# Computer Vision-Based Approach for Reading Analog Multimeter
## 1 Introduction
Since reading the A-meter is dependent upon the indications of \(\theta_{s}\) and \(\theta_{p}\), we need two mapping functions, _mapping function for selector arrow_, \(F(\theta_{s})\), and _mapping function for pointer_, \(M(\theta_{p})_{F(\theta_{s})}\), to determine which function of the A-meter and which scale in \(\mathbf{R}_{IM}\) should be used, respectively. Once the \(F(\theta_{s})\) is known, the measurement result can then be obtained by \(M(\theta_{p})_{F(\theta_{s})}\). The related works are simply reviewed in the following text. Several methods have been developed to determine the angle of pointer, where the binary image obtained by image differencing and binarization has often been used for study. For example, in a previous study, two images with different pointer angles were required for image differencing operation to make an _instrument meter_ background image. The background image was used to operate image differencing again with the new position of pointer. Then, binarization and the Hough transform method were used to calculate the pointer angle [3; 4]. [PERSON] et al. collected the line intersection points to form a fitting curve which reflected the relationship between the abscissa values and the resistance values [5]. The method proposed by [PERSON] et al. [6] needed the meter to be perfectly horizontally lined up with the camera which would be troublesome for users. [PERSON] et al. described about the automatic recognition of analog and digital meters installed in a nuclear power plant [7]. They applied simple and elementary techniques, such as binarization, region labeling, and projection, to extract needles in analog meters and numeric codes in digital meters. Thresholding was performed to obtain the binary image. Region labeling was applied to extract the image elements. They used a simple run length technique to detect the needle in a meter area and calculated the run length from the center to the edge of the meter image as sweeping circularly. The position with the maximum run length value was selected as the needle position [7]. [PERSON] et al. used red light and green light to help detect the pointer for automobile meter calibration and an instrument qualification test before leaving the factory [8]. [PERSON] et al. studied an improved method of the Hough transform method to detect the pointer angle [9]. [PERSON] et al. used the centroid and tip coordinates of the pointer of a water meter to form a straight line and calculated the pointer angle between the \(x\)-axis and the line that was created by joining the tip and the centroid of the pointer [10]. [PERSON] and [PERSON] developed a preprocessing method to automatically read a water meter, where Fourier transform was used to correct a tilted dial image of a water meter [11]. Recently, to develop a Flight Guardian (FG) system which is not physically connected to an aircraft, [PERSON] et al. [12; 13] developed a simple method to automatically read the airspeed and revolutions per minute (RPM) of instruments where the video streams are acquired with two fixed-position camera devices. Based on the features of circular shape and needle locations in both dials, the focus of the camera devices is fixed at the center of this shape, and a circular-range sub-image composed of black pixels, except the white pixels occupied by the needle, are adopted for  
Figure 2: Flowchart of the proposed approach. processing. By converting the sub-image pixels into a one-dimensional vector, the needle's position is identified by finding the maximum convolution output. In practical applications of automatically reading analog meters, rotated/tilted/shifted situations under variant illumination need to be considered.