5 results about "Direct linear transformation" patented technology

The application provides an image target matching method and device based on local homography, and relates to the field of image processing; the method adopts a registration algorithm based on image local features to pre-acquire natural feature point pairs between background image pairs; secondly, the source background image is evenly divided into multiple rectangular grids; a moving direct linear transformation algorithm is used to calculate the local homography matrix of each rectangular grid; and the mapping point of the image target to be matched in the source image in the target image is calculated by using the local homography matrix; and the image target uniquely existing in the mapping area or the image target closest to the mapping point in the Euclidean distance is taken as the matching point. The application uses the local homography matrix to weaken the mapping error introduced by the parallax change, improve the image target matching precision, reduce the mis-matching probability of the cluster target with similar features and dense distribution in a complex environment, and significantly improve the image target matching speed.

The application relates to a pavement crack identification method based on point cloud-RGB heterogenous image multi-stage registration mapping. The method comprises the following steps: collecting pavement point cloud data and pavement image data for time and space synchronization, creating a projection image pair for feature extraction, obtaining a local feature descriptor, matching feature points in the local feature descriptor, obtaining an actual matching point pair, solving the parameters of a direct linear transformation equation according to the actual matching point pair, obtaining the mapping relationship between the RGB image pixel coordinates and the three-dimensional point cloud coordinates, traversing the coordinates of each point in the pavement image data, assigning the mapping relationship depth information to the pavement image data, obtaining a point cloud projection image, generating a depth image from the point cloud projection image, labeling cracks and backgrounds, constructing a data set, training a crack identification model using the data set, obtaining a trained crack identification model, and identifying pavement cracks, thereby improving the efficiency and accuracy of automatic crack identification.

The invention relates to an unmanned aerial vehicle video image real-time splicing method based on perspective transformation, and the method comprises the following steps: 1, obtaining adjacent image pairs collected by an unmanned aerial vehicle, and determining an image pair with a potential overlapping region through flight path data analysis; 2, extracting feature points and descriptors of the image by adopting an SURF algorithm; 3, performing initial feature matching on the feature descriptors by using a violent matcher, and removing mismatching point pairs in combination with an RANSAC algorithm to obtain an accurate feature matching result; 4, estimating a homography matrix based on the matching point pairs by adopting a normalized direct linear transformation algorithm; 5, performing perspective transformation on the image by using the homography matrix, mapping the image to a unified plane, and correcting geometric deformation; step 6, fusing the transformed images by adopting a multi-resolution fusion technology to generate a seamless image; and 7, performing incremental splicing on the processed unmanned aerial vehicle images in sequence to realize real-time splicing of the unmanned aerial vehicle video images.

The application provides a machine vision-based panoramic surround-view auxiliary driving method and system, which comprises the following steps: acquiring a plurality of first images around a special vehicle by using a plurality of fisheye cameras; acquiring a distortion model of the fisheye camera; correcting the first images by using a polynomial model correction method through the distortion model to obtain second images; performing pitch transformation on the second images by using a direct linear transformation algorithm to obtain third images; eliminating splicing gaps of all the third images by using a weighted average algorithm to obtain a panoramic surround-view image; performing target detection on the panoramic surround-view image based on a multi-task perception algorithm of YOLOv5s, wherein the target comprises drivable area segmentation results and lane line segmentation results; and displaying the panoramic surround-view image and the target in the panoramic surround-view image. The application can completely display a virtual vehicle, a vehicle bottom and a surrounding road surface fusion image, intelligently recognize road scene information, eliminate a visual field blind area and enhance the visual field of a special vehicle driver.

This invention discloses a bolt pose estimation method based on monocular RGB images, applicable to industrial production and intelligent manufacturing. The method involves inputting a monocular RGB image or video frame into a pre-defined feature extraction network to obtain a high-dimensional feature map of the image or video frame to be detected. This high-dimensional feature map is then input into a convolutional pose estimation network to predict the bolt's 2D keypoint confidence map and affinity map. The prediction results are used to search for the optimal match among all potential symmetrical poses of the bolt, and the network loss is calculated to update the network parameters. A heuristic algorithm is used to search within the prediction results of the confidence map and affinity map to obtain the 2D coordinates of all bolt keypoints. Finally, a direct linear transformation algorithm is used to establish the connection between the 2D and 3D keypoints of the bolt, calculating the 6D pose of the bolt.