# Computer Vision-Based Approach for Reading Analog Multimeter
## 1 Introduction  
Multimeters are significant instruments for sensing or measuring electronic parameters (e.g., voltage, current, resistance) and are indispensable to the fields of science and technology. Moreover, they usually appear in our daily life, e.g., factory, school laboratory, home tools, and so on. Although the digital multimeter has been widely used for several decades due to the considerations of precision and cost, the analog multimeter is still preferable in many applications, in particular, for monitoring promptly varying values and easy understanding. The disadvantage of the traditionalanalog multimeter (abbreviated as A-meter for short) is the lack of data communication interface within it to allow further data processing. Therefore, reading an A-meter usually relies on human eyes with two obvious problems--inefficiency and easy fatigue--while visual inspection onto an A-meter is needed for a long period of time. Therefore, as the goal of this study, from the viewpoint of optical sensor application, we develop a computer vision-based method to automatically read the meter value so that the mentioned drawbacks of A-meter can be overcome. Figure 1a illustrates a scenario where a 1 K\(\Omega\) resistor is being measured with the A-meter model YF-370A [1], which is used in this study. The 1 K\(\Omega\) resistor apparently shown in Figure 1b can be identified by the four-band of colors (brown-black-red-gold) which represents \(10\times 10^{2}\pm 5\%\) (\(\Omega\)) as studied in reference [2]. According to human operation and visual inspection, in this case, the selector arrow of the function selector is switched to the location of \(\times 10\) (see also Figure 1c) and the pointer in instrument meter indicates around 100 with the \(\Omega\)-scale (see also Figure 1d). The resistance about 1000 \(\Omega\) is thus obtained by \(100\times 10\) (\(\Omega\)). In fact, the result obtained by human visual inspection is an approximate value due to the viewing angle and the nonlinear \(\Omega\)-scale, as demonstrated in Figure 1d. Despite such an approximate measurement obtained from an A-meter, from the viewpoint of practical application, it is convenient to let an engineer quickly observe an electronic component characteristic or understand a circuit system's behavior for instance. According to this philosophy of people using the A-meter, the proposed approach based on computer vision can be simply introduced as follows. Figure 2 shows the flowchart of our computer vision-based approach. In order to facilitate the effective angle detections in our method, the A-meter image (say original image or I\({}_{\text{org}}\)) should be adjusted first in the horizontal orientation using the _horizontal alignment_ stage, and the adjusted image can be obtained as I\({}_{\text{adj}}\). With the edge-based geometric matching and pyramidal gradient matching techniques, the region of the instrument meter (R\({}_{IM}\)) can then be located by the _instrument meter  
Figure 1: (**a**) Illustration of people using the A-meter (model YF-370A) to measure a 1 K\(\Omega\) resistor. (**b**) The measured resistor can be identified by the four-band of colors (brown-black-red-gold) which represents \(10\times 10^{2}\pm 5\%\) (\(\Omega\)) [2]. Note that (**c**) the selector arrow of the function selector is switched to the location of \(\times 10\) and (**d**) the pointer in the instrument meter indicates around 100 by observing the nonlinear \(\Omega\)-scale. region detection_ stage. Because the pointer appearing in the \(\mathbf{R}_{IM}\) is needle-shaped (refer to Figure 1d), the angle (\(\theta_{p}\)) of pointer can be detected at the _angle detection of pointer_ stage. Similarly, the angle (\(\theta_{s}\)) of the selector arrow appearing in the adjusted image \(\mathbf{I}_{adj}\) can be detected at the _angle detection of selector arrow_ stage.