Scan Camera Research Articles

Precise-robust position measurement of a linear motor based on the extended speed-up robust features algorithm and sawtooth stripe pattern.

To improve the accuracy and robustness of linear motor mover position detection, a linear motor displacement measurement method is proposed based on the extended speed-up robust features algorithm and sawtooth stripe image. First, a sawtooth stripe image is constructed as the target image. To optimize the target image with strong robustness, the spatial frequency and image standard deviation are introduced as the image quality evaluation indices. Second, a line scan camera fixed on the linear motor is used to capture the target image signals in real time. The sequential sawtooth stripe signals are preprocessed by filling sampling to improve the matching rate of feature points. To satisfy the real-time requirement of mover position detection, the singular value decomposition is used to reduce the dimension of the preprocessed image. Subsequently, an improved speed-up robust features algorithm is used to achieve sub-pixel displacement measurement. Finally, the actual displacement of the mover can be calculated by the calibration coefficient of the measurement system. Simulation and comparative experiments show that the proposed sawtooth stripe image has better robustness, in contrast with the fence image and aperiodic sinusoidal image in references. It is also demonstrated that the proposed method has higher accuracy and anti-interference performance than other methods under different conditions.

Multi‐angle measurement device for analysis of coating appearances

AbstractMaterial appearance is an important factor in product design because it tends to attract consumers. Effect coatings that contain aluminum and interference pigments are widely used in automobiles. The color and sparkle values of these coatings vary with the observation angle. Several methods have been proposed to study these variations with respect to the observation angle. However, a high angular resolution is required to understand human perception. Thus, we developed a measurement device that can analyze the color and sparkle values at a high angular resolution for multiple angles. The device captures the sample surface by using a line scan spectral camera. Because the lighting device rotates around the sample while the line scan camera is capturing images, each scan line that makes up the image contains different angle information. A comparison was performed among the results obtained from this study, a commercially available device, and a subjective value of sparkle perception. Twenty samples were required to confirm that the sparkle values calculated from this study correlated closely with the subjective scores. In addition, the difference between the color values obtained from the BYK‐mac i spectrophotometer and the proposed technique was less than 3.5 (ΔE00). The findings of this study will help in developing the appearance of effective coatings. In addition, using evaluation values, quality control, and improvement are possible after mass production of coatings.

Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance

BackgroundDrought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.ResultsHere we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding.ConclusionOur study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

A stitching algorithm for automated surface inspection of rotationally symmetric components

This paper provides a novel approach to stitching surface images of rotationally symmetric parts. It presents a process pipeline that uses a feature-based stitching approach to create a distortion-free and true-to-life image from a video file. The developed process thus enables, for example, condition monitoring without having to view many individual images. For validation purposes, this will be demonstrated in the paper using the concrete example of a worn ball screw drive spindle. The developed algorithm aims at reproducing the functional principle of a line scan camera system, whereby the physical measuring systems are replaced by a feature-based approach. For evaluation of the stitching algorithms, metrics are used, some of which have only been developed in this work or have been supplemented by test procedures already in use. The applicability of the developed algorithm is not only limited to machine tool spindles. Instead, the developed method allows a general approach to the surface inspection of various rotationally symmetric components and can therefore be used in a variety of industrial applications. Deep-learning-based detection Algorithms can easily be implemented to generate a complete pipeline for failure detection and condition monitoring on rotationally symmetric parts.

Penggunaan Algoritma Color-Based Filtering Sebagai Pendeteksi Nominal Pada Uang Kertas

As a legal tender, banknotes have several drawbacks in terms of their use. for people with visual impairments they have, it is very likely that they will be confused, misplaced, and even some ignorant people take advantage of their weaknesses in using the money. Based on this problem, in this paper we propose to make a banknote detector with sound output. The method used for the nominal identification process is the color based filtering algorithm using the Pixy2 CMUcam5 camera and Arduino as the data processor. Tests were carried out on 1000, 2000, 5000, 10000, 20000 and 50000 banknotes. From the test results, it was found that the success rate was 90% with the optimal distance for the camera scan process being 7 to 10 cm in an average time of about 5.7 seconds.

