基于计算机视觉的农作物害虫自动检测研究综述

综述了国内外计算机视觉在农业生产领域的应用研究热点——农作物害虫自动检测技术的进展及最新研究成果,重点论述了害虫识别、分类、计算机视觉和图像处理技术的研究和开发,并分析提出了这一技术的发展趋势。     

基于计算机视觉的农作物病虫害识别研究现状

农作物病害虫种类多,不仅给农作物的产量和品质带来了很大影响,也对人类的健康造成较大威胁.分析了国内外农作物病虫害研究的现状,讨论了与以往肉眼观测法不同,采用计算机视觉技术中的数字图像处理以及模式识别等技术具有很高的实用价值,能够精确、实时、快速识别病虫害,从而及时地采取相应的补救措施,提高经济效益.     

基于计算机视觉的苹果自动分级方法研究

研究苹果图像自动分级优化问题,传统方法采用单一特征进行分级,难以全面描述苹果状态,导致分级精度低。为提高苹果分级精度,提出计算机视觉的苹果自动分级方法。首先对计算机视觉采集的苹果图像多种特征进行提取,然后采用主成分分析对特征进行选择,最后建立基于最小二乘支持向量机的苹果自动分级模型。采用苹果数据对分组方法的性能进行仿真测试,实验结果表明,相对于其它分级方法,计算机视觉方法不仅提高了苹果自动分级效率,克服了传统方法的缺陷,而且加快了苹果自动分级速度,为水果品质分级等领域提供广泛的应用前景。     

一种基于计算机视觉的日常行为识别方法

人的行为理解与描述是近年来被广泛关注的研究热点和难点.本文设计了一个利用视频序列对人的日常行为进行识别的系统,本系统由图像处理模块和图像理解模块组成,图像处理模块主要实现图像的预处理和人体的正确分割,图像理解模块则完成人体动作的识别.利用本系统对人的日常生活中的"站"、"躺"、"蹲"、"坐"、"弯腰"等五种动作进行识别,试验结果表明本方法在实践中是可行的.在虚拟现实、视觉监控、感知接口等领域均有着广阔的应用前景.     

基于计算机视觉技术的电力指针表自动读数识别研究

本文详细介绍了电力指针表自动读数识别的有关算法,通过计算机视觉技术与查表法相结合的方式实现高精度的读数判别,在不需要调整相机位置的情况下大幅提高了读数识别效率,研究结果可广泛应用于电力、机械、医疗等行业.     

基于机器视觉的在线环境监测系统设计

基于图像处理与机器视觉技术,设计了在线环境监测系统.并对该系统基本构成、工作原理以及一些关键图像技术进行了研究,主要包括图像的获取、图像的预处理和图像的特征提取与识别等.该系统的研究能够为工业烟尘污染的预防与控制策略的制定提供依据,有助于环境的保护.     

Robot guidance using computer vision

This paper describes a procedure for locating an autonomous robot vehicle in a three-dimensional space. The method is an extension of a two-dimensional technique described by Fukui. The method is simple, requires little computation and provides the unique three-dimensional position of the robot relative to a standard mark.     

Decoding depressive disorder using computer vision

Multimedia Tools and Applications - This paper intends to decode depressive disorder using computer vision. Facial expressions rendered by a depressive and non-depressive person were studied...     

基于机器视觉的高速公路车辆停车检测

为减少高速公路上车辆追尾等交通事故的发生,研究了基于机器视觉的车辆停车检测方法.检测系统采用当前帧和背景帧差分的方法实现了检测线圈范围内图像特征提取.分析了车辆从运动到静止的特征提取值图,并利用车辆存在阈值、车辆存在值、系统最小车辆存在值、系统不相似度阈值、系统相似度值、系统相似度最小值、车辆不存在值和系统最小车辆不存在值等参数实现了车辆停车检测.试验表明,该方法能够有效的将停靠在路面上的车辆检测出来.     

Structural matching in computer vision using probabilisticrelaxation

In this paper, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply, We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations     

基于计算机视觉的齿条测量方法研究

提出一种基于计算机视觉的齿条测量方法。建立一套计算机视觉的测量系统,系统把CCD摄像机安装于光栅反馈式精密导轨上,精密导轨带动CCD摄像头移动对齿条各处进行图像采集,通过对采集图像的处理,实现全齿的测量,获得齿条的基本参数如齿距p、齿厚s等数据。实验对指示表卡尺齿条进行测量,并与东京精密S1910DX3轮廓度仪的测量结果对比,结果表明,此方法的测量精度能满足该类齿条测量的要求。该测量方法简单实用,有效提高了卡尺齿条的测量效率,为卡尺齿条的在线检测提供技术保障。     

基于苹果着色面积的计算机视觉分级技术研究

通过数码相机获取不同苹果等级的彩色图像,将图像平滑后利用R颜色分量进行最小错误阀值分割,再进行腐蚀去噪声和孔洞填充,并将RGB值转换成HSL值,最后用像素点变换法恢复苹果在二维投影图中真实的几何信息,重新计算像素点的面积,计算苹果的着色面积并进行分级.实验结果表明,机器识别苹果等级的准确率为89.8%.     

A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.     

Experiences on developing computer vision hardware algorithms using Xilinx system generator

Computer vision is one of the areas where hardware-implemented algorithms perform clearly better than those implemented via software. Digital designers have so far optimized their designs by means of application specific integrated circuits (ASICs) or digital signal processors (DSPs). However, nowadays they are increasingly using field programmable gate arrays (FPGAs), powerful hardware devices combining the main advantages of ASICs and DSPs with the possibility of re-programming, which make them very attractive devices for rapid prototyping. This paper shows how the Xilinx system generator (XSG) environment can be used to develop hardware-based computer vision algorithms from a system level approach, which makes it suitable for developing co-design environments.     

Manual lifting task risk evaluation using computer vision system

Low back injury has been a significant problem in industry. Studies indicate that inappropriate design of manual material handling (mmh) task is one of the major factors causing low back pain. Here, an automated system of evaluating the possible risk of low back injury in mmh task is developed. The system applies computer vision technique to identify the working posture, then incorporating biomechanical model and anthropometric data to calculate the low back compression force. By comparing with the specified standard limits, the system can indicate the risk level of the task. Some recommendations will then be provided. The system has been validated by comparing with the existing 2-D software, and it is found to be valid and effecient.     

CAGD-based computer vision

The authors explore the connection between CAGD (computer-aided geometric design) and computer vision. A method for the automatic generation of recognition strategies based on the 3-D geometric properties of shape has been devised and implemented. It uses a novel technique to quantify the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a strategy tree, is accomplished. Strategy trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. The consist of selected 3-D features which satisfy system constraints and corroborating evidence subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses are explored. Experiments utilizing 3-D data are presented