基于类球形亮度变换的水果表面缺陷提取

针对基于机器视觉技术的水果表面缺陷因受到亮度不均影响而提取困难的问题,以阿克苏苹果为研究对象,采用可见-近红外双CCD成像系统,设计了一种无需预先建模的类球形亮度变换方法,对R分量图像进行亮度变换,变换后的图像使整个水果表面正常区域灰度趋于一致,而缺陷区域依然保留为低灰度区,增强了缺陷和正常果皮的对比度,提高了缺陷检测精度.使用共计100个样本评估算法的可行性,其中45个缺陷果的检测精度为93.3％,55个正常果的检测正确率为100％,整体检测精度达到97％.研究结果表明,利用基于类球形亮度变换结合单阈值分割方法提取水果表面缺陷是可行的.     

基于元搜索的农业信息可视化平台实现研究

随着农业信息资源呈爆炸性增长,可视化信息检索平台的研制显得日益迫切。以农业信息资源检索显示为对象,将可视化技术与搜索引擎技术相结合,主要叙述了基于元搜索的农业信息可视化中的数据处理方法和相应功能模块的设计、实现方法,还叙述了平台界面中的一些交互功能,设计实现了一个基于元搜索的农业信息可视化平台,整个系统性能稳定,能够较好地显示搜索结果。本研究不仅对农业信息检索可视化有帮助,对其它领域信息检索以及对信息检索技术本身的应用研究也具有较好的参考价值。     

基于比较的大米样本图像分割

[目的]提高大米加工精度时图像识别的效果。[方法]采用SUSAN算子,讨论其参数的选取原理,并与传统边缘检测算法作对比。[结果]SUSAN边缘检测算法对含噪大米样本图像的边缘检测效果优于传统的边缘检测算法。[结论]SUSAN边缘检测算法非常适于含噪或低对比度大米样本图像的边缘检测     

基于GMM的黄瓜病害图像建模

通过对黄瓜病害图像的准确分析,有效提取了图像的底层特征,建立了8种常见黄瓜病害的高斯混合模型（Gaussian Mixture Model,GMM）,并利用最大期望算法（Expectation-Maximization,EM）估计GMM的参数,精确描述了8种黄瓜病害的特征分布,从而提高了对黄瓜病害的正确识别和为害情况的准确把握,为实现黄瓜病害的实时与准确的预测和防治提供了理论依据。     

基于形态结构特征参数的樟子松人工林单木可视化研究

为更加直观地展示樟子松人工林单木的整个生长过程,将系统分析方法和数学建模技术应用于樟子松人工林单木的形态模拟。通过对樟子松人工林单木形态数据的定量分析,结合前人的研究成果,构建了樟子松人工林单木形态模拟模型,并以VC＋＋6.0为开发平台,利用OpenGL构建了樟子松人工林单木生长模拟系统,整个系统由形态模型模块、树冠结...     

Discrimination of bark from wood chips through texture analysis by image processing

Utilization of wood chips for bioenergy requires classification and segregation of the constituents of the chipped mass to help optimize energy conversion. Wood chips obtained from processes such as forest thinning can contain a considerable amount of material other than wood chips, such as bark. An image processing algorithm was developed to discriminate bark from wood chips. The algorithm involved object identification, image capture, single value decomposition to describe wood texture evident in grayscale image with a single numerical value, and application of logistic models involving the single values representative of wood texture to predict whether a chip is bark. The percentage of correct predictions using this system was about 98%.     

Automatic counting of annual rings on Pinus sylvestris end faces in sawmill industry

One of the essential requirements of current agricultural practice is the need to evolve towards crop techniques that make better use of productive factors behaving in sustainable and environmentally respectful ways. Pest control is one of the most important problems to take into account, due to the significant production losses that pests may cause. The Integrated Production for olive crop defines a set of rules that has to be followed in order to ensure a production of higher quality, and promotes an environmentally respectful model of Olive cultivation. This work presents SAIFA (spanish acronym for Sistema de Alerta e Información Fitosanitaria Andaluz - Andalusian Phitosanitary Information and Alert System) a web-based information system which allows monitoring the Integrated Production for the olive crop in Andalusia, Spain. SAIFA has been built as a tool to assist agricultural technicians to comply with the quality standards of Integrated Production, and to help decision makers to choose the actions to be performed in the crop. Also, it assists coordinators to choose an Integrated Production strategy applicable for the whole region with the main objective of ensuring the safety of crops and reporting the current phitosanitary state to the authorities.     

Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry

This paper describes a computer vision based model for object detection that can serve as a preliminary step in fruit prognosis, which involves the estimation of the number, diameter and yield of apple fruits. In order to overcome the recognition unreliability in uncontrolled environments caused by uneven illumination conditions, partly occluded surfaces, and similar background features, we rely on a combination of the object's colour, texture and 3D shape properties. In our research, we apply colour segmentation to multiple scene snapshots to separate potential regions from the background and verify them first with texture analysis and second by reconstructing them to 3D space. By analysing all three distinct features (colour, texture and 3D shape) of possible areas, we can safely conclude if they represent fruits we are looking for. Once we detect and verify all areas representing fruits, we can measure their size and model estimated fruit yield.     

A morphological assessment system for ‘show quality’ bovine livestock based on image analysis

Morphological assessment is one important parameter considered in conservation and improvement programs for bovine livestock. This assessment process consists of scoring an animal based on its morphology and is normally carried out by highly qualified staff. These animals are all of agreed ‘show quality’ and hence they are morphologically very similar.This paper presents a system designed to provide an assessment based on a lateral image of the cow. The system consists of two main parts: a feature extraction stage, to reduce the information on the cow in the image to a set of parameters, and a neural network stage to provide a score based on that set of parameters. For the image analysis section, a model of the animal is constructed by means of the point distribution model (PDM) technique. Later, that model is used in the searching process within each image, which is implemented using genetic algorithms (GAs). As a result of this stage, the vector of weights that describe the deviation of the given shape from the mean is obtained. This vector is used in the second stage, where a multilayer perceptron is trained to provide the desired assessment, using the scores given by experts for selected cows.The system has been tested with 138 images corresponding to 44 individuals of a special rustic breed, with very promising results, given that the information contained in only one view of the cow can not be considered complete.     

基于关键点存取的虚拟植物碰撞检测方法的研究

针对虚拟植物可视化模拟过程中的碰撞检测算法复杂等问题,提出了基于关键点存取的碰撞检测方法.该方法对植物生长特点进行分析,将要碰撞检测的植株进行关键生物点的提取和比较,采用邻接表的形式对关键点进行存储,使用深度优先遍历法进行查找.这种方式将有效减少关键点的比较数量,提高了碰撞检测速度.