搜索结果: 1-15 共查到“工学 machine vision”相关记录20条 . 查询时间(0.14 秒)
2017第十届机器视觉国际会议(2017 The 10th International Conference on Machine Vision)
2017 第十届 机器视觉 国际会议
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2017/5/23
Welcome to the official website for 2017 The 10th International Conference on Machine Vision (ICMV 2017). ICMV conference is initiated by School of Electronics, Si Chuan University, China, assisted by...
2017年第二届机器人与机器视觉国际会议(2017 2nd International Conference on Robotics and Machine Vision)
2017年 第二届 机器人 机器视觉 国际会议
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2017/5/23
We are excited to announce 2017 2nd International Conference on Robotics and Machine Vision (ICRMV 2017) will be held on September 15-18, 2017 in Kitakyushu, Japan.
SIMULATION OF THE «COSMONAUT-ROBOT» SYSTEM INTERACTION ON THE LUNAR SURFACE BASED ON METHODS OF MACHINE VISION AND COMPUTER GRAPHICS
Lunar Exploration Extravehicular Activity (EVA) Human-Robot Interaction (HRI) Mobile Robot Control "Follow Me" Mode Gesture Interface Object Tracking Gesture Recognition Motion Capture
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2017/6/19
Extravehicular activity (EVA) on the lunar surface, necessary for the future exploration of the Moon, involves extensive use of robots. One of the factors of safe EVA is a proper interaction between c...
2017 SPIE 自动视觉检查和机器视觉会议(2017 Conference on Automated Visual Inspection and Machine Vision)
2017 SPIE 自动视觉检查 机器视觉 会议
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2017/4/25
Conference Sessions:1: Image Acquisition 2: Simulation 3: Multispectral inspection Posters--Thursday 4: Inspection, Monitoring and Detection .
2017SPIE自动视觉检查和机器视觉会议(2017 Conference on Automated Visual Inspection and Machine Vision)
2017 SPIE 自动视觉检查和机器视觉 会议
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2017/4/25
2017SPIE自动视觉检查和机器视觉会议(2017 Conference on Automated Visual Inspection and Machine Vision)。
2017第十五届IAPR机器视觉应用国际会议(2017 Fifteenth IAPR International Conference on Machine Vision Applications)(MVA)
2017第十五届IAPR机器视觉应用国际会议
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2017/3/21
The Fifteenth IAPR International Conference on Machine Vision Applications will be held at Toyoda Auditorium, Nagoya University, Nagoya, Japan from May 8th through 12th, 2017. The conference is co-spo...
2017年英国机器视觉会议(British Machine Vision Conference)
2017年 英国机器视觉 会议
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2017/3/9
Authors are invited to submit full-length high-quality papers in image processing and machine vision. Papers covering theory and/or application areas of computer vision are invited for submission. Sub...
Close Range Photogrammetry - Structured Light Approach for Machine Vision Aided Harvesting
Close-range photogrammetry Synchronized digital cameras Industrial photogrammetry
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2015/12/14
The following paper describes a novel data acquisition system for machine vision aided harvesting, which can also be used to
provide data for various different applications. The system consists of tw...
Multistation Bundle Adjustment with a Machine Vision Parallel Camera System - an Alternative to the Perspective Case for the Measurement of Small Objects
Calibration Orientation Measurement
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2015/12/14
Photogrammetric measurement and modelling procedures are dependent on accurate camera calibration and orientation. The
accuracy of image-based registration and texture mapping approaches can be defic...
基于机器视觉的甘蔗茎节特征提取与识别(Recognition and Features Extraction of Sugarcane Nodes Based on Machine Vision)
甘蔗茎节 识别 机器视觉 支持向量机
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2010/12/29
为实现含有蔗芽的有效蔗种片段机器智能切断,引入机器视觉技术识别甘蔗茎节。以甘蔗图像HSV颜色空间的S分量经阈值分割、数学形态滤波处理作为模板,和H分量经阈值分割的反图像进行与运算得到合成图;将合成图划分为64个列块区域,提取质心比、粗度比和白点比等7个特征指标,再用支持向量机分类识别茎节与节间列块,得到茎节与节间的平均识别率为93.359%;对支持向量机分类出的茎节列块进行聚类分析,得到茎...
基于机器视觉的水稻种子精选技术(Rice Seeds Selection Based on Machine Vision)
水稻种子 精选 图像处理
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2010/12/29
以面积和宽长比作为稻种类型的特征参数,建立了稻种类型数据库;以等价矩形长、宽的差值最小为标准,进行了未知稻种类型的判断;以扫描线上黑白像素的变化次数和扫描线数来判断稻种的破裂;以不同阈值提取的稻种面积差来判断稻种是否霉变。选取丰源优299等10种稻种进行实验,分别进行了种子类型判断、工位有无种子判断、几何参数判断以及发霉与破损情况判断,检测正确率分别为100%、91.4%、88.9%和76.8%。
基于机器视觉的棉种破损检测技术(Detection of Damaged Cottonseeds Using Machine Vision)
棉种 破损 机器视觉
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2009/12/28
研究了破损棉种的机器视觉识别方法,采用均值、方差、均方比等统计特性参数,计算棉种边界破损参数。通过实验确定均方比分类阈值为0.58,将棉种分为破损棉种和正常棉种。选取正常棉种330粒、破损棉种110粒,利用该检测系统进行检测,其识别精度达93%。
基于机器视觉和信息融合的邻接苹果分割算法(Separating Adjoined Apples Based on Machine Vision and Information Fusion)
苹果 机器视觉 信息融合
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2009/11/24
提出了利用亮度和颜色的信息融合来分割邻接苹果的方法。首先使用Lab模型对苹果图像进行分割,然后计算分割后每个区域的面积,并判断其是否为邻接苹果区域。接着在邻接区域内计算亮度信息,利用亮度产生的亮斑对邻接苹果进行分割。这样,在邻接区域以外的部分,亮度信息产生的噪声被Lab模型的信息屏蔽,而邻接区域以内的部分,具有惟一性的亮度信息可以较好分割经Lab模型处理后的邻接苹果。实验表明,此算法对邻接苹果识别...
基于机器视觉的农业车辆路径跟踪(Path Tracking of Agricultural Vehicle Based on Machine Vision)
机器视觉 自动导航 随机霍夫变换 模糊逻辑
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2009/11/3
简述了一种基于机器视觉的农业车辆自动导航系统。提出了直线检测算法,显著降低了内存需求和时间消耗;以横向偏差和航向偏差作为输入量,构建了二维模糊决策器,对期望前轮转角进行决策;构建了基于PID的转向控制器,实现前轮转向控制,并采用简化的两轮车运动学模型进行了仿真。仿真和实验结果表明,该导航系统可以有效地实现直线路径跟踪。当车速为0.3m/s时,最大跟踪横向偏差不超过5cm,平均偏差不超过2cm;当车...
农用车辆作业环境障碍物检测方法(Obstacle Detection in the Working Area of Agricultural Vehicle Based on Machine Vision)
农用车辆 颜色分割 特征匹配
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2009/11/3
针对联合收获机视觉导航系统中的视觉测障,提出了一种基于单目彩色图像分割测障与立体视觉特征匹配测障相结合的测障方法:利用H、S颜色分量对单目图像实施固定阈值分割并二值化,获得潜在障碍物的位置及区域;采用尺度空间不变(SIFT)算法获取潜在障碍物区域特征;采用近似最近邻分类算法(ANN)进行快速特征匹配,获得潜在障碍物的世界坐标,由此进一步确认障碍物以及障碍物与车辆之间的距离。提出了提高算法效率的措施...