CALIBRATION OF SMARTPHONE’S REAR DUAL CAMERA SYSTEM

This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos for a chessboard pattern from different exposure positions. First, photos were captured in dual camera mode. Then, for both wide-angle and telephoto lenses, image coordinates for node points of the chessboard were extracted. Afterwards, intrinsic, extrinsic, and lens distortion parameters for each lens were calculated. In order to enhance the accuracy of the calibration model, a constrained least-squares solution was applied. The applied constraint was that the relative extrinsic parameters of both wide-angle and telephoto lenses were set as constant regardless of the exposure position. Moreover, photos were rectified in order to eliminate the effect of lens distortion. For results evaluation, two oriented photos were chosen to perform a stereo-pair intersection. Then, the node points of the chessboard pattern were used as check points.

A Multi Camera and Multi Laser Calibration Method for 3D Reconstruction of Revolution Parts

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm.

Digital Model in Close-Range Photogrammetry Using a Smartphone Camera

Smartphones recently expanded the potential for low-cost close-range photogrammetry for 3D modeling. They enable the simultaneous collection of large amounts of data for a variety of requirements. It is possible to calculate image orientation elements and triangular coordinates in phases as in Relative and Absolute image orientation. This study demonstrates the photogrammetric 3D reconstruction approach that performs on tablets and smartphones as well. Images are taken with smartphone cameras of iPhone 6 and then calibrated automatically using normal calibration model for photogrammetry and computer vision on a PC, depend on Agisoft Lens add-on that imbedded in Agisoft program, and MATLAB camera calibration Toolbox, and by using an oriented bunch of images of chessboard pattern for large point cloud-based picture using matching. The camera calibration results indicate that the calibration processing routines pass without any error, and the accuracy of estimated IOPs was convenient compared with non-metric digital cameras and are more accurate in Agisoft Lens in terms of standard error. For the 3D model, 435 cameras were used, 428 cameras located from 435 are aligned in two photogrammetric software, Agisoft PhotoScan, and LPS. The number of tie points that are used in LPS is 10 tie points, and 4 control points which used to estimate the EOPs, and the number of tie points that are regenerated in Agisoft PhotoScan were 135.605 points, the number of Dense cloud 3,716,912 points are generated, for 3D model a number of 316,253 faces are generated, after processing the tiled model generated (6 levels, 1.25 cm/pix), the generated DEM having (2136×1774/pix), the dimensions of the generated high-resolution orthomosaic are (5520×4494, 4.47 cm/pix). For accuracy assessment, the Xerr. = 0.292 m, Yerr. = 0.38577 m, Zerr.= 0.2889 m, and the total RMS = 0.563 m in the estimated locations of the exterior orientation parameters.

Computer molecular-dynamic simulation of shs microkinetics in the atomic structure with a checkerboard-like arrangement of nanoscale blocks of Ni and Al atoms

The article presents the results of computer simulation of the propagation of the combustion wave of "self-propagating high-temperature synthesis (SHS)" process in an atomic layered structure. In each layer of the structure, nanosized blocks of two types alternate: a block of the first type is composed as a packet of unit cells of Ni atoms, and a block of the second type is composed of a packet of elementary cells of Al atoms. In each pair of layers adjacent to each other, sequences of alternating blocks of two types are shifted relative to each other by one block, so the full layered structure of the layers with alternating blocks in them is associated with a chessboard pattern. Computer simulation of SHS in such a structure was carried out using the LAMMPS software package taking into account parallel computations, which uses the molecular dynamics method and the interatomic interaction potential in the embedded atom" model (EAM). In addition to the LAMMPS package, the authors implemented program procedures for calculating the temperature and density profiles of the substance along the motion direction of the SHS combustion wave front, which made it possible to carry out temperature analysis of the SHS microkinetics (to estimate the velocity of the combustion wave front) and recognition of intermetallic phases in the reaction volume of the Ni-Al system when using the OVITO package.

Systematic atlasing in Hessequa – Report on the first cycle of seasonal monitoring

Bird atlasing in the Hessequa region of the Western Cape has progressed beyond mapping to monitoring. During a three-year period within 2014/17, the U3A Stilbaai Bird Group upgraded the distribution maps using a strategy which aimed to even out coverage per grid cell, and achieve minimum mapping standards. In the two-year period December 2017 to November 2019, the group implemented a new strategy that would result in each of the 75 pentads in the Hessequa Atlas Area being atlased in each of the four seasons over a two-year period. Using a chessboard pattern to split the 75 pentads into two sets, the first set was atlased in summer and winter in the first year and autumn and spring of the second year. The second set was atlased in autumn and spring of the first year, and summer and winter of the second year. This paper reports the successful completion of the first monitoring cycle.

Applying Computer Vision Technology to Triaxial Deformation Monitoring of Remedial Construction for a Landslide

Background & Objective: The paper explores a new instrument of computer vision to measure three-dimension deformation with an Internet of Things (IoT) system including Raspberry Pi, digital cameras and OpenCV programs in laboratory and field testing so as to monitor the potential deformation of a structure drainage well in a landslide. Methods: A chessboard pattern is detected in the image by the camera so that pixels of chessboard cornors can be recognized by OpenCV programs. X-direction, Y-direction and Z-distance changes can be casulated by the similar triangles relationship of camera pixels. For laboratory testing, standard deviations of the measurement were approximately 0.01 cm. Results: For field testing, the study installed four sets of Raspberry Pi in a drainage well within a landslide and employed OpenCV programs to interpret pixel changes of chessboards at four levels of the draiage well. Conclusion: Overall, the instrument can be employed for triaxial deformation monitoring of the construction in the field effectively and automatically.

A Novel Feature-Based Approach for Indoor Monocular SLAM

Camera tracking and the construction of a robust and accurate map in unknown environments are still challenging tasks in computer vision and robotic applications. Visual Simultaneous Localization and Mapping (SLAM) along with Augmented Reality (AR) are two important applications, and their performance is entirely dependent on the accuracy of the camera tracking routine. This paper presents a novel feature-based approach for the monocular SLAM problem using a hand-held camera in room-sized workspaces with a maximum scene depth of 4–5 m. In the core of the proposed method, there is a Particle Filter (PF) responsible for the estimation of extrinsic parameters of the camera. In addition, contrary to key-frame based methods, the proposed system tracks the camera frame by frame and constructs a robust and accurate map incrementally. Moreover, the proposed algorithm initially constructs a metric sparse map. To this end, a chessboard pattern with a known cell size has been placed in front of the camera for a few frames. This enables the algorithm to accurately compute the pose of the camera and therefore, the depth of the primary detected natural feature points are easily calculated. Afterwards, camera pose estimation for each new incoming frame is carried out in a framework that is merely working with a set of visible natural landmarks. Moreover, to recover the depth of the newly detected landmarks, a delayed approach based on linear triangulation is used. The proposed method is applied to a realworld VGA quality video (640 × 480 pixels) where the translation error of the camera pose is less than 2 cm on average and the orientation error is less than 3 degrees, which indicates the effectiveness and accuracy of the developed algorithm.