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<!DOCTYPE HTML>
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Zheng Wang, PH.D
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<article id="calibration" class="wrapper style1">
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<h2> Zoom-Lens Calibration </h2>
<p class="large" align="left">A reasearch project of zoom-lens camera calibration in close-range photogrammetry and computer vision. Application of calibrated zoom-lens cameras to render LiDAR (Light Detection and Ranging) point clouds with the highest-quality photos taken at the best camera settings.</p><hr>
<h4 align="left">Contents:</h4>
<ul>
<li style="margin:10px" align="left">Background</li>
<li style="margin:10px" align="left">Zoom lens calibration with zoom- and focus-related intrinsic parameters applied to bundle adjustment</li>
<li style="margin:10px" align="left">A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration</li>
<li style="margin:10px" align="left">LiDAR point cloud rendering with calibrated zoom-lens cameras</li>
</ul><hr>
<h3 align="left">Background</h3>
<p class="large" align="left">Zoom-lens cameras offer significant levels of versatility in comparison with their prime lens counterparts. Apart from changing focus and aperture settings, a zoom-lens camera is also capable of adjusting zoom settings to suit different FOV and depths of field (DOF). Thus, zoom lenses have generally been favored in consumer photography and extensively employed for various low accuracy applications in the field of computer vision, e.g., active stereo vision, augmented reality, and object detection and tracking.</p><hr>
<h3 align="left">Zoom lens calibration with zoom- and focus-related intrinsic parameters applied to bundle adjustment</h3>
<p class="large" align="left">Zheng, S., <b>Wang, Z.</b>, Huang, R., 2015. Zoom lens calibration with zoom-and focus-related intrinsic parameters applied to bundle adjustment.<i><b>ISPRS Journal of Photogrammetry and Remote Sensing</b></i>, 102, pp.62-72.</p>
<p class="large" align="left"><strong> Abstract</strong>: A zoom lens is more flexible for photogrammetric measurements under diverse environments than a fixed lens. However, challenges in calibration of zoom-lens cameras preclude the wide use of zoom lenses in the field of close-range photogrammetry. Thus, a novel zoom lens calibration method is proposed in this study. In this method, instead of conducting modeling after monofocal calibrations, we summarize the empirical zoom/focus models of intrinsic parameters first and then incorporate these parameters into traditional collinearity equations to construct the fundamental mathematical model, i.e., collinearity equations with zoom- and focus-related intrinsic parameters. Similar to monofocal calibration, images taken at several combinations of zoom and focus settings are processed in a single self-calibration bundle adjustment. In the self-calibration bundle adjustment, three types of unknowns, namely, exterior orientation parameters, unknown space point coordinates, and model coefficients of the intrinsic parameters, are solved simultaneously. Experiments on three different digital cameras with zoom lenses support the feasibility of the proposed method, and their relative accuracies range from 1:4000 to 1:15100. Furthermore, the nominal focal length written in the exchangeable image file header is found to lack reliability in experiments. Thereafter, the joint influence of zoom lens instability and zoom recording errors is further analyzed quantitatively. The analysis result is consistent with the experimental result and explains the reason why zoom lens calibration can never have the same accuracy as monofocal self-calibration.</p>
<figure>
<img src="images/project_calib_flowchart.png" alt="project_calib_flowchart.png" style="width: 65%">
<figcaption style="text-align: center;">Figure 1. Flowchart of the implementation of the proposed method. </figcaption><br>
</figure>
<figure>
<img src="images/project_calib_rulers.png" alt="project_calib_rulers.png" style="width: 60%">
<figcaption style="text-align: center;">Figure 2. Manually recording the zoom and focus settings for overcoming built-in setting inaccuracies. </figcaption><hr>
</figure>
<h3 align="left">A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration</h3>
<p class="large" align="left"><b>Wang, Z.</b>, Mills, J., Xiao, W., Huang, R., Zheng, S., Li, Z., 2017. A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration.<i><b>Remote Sensing</b></i>, 9(3), 244.</p>
