机器视觉技术捕获显示设备数据方法

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"这篇研究论文探讨了一种利用机器视觉从显示设备中捕获数据的方法，主要关注于数字识别和算法的开发。随着eHealth（电子健康）领域的发展，数字化个人健康统计数据的需求日益增长。许多现有的健康评估设备在显示结果后，用户无法方便地将这些数据保存为数字记录。该论文提出了一种直接将设备的数值显示器转化为数字记录的机器视觉技术。文中介绍了一个基于无线的机器视觉系统，该系统能够拍摄显示器的图像，并将其无线传输到远程计算机。首先，本地相机捕获显示器的图像，并通过无线方式将其发送到远程计算机。在远程计算机上，图像被转化为灰度和二值图像，以便进行进一步处理。接下来，计算机应用了基于SIFT（尺度不变特征变换）的跟踪算法来识别捕获图像中的数字。SIFT算法是一种强大的特征检测和描述方法，能够在不同尺度和旋转下保持不变性，从而在图像中准确识别出数字。在图像分割阶段，采用了watershed算法，这是一种常用的图像分割技术，用于将图像划分为多个区域，有助于区分和提取数字。论文中可能还涉及到了错误校正和噪声过滤的步骤，以确保从显示设备捕获的数据的准确性和可靠性。这些技术对于处理可能出现的图像模糊、光照变化或数字重叠等挑战至关重要。此外，该研究可能还讨论了系统性能的评估，包括识别速度、准确率和鲁棒性等关键指标。这有助于理解该方法在实际应用中的效果，并为进一步优化提供依据。这篇论文提供了一种创新的解决方案，通过机器视觉技术从显示设备捕获并转换数据，解决了当前健康监测设备数据存储的问题，为eHealth领域的数字化进程作出了贡献。"

Catching Data from Displayers by Machine Vision

Lifeng Yao

1,a

, Jianfei Ouyang

1,b

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University,

Tianjin 300072, China

yaolifeng2006@gmail.com,

oyj@tju.edu.cn

Keywords:

Data Catching

Machine Vision, Numeric Recognition, Algorithm

Abstract. With the emergence of eHealth, the importance of keeping digital personal health

statistics is quickly rising in demand. Many current health assessment devices output values to the

user without a method of digitally saving the data. This paper presents a method to directly translate

the numeric displays of the devices into digital records using machine vision. A wireless-based

machine vision system is designed to image the display and a tracking algorithm based on SIFT

(Scale Invariant Feature Transform) is developed to recognize the numerals from the captured

images. First, a local camera captures an image of the display and transfers it wirelessly to a remote

computer, which generates the gray-scale and binary figures of the images for further processing.

Next, the computer applies the watershed segmentation algorithm to divide the image into regions

of individual values. Finally, the SIFT features of the segmented images are picked up in sequence

and matched with the SIFT features of the ten standard digits from 0 to 9 one by one to recognize

the digital numbers of the device’s display. The proposed approach can obtain the data directly from

the display quickly and accurately with high environmental tolerance. The numeric recognition

converts with over 99.2% accuracy, and processes an image in less than one second. The proposed

method has been applied in the E-health Station, a physiological parameters measuring system that

integrates a variety of commercial instruments, such as OMRON digital thermometer, oximeter,

sphygmomanometer, glucometer, and fat monitor, to give a more complete physiological health

measurement.

Introduction

With the emergence of eHealth, the importance of keeping digital personal health statistics is

quickly rising in demand. Many current health assessment devices output values to the user without

a method of digitally saving the data. They are including digital thermometer, oximeter, and fat

monitor etc.

In order to output the measurement results in the case of the electronic measuring devices have

no standard output interface, the image processing method based on machine vision is presented,

which can realize the auto-recognition of the numeric displaying on the devices. While acquiring

the images of the display screen, which is often affected by uneven illumination, so that the

acquired images are with gray uniform and mutation. Sometimes, because of the shooting location

could not be kept being vertical with the display screens, but with a certain angle, the images have

different perspectives and tilt angles. That is, affected by the factors like illumination, perspective

and scaling, the acquired images are with non-uniform gray, ranging in size and with a certain

degree of tilt, making it difficult to recognize accurately the numeric displayed on display screens.

There are many methods to recognizing the numeric, such as the pixel-based neural network [1]

and the identification method based on the topological structure of characters, which including the

threading methods. However, SIFT is a matching algorithm based on the scale invariant features of

images. SIFT feature matching algorithm [2] is presented by David Lowe through summing up the

proposed feature detection method [3] based on invariant features in 2004. The algorithm is

extracting the features on DOG scale-spaces and the 2D image space. The features are invariant to

image scaling and rotation, and partially invariant to change in illumination and 3D camera

viewpoint even to affine transformation. The algorithm is well-matching, and the features extracted

by which methods is stable. It can match two images well, while translation, rotation, affine

transformation, perspective transformation, illumination change between them, even the images are

shooting at any angle. In a word, it can match the two images in large differences with features.

Advanced Materials Research Vol. 566 (2012) pp 124-129

doi:10.4028/www.scientific.net/AMR.566.124

www.ttp.net. (ID: 202.113.1.151-24/07/12,15:17:42)

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