Automatic subway tunnel crack detection system based on line scan camera

Automatic visual inspection of cracks on concrete surface has become increasingly important for the health diagnosis of subway tunnel structures to prevent catastrophic failures. This paper designs an on-board image acquisition system using multiple line scan cameras to capture the full-section images of tunnel surface and proposes a robust automatic crack detection method for noisy and complicated tunnel surface images. The detection method consists of three steps. The first step is a crack enhancing process by using morphological image processing and frequency-domain enhancing algorithm. In the second step, a multistage fusion filtering algorithm is designed to filter out background noise and interference from images of water stain and various railway devices on the tunnel surface. The third step is to use an improved seed growth algorithm to segment real cracks from the noisy background. The detection system is tested in the Beijing Metro Line 1, and the results demonstrate that the proposed method outperforms existing methods that are only robust for images of small area with a simple background.

Cytotoxic CD8+ T cells and tissue resident memory cells in colorectal cancer based on microsatellite instability and BRAF status.

BACKGROUNDRecent studies in non-colorectal malignancy have associated T resident memory (TRM) cells with improved patient survival. It is unknown if TRM plays a role in colorectal cancer (CRC).AIMTo examine the potential role of TRM cells in providing immunogenicity in CRC stratified by microsatellite instability (MSI) and BRAF status.METHODSPatients with known MSI and BRAF mutation status were eligible for inclusion in this study. CRC tumour sections stained with haematoxylin and eosin were microscopically reviewed and the images scanned prior to assessment for location of invading edge and core of tumour. Sequential sections were prepared for quantitative multiplex immunohistochemistry (IHC) staining. Opal Multiplex IHC staining was performed with appropriate positive and negative controls and imaged using a standard fluorescent microscope fitted with a spectral scanning camera (Mantra) in conjunction with Mantra snap software. Images were unmixed and annotated in inForm 2.2.0. Statistical analysis was performed using Graphpad Prism Version 7 and Stata Version 15.RESULTSSeventy-two patients with known MSI and BRAF status were included in the study. All patients were assessed for MSI by IHC and high resolution capillary electrophoresis testing and 44 of these patients successfully underwent quantitative multiplex IHC staining. Overall, there was a statistically significant increase in CD8+ TRM cells in the MSI (BRAF mutant and wild type) group over the microsatellite stable (MSS) group. There was a statistically significant difference in CD8+ TRM between high level MSI (MSI-H):BRAF mutant [22.57, 95% confidence interval (CI): 14.31-30.84] vs MSS [8.031 (95%CI: 4.698-11.36)], P = 0.0076 andMSI-H:BRAF wild type [16.18 (95%CI: 10.44-21.93)] vs MSS [8.031 (95%CI: 4.698-11.36)], P = 0.0279. There was no statistically significant difference in CD8 T cells (both CD8+CD103- and CD8+CD103+TRM) between MSI-H: BRAF mutant and wild type CRC.CONCLUSIONThis study has shown that CD8+ TRM are found in greater abundance in MSI-H CRC, both BRAF mutant and MSI-H:BRAF wild type, when compared with their MSS counterpart. CD8+ TRM may play a role in the immunogenicity in MSI-H CRC (BRAF mutant and BRAF wild type). Further studies should focus on the potential immunogenic qualities of TRM cells and investigate potential immunotherapeutic approaches to improve treatment and survival associated with CRC.