<p class="large" align="left"><strong> Abstract</strong>: Compared with prime lenses, zoom lenses have inherent advantages in terms of operational flexibility. Zoom lens camera systems have therefore been extensively adopted in computer vision where precise measurement is not the primary objective. However, the variation of intrinsic camera parameters with respect to zoom lens settings poses a series of calibration challenges that have inhibited widespread use in close-range photogrammetry. A flexible zoom lens calibration methodology is therefore proposed in this study, developed with the aim of simplifying the calibration process and promoting practical photogrammetric application. A zoom-dependent camera model that incorporates empirical zoom-related intrinsic parameters into the collinearity condition equations is developed. Coefficients of intrinsic parameters are solved in a single adjustment based on this zoom lens camera model. To validate the approach, experiments on both optical- and digital-zoom lens cameras were conducted using a planar board with evenly distributed circular targets. Zoom lens calibration was performed with images taken at four different zoom settings spread throughout the zoom range of a lens. Photogrammetric accuracies achieved through both mono-focal and multi-focal triangulations were evaluated after calibration. The relative accuracies for mono-focal triangulations ranged from 1: 6300 to 1: 18,400 for the two cameras studied, whereas the multi-focal triangulation accuracies ranged from 1: 11,300 to 1: 16,200. In order to demonstrate the applicability of the approach, calibrated zoom lens imagery was used to render a laser-scanned point cloud of a building façade. Considered alongside experimental results, the successful application demonstrates the feasibility of the proposed calibration method, thereby facilitating the adoption of zoom lens cameras in close range photogrammetry for a wide range of scientific and practical applications.</p>
<figure>
<img src="images/project_calib_iphone6.png" alt="project_calib_iphone6.png" style="width: 60%">
<figcaption style="text-align: center;">Figure 3. (a) Nikon 1 J4 camera with a 3x optical zoom lens; (b) iPhone 6 mobile phone camera. </figcaption><hr>
</figure>
<h3 align="left">LiDAR point cloud rendering with calibrated zoom-lens cameras</h3>
<p class="large" align="left">Terrestrial Laser Scanning (TLS) and photogrammetry are generally regarded as complementary techniques, and it is recognized that their integration can lead to more accurate and complete products. In this study, a Leica Geosystems P20 terrestrial laser scanner was used to acquire a point cloud of the façade of the King Edward VII Building on Newcastle University’s main campus. The point cloud density was set as 0.8 mm at 10 m range, with the highest quality mode selected. As the building façade is a relatively simple structure of regular size, only one scan was sufficient to acquire full coverage.</p>
<p class="large" align="left">The Nikon 1 J4 camera were used to acquire imagery of the façade at different focal lengths. One image was captured using a focal length appropriate to capture the entire building façade, and a second image was acquired at a longer focal length, focused on an intricate central feature, a coat of arms. All images were acquired from approximately the same location, directly adjacent to the TLS location. The intrinsic parameters for each image were computed with the EXIF focal length of each image and pre-calibrated model coefficients via the zoom-lens calibration process. </p>
<p class="large" align="left">In order to obtain the exterior orentation (EO) parameters of each image, sufficient conjugate reference points were manually selected on both the point cloud and corresponding image. Reference points for each co-registration are marked as circular dots and labeled with numbers in Figure 4. The EO parameters were computed by the following procedure: (i) a 3D direct linear transformation (DLT) was performed and the EO parameters were extracted from the 11 coefficients of DLT, which requires at least six reference points; (ii) iterative space resection was executed to refine the EO parameters.</p>
<p class="large" align="left">Once EO parameters of each image were determined, the co-registration of both the point cloud and image was implemented based on the collinearity equations with lens distortion correction terms. 3D space point coordinates were converted to an image plane and the color was assigned to each point in the cloud using nearest neighbor interpolation. In this application, the shorter focal length image was used to first texture the entire point cloud. The initial textured point cloud was then updated with the longer focal length image. Figure 4 compares the final textured point cloud products. </p>
<figure>
<img src="images/project_calib_rendering.png" alt="project_calib_rendering.png" style="width: 80%">
<figcaption style="text-align: left;">Figure 4. (a), (b) Point clouds of the King Edward VII façade, textured with images taken by a Nikon 1 J4 camera with a focal length of 16.8 mm and 29.4 mm respectively. </figcaption>
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