Calculation Of Transformation Matrix Research Articles

Developing an image processing pipeline to improve the position accuracy of single UAV images

Unmanned aerial vehicle (UAV) based remote sensing has been extensively used in precision agriculture applications, such as vegetation growth and health monitoring, yield estimation, and irrigation management. Conventional procedures for UAV data collection and processing require collecting highly overlapped images, stitching images to generate an orthomosaic, and using ground control points (GCPs) in the field or UAV onboard real-time-kinematic (RTK) global navigation satellite system (GNSS) data to improve position accuracy. For improving efficiency, a previous study developed a framework to process individual UAV images for mapping cotton emergence. The current study aimed to build a near-real time image processing pipeline to further improve the positioning accuracy of single UAV images. The improved image processing pipeline comprised feature detection and matching, false matches removal, geometric transformation matrix calculation, crop row alignment, image position assignment, and mapping. The developed pipeline was tested for mapping in both cotton and corn fields. Results showed that the position accuracies for measuring the distance between GCPs were 0.32 ± 0.21 m and 0.57 ± 0.28 m in a cotton and a corn field, respectively, when compared to ground truth data collected with an RTK-GNSS. The developed pipeline did not require GCPs in the field or image post-processing steps, such as image mosaicking and feature extraction, which allowed processing in near-real time and may possibly be implemented in real-time using an onboard edge computing system. The pipeline was used to map emergence parameters for cotton and corn fields, including stand count, canopy area, mean days to imaging after emergence, and plant spacing standard deviation. These maps demonstrated the success of the developed methods in providing a low-cost near real-time tool (8.6 and 3.6 s/image for the cotton and corn fields, respectively) for mapping emergence parameters at field-scale for use in both research and agricultural production.

Image Registration Based on Camera Calibration for Dual-Wavelength Retinal Oximetry

Retinal images at 570nm and 600nm are simultaneously captured by retinal oximetry. However, there exists misalignment between the two images, which should be adjusted before they are used to analyze hemoglobin oxygen saturation (SO2). Having a robust, accurate and fast registration scheme can ensure accuracy and speed of SO2 calculation. However, the existing retinal image registration methods cannot meet demand for clinical application of retinal oximetry. For example, errors in the vessel segmentation from each image cause corresponding errors in the registration, point matches may become unreliable when the predefined matching cost is corrupted by pathological retinal images. And it is always a time-consuming task. In this paper, we first analyzed the misalignment reason between dual-wavelength images, and concluded that there only exists the rotation and translation deviation between the two images caused by a mounting deviation of CCD cameras. The parameters were only related to system itself. Based on this, we proposed a registration method based on camera calibration that benefits from feature extraction and affine transformation matrix calculation. The parameters obtained by the proposed method were tested on 80 pairs of retinal images of 5 healthy volunteers and the results showed that the proposed method can successfully register the dual-wavelength images. The proposed registration method can significantly improve SO2 calculation speed while ensuring accuracy.

Interdependent quality control of collocated seismometer and accelerometer

A new method for the interdependent quality control (IQC) of a collocated seismometer and accelerometer is presented. It is useful for seismic stations, where broadband seismometer and strong-motion accelerometer are installed side by side. The number of this type of seismic stations is growing and so is the number of activities associated with the quality control. With the IQC, the collocated seismometer and accelerometer are controlled with the single procedure. It is based on the calculation of transformation matrix which numerically transforms the detection of a seismic signal of accelerometer to the space of detection of seismometer. This matrix contains a lot of information; among them were also the “orientation misalignment,” “sensitivity corrections,” and two indicators, from which the quality of data and the error of systems can be identified. The procedure uses seismic signals, detected by both systems. The self-noise of commercial accelerometers is usually higher than the average seismic noise; therefore, a moderate shaking of the ground is needed. This type of shaking can come from stronger regional earthquakes. In this article, a mathematical description of the procedure is introduced first; then, a few examples with real data are presented. A critical error was discovered with the help of this algorithm. It cannot be identified by testing signals, injected from an acquisition unit into the STS-2 seismometer. There is also an example of serious orientation misalignment between a seismometer and an accelerometer.

Automatic Calculation of a Transformation Matrix Between Two Frames

This paper presents a novel strategy for the automatic calculation of a homogeneous transformation matrix between two frames given a set of matched position measurements of objects as observed in both frames. The transformation matrix is calculated by applying a linear regression to matched data in two coordinate frames. This method scales linearly with the number of data points, enabling it to be used in a wide variety of applications without requiring large amounts of processing time. The automatic transformation matrix calculation is demonstrated to work in a real-world application and produce deterministic results. The error on the transformation matrix calculation was found to be low, making this an ideal method for the easy calculation of transformation matrices.

The investigation on sound souce indentification in semi‐space by NAH

The near field acoustic holography (NAH) theory model in semi‐space has been constructed on the foundation of NAH theory in full‐space. The technique of semi‐space acoustic field reconstruction by NAH has been developed on Fast Fourier Transform Algorithm. This method has considered the water surface as soft boundary condition. The equivalent free sound field can be acquired if the measured sound field has been handled dissymmetrically by the principle of the mirror. This method can overcome the disadvantages of the boundary element method which can be used to resolve the problems of sound source identification in semi‐space, such as the using of the semi‐space Green function, complex transformation matrix calculation, more time consummation and singularity solution existence. This method has been verified by numerical calculation and by experiment respectively. The experimental frequency is between 3 and 10 kHz. The distance between the hologram plane and source plane is 3 centimeter. The results show that it's an efficiency way to deal with the problem of sound source identification in semi‐space.