Design and sorting of an object identification on machine vision by using line scan camera

This paper illustrates the design of a system to identify objects on a conveyor belt using machine vision. In the present study, a machine vision based on one line scan sorting was developed, the purpose being to sort objects based on various stages of maturity. Many different methods are available for object identification. But we made design a system that separates and counting them. Different objects placed on the conveyor belt moves along, a camera placed above the belt takes real-time video and feeds it to the MATLAB software for processing the object to compare with the basic template object. The vision camera understands an object based on its physical attributes, such as shape and size for effectively controlling the hardware, which will use in this work. Besides, the number of objects of a particular section that cross the conveyor to demonstrate the identification of moving objects is counted and displayed. A low-speed conveyor belt is manufactured with various test objects that pass through it. For identifying a good object, the wavelength data is used, determining the way to match the geometric patterns and to identify the dimensions, and edge detection is applied. The ability to count specific attributes objects is testing different test paths. The sorting of objects using machine vision was performed using an algorithm of pattern matching of machine vision. A pattern image template was built and stored in a computer's memory. When the object is sorting the application run, the camera receives the image of the object into MATLAB. The vision application investigates the image and transfers it to the classifier if the received image matches the model image or not matches.

Design a smart infrastructure monitoring system: a response in the age of COVID-19 pandemic

Since the end of 2019, COVID-19 has been a challenge for the world, and it is expected that the world must take precautionary steps to tackle the virus spreading prior produces an efficient vaccine. Currently, most government efforts seek to avoid disseminating the coronavirus and forecast probable hot areas. The most susceptible to coronaviral infection are the healthcare staff due to their daily contact with potential patients. This article proposes a COVID-19 real-time system for tracking and identifying the suspected cases using an Internet of Things platform for capturing user symptoms and notify the authority. The proposed framework addressed four main components: (1) real-time symptom data collection via thermal scanning algorithm, (2) facial recognition algorithm, (3) a data analysis that uses artificial intelligence (AI) algorithm, and (4) a cloud infrastructure. A monitoring experiment was conducted to test three different ages, kid, middle, and older, considering the scanning distance influence compared with contact wearable sensors. The results show that 99.9% accuracy was achieved within a (500 ± 5) cm distance, and this accuracy tends to decrease as the distance the camera scanning and objects increased. The results also revealed that the scanning system's accuracy had been slightly changed as the environmental temperature dropped lower than 27 °C. Based on the high-temperature presence's simulated environment, the system demonstrated an effective and instant response via sending email and MQTT message to the person in charge of providing accurate identification of potential cases of COVID-19.

Q-Bot: automatic DICOM metadata monitoring for the next level of quality management in nuclear medicine

BackgroundRegular and precise inspection of the realization of the local nuclear medicine standard operation procedures (SOPs) is very complex and time-consuming, especially when large amount of patient data is obtained from a wide scale of different scan procedures on a daily basis. DICOM metadata comprise a complete set of data related to the patient and the imaging procedure, and consequently all information necessary to evaluate the compliance with the actual SOP.MethodsQ-Bot, an automatic DICOM metadata monitoring tool which is capable to verify SOP conformities, was tested for 11 months at two nuclear medicine departments. Relevant parameters, such as patient ID, patient mass and height, injected activity, and uptake time, were investigated in the case of adult 18F-FDG whole-body PET/CT and 99mTc-MDP gamma camera bone scans on a daily basis. Q-Bot automatically inspected the actual SOP compliance of these relevant DICOM parameters. Q-Bot graphical user interface (GUI) provided a summary of the outliers in a table format to be investigated by a dedicated technologist. In addition, information related to the error handling was also collected for retrospective analysis of long-term tendencies.ResultsIn total, 6702 PET/CT and 2502 gamma camera scans were inspected, from which 8581 were confirmed as valid patient study without errors. Discrepancies related to the lack of a parameter, not appropriate format, or improper scan procedures were found in 623 cases, and 156 out of these were corrected before the medical reading and reporting. SOP non-conformities explored with Q-Bot were found to be non-correctable in 467 cases. Systematic errors to our practice turned out to be the manual radiopharmaceutical injection, the allowance to use both SI and non-SI units, and the clear definition of decimal point symbol to use.ConclusionThe daily evaluation of Q-Bot results provided early detection of errors and consequently ensured the minimization of error propagation. Integration of a QM software that inspects protocol compliance at a nuclear medicine department provides significant support to detect non-conformities for technologists, and much higher confidence in image quality for physicians.

