构对壳斗科(Fagaceae)现存植物和化石的鉴定具有重要意义。通过对水青冈属5种植物叶结构特征进行细致的研究,结果发现水青冈属植物叶脉有羽状弓形脉、羽状半达缘脉两种类型;三级脉有波状对生贯穿、互生贯穿及混合贯穿三种类型;小脉缺失、简单无分支或一次分支;脉间区发育良好,网眼有三边形、四边形和五边形三种类型,排列规则;具齿种类叶齿由齿主脉和齿侧脉构成,齿侧脉环状。研究结果表明水青冈属二级脉与更高级脉序形成的结构稳定且存在种间差异,具重要分类学价值。基于水青冈属叶结构特征观察结果,本次研究编制了水青冈属植物的分种检索表;参照已有研究结果并结合重要外部形态学特征,编制了壳斗科相关类群分属检索表。     

中国水青冈属（壳斗科）叶结构及分类学意义

叶结构对壳斗科（Fagaeeae）现存植物和化石的鉴定具有重要意义。通过对水青冈属5种植物叶结构特征进行细致的研究,结果发现水青冈属植物叶脉有羽状弓形脉、羽状半达缘脉两种类型;三级脉有波状对生贯穿、互生贯穿及混合贯穿三种类型：小脉缺失、简单无分支或一次分支：脉间区发育良好,网眼有三边形、四边形和五边形三种类型,排列规则;具齿种类叶齿由齿主脉和齿侧脉构成,齿侧脉环状。研究结果表明水青冈属二级脉与更高级脉序形成的结构稳定且存在种问差异．具重要分类学价值。基于水青冈属叶结构特征观察结果,本次研究编制了水青冈属植物的分种检索表：参照已有研究结果并结合重要外部形态学特征,编制了壳斗科相关类群分属检索表。     

染色体易位重组位点的自动识别方法研究

染色体易位重组位点的识别对很多染色体遗传性疾病的诊断有着重要的意义.本文基于实际诊断中采集到的24类染色体数据和9号正常与异常染色体数据,构建了一套自动识别染色体易位重组位点的模型和方法.首先,对染色体图像进行预处理,得到了方向梯度直方图特征(HOG)和局部二值模式特征(LBP),构建了基于纹理特征的染色体24分类多通...     

基于复合叶片特征的计算机植物识别方法

该文探讨如何根据植物的叶片特征,利用图像处理和机器学习的方法对植物进行分类。鉴于现有的叶片分类系统多采用单一的特征,如几何和纹理等,仅能在小规模数据库上得到较好的结果。然而,随着样本种类的增多,单一特征在不同种类叶片之间的相似性非常明显,致使分类正确率降低。该研究使用多种复合特征,并提出了原创的预处理方法以及宽度、叶缘频率特征,较传统的几何特征更为详尽。研究结果显示,复合特征可以有效避免算法过拟合问题,使之适用于更大的数据库。通过提取21类植物的叶片宽度、颜色、叶缘和纹理共292维特征,对1 915张数字图像进行了分类,正确率达到93%,并分析了各类特征对分类结果的影响。研究结果表明,在不影响分类正确率前提下,可将特征减少到约100维。     

Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA

To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems.     

弯曲姿态蛾类幼虫的自动识别方法研究

【目的】为害态幼虫现场识别时, 幼虫常出现姿态弯曲情况, 使提取的特征向量失真, 影响幼虫的匹配识别结果。本文提出了一种基于扇形变换的姿态不变胡氏矩特征向量提取方法, 提取的病害幼虫特征向量具有平移、 比例、 旋转和姿态不变性, 可以实现粗短弯曲姿态幼虫的自动识别。【方法】首先在幼虫图像细化的基础上采用最优一致逼近法确定了幼虫的弯曲区域和非弯曲区域。然后, 幼虫的弯曲区域采用扇形变换实现校正变直, 非弯曲区域经旋转和平移与扇形变换后的区域拼接组成完整虫体; 采用八邻域均值法填充变换后虫体区域中的空白点, 实现幼虫像的弯曲自动校正; 在此基础上提取胡氏不变矩具有姿态不变性, 采用最小距离分类器实现了多姿态幼虫的自动识别。最后, 以多种弯曲姿态的斜纹夜蛾Prodenia litura、 棉铃虫Heliocoverpa armigera、 甜菜夜蛾Spodoptera exigua、 玉米螟Ostrinia nubilalis等病害蛾类幼虫为识别对象进行了识别验证。【结果】对于24种不同姿态的幼虫图像, 在80%的识别阈值条件下, 基于经典胡氏不变矩的幼虫识别率为25%, 基于姿态不变胡氏矩的识别率为100%。【结论】实验结果表明该方法对多种弯曲姿态的粗短幼虫具有较高的识别率。     

实蝇科果实蝇属昆虫数字图像自动识别系统的构建和测试

针对双翅目实蝇科果实蝇属昆虫的自动识别,本文提出利用翅及中胸背板图像的局部二进制模式（local binary pattern, LBP）特征,采用Adaboost算法, 设计和开发“实蝇科果实蝇属昆虫数字图像自动识别系统”（Automated Fruit fly Identification System-Bactrocera, AFIS-B）。该系统包括图像采集、图像裁剪、预处理、特征提取、分类器设计、识别和显示,共7个模块。研究结果表明： LBP特征可以有效鉴别实蝇科果实蝇属昆虫;在对实蝇科果实蝇属8个种的测试中, 该系统表现出较高的准确性和稳定性,平均识别率可达80%以上。此外,还对果实蝇属昆虫翅膀及中胸背板图像在光照不均匀、姿态扭曲、样本受损及样本量大小等不同条件下的识别率进行了试验测试。结果表明, 该系统对测试样本的光照不均匀、 姿态扭曲和样本受损都表现出良好的鲁棒性,正确识别率与训练集样本各个种数量在一定条件下明显正相关,与训练集样本物种总量负相关。该项研究为实蝇科有害昆虫自动识别系统的构建及实际应用提供了理论、 方法及基础数据的支撑, 亦可为其他昆虫自动识别系统的研究和构建提供有益借鉴。 关键词：     

Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.