COLOURIMETRIC CALIBRATION FOR PHOTOGRAPHY, PHOTOGRAMMETRY, AND PHOTOMODELLING WITHIN ARCHITECTURAL SURVEY

In accordance with the “Declaration of Rome on architectural survey”, we can affirm that recording and interpretation of colour information in photographic surveying, in photogrammetric surveying and in photomodelling requires careful planning of Colour Imaging processes. Information acquired by an optical sensor is influenced not just by the actual photographed scene, but also by the spectral sensitivity of the sensor. We have adopted, from the field of Cultural Heritage, a method of colourimetric calibration for digital photographs and have proposed some adjustments to finalise this process for the purposes of Architectural Survey. With the use of a colourimetric target and a non-linear transformation algorithm, our Colour Imaging method statistically reconstructs colours conventionally unrecordable by a commercial camera. In addition, this method reconstructs colours as if the photographed object were exposed to a standard illuminant, assessing a colour error parameter value for each photo. By including the colourimetric target in every shot and by applying the calibration algorithm to all photographs taken, the process correlates all data sets to a single standard illuminant: regarding photomodelling, this leads to a more uniform and detailed representation of the surfaces of virtual models. We present two successful examples of application: one focused on a design object with physioplastic decoration and another regarding a circular fountain in a historic villa.

SMART SHIELD PANEL AC VOLTMETER CELL

In this paper smart shield panel electrical operating parameters meters of energy generating facilities functionality is analysis. The list of functions of measuring instruments supplemented, which allowed increasing their operational characteristics. Methods and results of realization of these functions given for the panel board intellectualized voltage meter of alternating current. The structural scheme of the developed panel board intellectualized meter is described and its main technical characteristics are given.A method of mobile calibration of the device is proposed, in which a calibration signal source with a separate fixed value is used, and the calibration process itself is controlled from the device keyboard. A modernized detailed and simplified calibration algorithm is present. Ref. 12, fig. 5, tabl. 2.

CFNet: LiDAR-Camera Registration Using Calibration Flow Network

As an essential procedure of data fusion, LiDAR-camera calibration is critical for autonomous vehicles and robot navigation. Most calibration methods require laborious manual work, complicated environmental settings, and specific calibration targets. The targetless methods are based on some complex optimization workflow, which is time-consuming and requires prior information. Convolutional neural networks (CNNs) can regress the six degrees of freedom (6-DOF) extrinsic parameters from raw LiDAR and image data. However, these CNN-based methods just learn the representations of the projected LiDAR and image and ignore the correspondences at different locations. The performances of these CNN-based methods are unsatisfactory and worse than those of non-CNN methods. In this paper, we propose a novel CNN-based LiDAR-camera extrinsic calibration algorithm named CFNet. We first decided that a correlation layer should be used to provide matching capabilities explicitly. Then, we innovatively defined calibration flow to illustrate the deviation of the initial projection from the ground truth. Instead of directly predicting the extrinsic parameters, we utilize CFNet to predict the calibration flow. The efficient Perspective-n-Point (EPnP) algorithm within the RANdom SAmple Consensus (RANSAC) scheme is applied to estimate the extrinsic parameters with 2D–3D correspondences constructed by the calibration flow. Due to its consideration of the geometric information, our proposed method performed better than the state-of-the-art CNN-based methods on the KITTI datasets. Furthermore, we also tested the flexibility of our approach on the KITTI360 datasets.

PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping

A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.

Bas-relief Modeling from RGB Monocular Images with Regional Division Characteristics

Bas-relief, a form of sculpture art representation, has the general characteristics of sculpture art and satisfies people’s visual and tactile feelings by fully leveraging the advantages of painting art in composition, subject matter, and spatial processing. Bas-relief modeling using images is generally classified into the method based on the three-dimensional (3D) model, that based on the image depth restoration, and that based on multi-images. The 3D model method requires the 3D model of the object in advance. Bas-relief modeling based on the image depth restoration method usually either uses a depth camera to obtain object depth information or restores the depth information of pixels through the image. Bas-relief modeling based on the multi-image requires a short running time and has high efficiency in processing high resolution level images. Our method can automatically obtain the pixel height of each area in the image and can adjust the concave–convex relationship of each image area to obtain a bas-relief model based on the RGB monocular image. First, the edge contour of an RGB monocular image is extracted and refined by the Gauss difference algorithm based on tangential flow. Subsequently, the complete image contour information is extracted and the region-based image segmentation is used to calibrate the region. This method has improved running speed and stability compared with the traditional algorithm. Second, the regions of the RGB monocular image are divided by the improved connected-component labeling algorithm. In the traditional region calibration algorithm, the contour search strategy and the inner and outer contour definition rules of the image considered result in a low region division efficiency. This study uses an improved contour-based calibration algorithm. Then, the 3D pixel point cloud of each region is calculated by the shape-from-shading algorithm. The concave–convex relationships among these regions can be adjusted by human–computer interaction to form a reasonable bas-relief model. Lastly, the bas-relief model is obtained through triangular reconstruction using the Delaunay triangulation algorithm. The final bas-relief modeling effect is displayed by OpenGL. In this study, six groups of images are selected for conducting regional division tests, and the results obtained by the proposed method and other existing methods are compared. The proposed algorithm shows improved image processing running time for different complexity levels compared with the traditional two-pass scanning method and seed filling method (by approximately 2 s) and with the contour tracking method (by approximately 4 s). Next, image depth recovery experiments are conducted on four sets of images, namely the “ treasure seal,” “Wen Emperor seal,” “lily pattern,” and “peacock pattern,” and the results are compared. The depth of the image obtained by the traditional algorithm is generally lower than the actual plane, and the relative height of each region is not consistent with the actual situation. The proposed algorithm provides height values consistent with the height value information judged in the original image and adjusts the accurate concave–convex relationships. Moreover, the noise in the image is reduced and relatively smooth surfaces are obtained, with accurate concave–convex relationships. The proposed bas-relief model based on RGB monocular images can automatically determine the pixel height of each image area in the image and adjust the concave–convex relationship of each image area. In addition, it can recover the 3D model of the object from the image, enrich the object of bas-relief modeling, and expand the creation space of bas-relief, thereby improving the production efficiency of the bas-relief model based on RGB monocular images. The method has certain shortcomings, which require further exploration. For example, during the process of image contour extraction for region division, small differences exist between the obtained result and the actual situation, which can in turn affect the image depth recovery in the later stage. In addition, partial distortion may occur in the process of 3D reconstruction, which requires further research on point cloud data processing to reconstruct a high-quality three-dimensional surface.