Implementation of CNN for Plant Leaf Classification

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

Opera and the Built Environment

In June 2020 one more video was released into the all-accommodating cloud. This one shows a concert addressed to 2,292 plants, one in each seat of a red velvet-lined auditorium at the Teatre del Liceu in Barcelona. These hand-selected plants are the leafy audience at a performance of Puccini's ‘Crisantemi’ string quartet, conducted in honour of healthcare workers amid lockdown measures to slow the spread of COVID-19. Once the usual announcements about silencing cell phones have been made, the camera closes in on four musicians as each bows to the verdant audience and takes a seat. When the music starts, our view advances from behind the musicians into the opera house: the camera scans the initial rows of the orchestra stalls, then moves into the boxes and balconies. In each successive section of the theatre we see the avatars chosen to listen in place of us. Our representatives are docile and beatific – Puccini seems to soothe them. For a moment the wondrous intrusion of the outside world indoors even starts to seem natural, as if the auditorium can hold the whole world within it, as if there is no outside to this windowless world.

Image-based surface scratch detection on architectural glass panels using deep learning approach

Abstract As a transparent and traditional building material, glass products such as glass facade are vital components of buildings. However, the surface scratches generated in the manufacturing process or emerging in the service stage such as windborne debris impacts may lead to remarkable strength degradation of glass material. In order to assess the fracture possibility of glass components, the size and number of scratches should be monitored during their lifecycle. Automatic scratch detection of architectural glass therefore remains a necessary task for civil engineers. A pixel-level instance segmentation model using Mask and region-based convolutional neural network (Mask R-CNN) was proposed for scratches detection on transparent glass surface. Images with scratches were firstly collected by a tailor-made automated microscopic camera scanning system to build the training and validation dataset. Test results demonstrate that the trained network is satisfactory, achieving a mean average precision of 96.5% with low missing and false rate under background interference. A comparison between the proposed model and another segmentation method YOLACT indicates that the proposed model has better performance in both detection and segmentation accuracy. The proposed deep learning-based approach can better support the development of non-contact defect assessment techniques for transparent building materials such as glass.

Effect of ZnO nanoparticles on optical textures and image analysis properties of 7O.O5 liquid crystalline compound

Abstract The systematic studies are carried out on liquid crystalline N-(p-n-heptyloxybenzylidene)-p-n-pentyloxy aniline (7O.O5) pure compound and with dispersion of ZnO nanoparticles in 1 wt% respectively. The phase transition studies of the pure and LC nanocomposite are carried out using Polarizing Optical Microscope (POM) attached with hot stage along with digital camera and Differential Scanning Calorimetry (DSC) is used to measure enthalpy and transition temperature values. Further the presence of ZnO nano particles in 7O.O5 is determined by Scanning Electron Microscopy (SEM) analysis. It is identified that due to dispersion of nano particles SmG phase is Quenched and nematic thermal range is increased. To enhance the captured textural images, an algorithm that uses tuned intensification operators have been proposed in this article. Statistical parameters for enhanced output images are calculated and compared with existing algorithms such as Adaptive Gamma Correction (AGC) and Discrete Wavelet Transform – Singular Value Decomposition (DWT-SVD) to understand the effectiveness of proposed algorithm. Further, optical parameters have been computed to understand the thermo optical nature of liquid crystalline compounds.

3D garment model reconstruction based on scattered point cloud

We describe a method to quickly reconstruct 3D garment model. We first use a depth camera scanning device to quickly obtain 3D point cloud data on the surface of the clothing, pre-process the point cloud data. Then combining the region growth algorithm and Delaunay triangulation algorithm, we propose a surface reconstruction algorithm that takes undirected point clouds as input and generates interpolated surfaces in the form of triangulation to reconstruct the clothing model. We build this systematic 3D garment modeling program. Through our method we obtain a uniformly distributed grid, smooth patches, and also retain the original shape of the clothing point cloud. Our method increases the convenience of garment modelling, reduces the manpower and time for garment modeling, helps expand the garment database of the virtual fitting system, and provides ideas for garment electronic sales.

Snow depth time series retrieval by time-lapse photography: Finnish and Italian case studies

Abstract. The capability of time-lapse photography to retrieve snow depth time series was tested. Historically, snow depth has been measured manually by rulers, with a temporal resolution of once per day, and it is a time-consuming activity. In the last few decades, ultrasonic and/or optical sensors have been developed to obtain automatic and regular measurements with higher temporal resolution and accuracy. The Finnish Meteorological Institute Image Processing Toolbox (FMIPROT) has been used to retrieve the snow depth time series from camera images of a snow stake on the ground by implementing an algorithm based on the brightness difference and contour detection. Three case studies have been illustrated to highlight potentialities and pitfalls of time-lapse photography in retrieving the snow depth time series: Sodankylä peatland, a boreal forested site in Finland, and Gressoney-La-Trinité Dejola and Careser Dam, two alpine sites in Italy. This study presents new possibilities and advantages in the retrieval of snow depth in general and snow depth time series specifically, which can be summarized as follows: (1) high temporal resolution – hourly or sub-hourly time series, depending on the camera's scan rate; (2) high accuracy levels – comparable to the most common method (manual measurements); (3) reliability and visual identification of errors or misclassifications; (4) low-cost solution; and (5) remote sensing technique – can be easily extended in remote and dangerous areas. The proper geometrical configuration between camera and stake, highlighting the main characteristics which each single component must have, has been proposed. Root mean square errors (RMSEs) and Nash–Sutcliffe efficiencies (NSEs) were calculated for all three case studies comparing with estimates from both the FMIPROT and visual inspection of images directly. The NSE values were 0.917, 0.963 and 0.916, while RMSEs were 0.039, 0.052 and 0.108 m for Sodankylä, Gressoney and Careser, respectively. In terms of accuracy, the Sodankylä case study gave better results. The worst performances occurred at Careser Dam located at 2600 m a.s.l., where extreme weather conditions and a low temporal resolution of the camera occur, strongly affecting the clarity of the images.

High-speed Inline Computational Imaging for Area Scan Cameras

For quality inspection in different industries, where objects may be transported at several m=s, acquisition and computation speed for 2d and 3d imaging even at resolutions in the micrometer (mm) scale is essential. AIT's well-established Inline Computational Imaging (ICI) system has until now used standard multilinescan cameras to build a linear light field stack. Unfortunately, this image readout mode is only supported by few camera manufacturers thus effectively limiting the application of ICI software. However, industrial grade area scan cameras now offer frame rates of several hundred FPS, so a novel method has been developed that can match previous speed requirements while upholding and eventually surpassing previous 3D reconstruction results even for challenging objects. AIT's new area scan ICI can be used with most standard industrial cameras and many different light sources. Nevertheless, AIT has also developed its own light source to illuminate a scene by high-frequency strobing tailored to this application. The new algorithms employ several consistency checks for a range of base lines and feature channels and give robust confidence values that ultimately improve subsequent 3D reconstruction results. Its lean output is well-suited for realtime applications while holding information from four different illumination direction. Qualitative comparisons with our previous method in terms of 3d reconstruction, speed and confidence are shown at a typical sampling of 22mm=pixel. In the future, this fast and robust inline inspection scheme will be extended to microscopic resolutions and to several orthogonal axes of transport.

In-vivo imaging for assessing tumor growth in mouse models of ocular melanoma

Uveal melanoma (UM) and conjunctival melanoma (CM) are ocular malignancies that give rise to life-threatening metastases. Although local disease can often be treated successfully, it is often associated with significant vision impairment and treatments are often not effective against metastatic disease. Novel treatment modalities that preserve vision may enable elimination of small tumors and may prevent subsequent metastatic spread.Very few mouse models of metastatic CM and UM are available for research and for development of novel therapies. One of the challenges is to follow tumor growth in-vivo and to determine the right size for treatment, mainly of the posterior, choroidal melanoma. Hence, the purpose of this study was to establish a simple, noninvasive imaging tool that will simplify visualization and tumor follow-up in mouse models of CM and UM.Tumors were induced by inoculation of murine B16LS9 cells into the sub-conjunctival or the choroidal space of a C57BL/6 mouse eye under a surgical microscope. Five to ten days following injection, tumor size was assessed by Phoenix MicronIV™ image-guided Optical Coherence Tomography (OCT) imaging, which included a real-time camera view and OCT scan of the conjunctiva and the retina. In addition, tumor size was evaluated by ultrasound and histopathological examination of eye sections.Tumor growth was observed 5–9 days following sub-conjunctival or sub-retinal injection of seven-thousand or seventy-thousand cells, respectively. A clear tumor mass was detected at these regions using the MicronIV™ imaging system camera and OCT scans. Histology of eye sections confirmed the presence of tumor tissue. OCT allowed an accurate measurement of tumor size in the UM model and a qualitative assessment of tumor size in the CM model. Moreover, OCT enabled assessing the success rate of the choroidal tumor induction and importantly, predicted final tumor size already on the day of cell inoculation.In conclusion, by using a simple, non-invasive imaging tool, we were able to follow intraocular tumor growth of both CM and UM, and to define, already at the time of cell inoculation, a grading scale to evaluate tumor size. This tool may be utilized for evaluation of new mouse models for CM and UM, as well as for testing new therapies for these diseases.

Analysis of Spatial Vibration on Scanning Mirror Imaging Based on Frequency Filtering

In order to analyze the impact of multisource vibration on the imaging quality of aerospace optical payload during camera scanning, a dynamic imaging quality degradation model fused with satellite vibration filter templates is established. Firstly, according to the motion scanning process of the TDI camera, a filter template combining the amplitude and frequency of the vibration source is designed; Secondly, a piecewise function of satellite vibration with a frequency range of 1 Hz to 1000 Hz is obtained by curve fitting, and the amplitudes of different frequency ranges are brought into the image filter template to simulate the output image affected by the vibration. Finally, the output degraded image is analyzed through structural similarity and mean square error. The simulation results show that during the imaging process of the aerospace scanning mirror, the line frequency of the camera changes regularly with the scanning speed. When platform vibration frequency is similar to the camera’s exposure frequency, it will have a large impact on image quality. Therefore, when the camera’s exposure frequency is 110 Hz, vibration at a frequency of 110 Hz will further degrade the image quality. This article is expected to provide guidance for spatial vibration suppression and image quality degradation assessment in the imaging process of aerospace scanning mirrors.

A Precise Calibration Method for Line Scan Cameras

With the advantages of ultrahigh resolution and acquisition rate, line scan cameras are increasingly being deployed for precision measurement. In order to achieve high precision, the intrinsic parameters must be calibrated properly. However, the existing calibration models that only calibrate the principle points and the distortion in 1-D direction and ignore the other directions result in limited adaptability and accuracy, which are especially serious for multiline cameras. To conquer this challenge, this article proposes a precise calibration method that is qualified for both multiline cameras and single-line cameras. Through the analysis of the relative position relationship between the pixel array and principle point, a method for calibrating the principle points and the distortion in 2-D direction is proposed and a flexible calibration device is designed based on a theodolite. The calibration quality is improved by the novel calibration method that effectively establishes the correspondence relationship between the rays and pixels by recording the incident angle of the collimated beam and its image, respectively. Moreover, a robust optimization algorithm is proposed to enhance the stability of parameter estimation. The proposed method is verified by Monte-Carlo simulation, real experimental data, and flight. The results show that the 2-D calibration model can improve the calibration accuracy of the multiline cameras by 45 pixels at most, and its reprojection measurement uncertainty is as low as 0.1 pixels. The comparative results of the experiments demonstrate that the proposed method has the advantages of high accuracy, low cost, and easy implementation.