6 results about "Probabilistic hough transform" patented technology

The invention discloses a visual image processing method based on an automatic calibration system, and belongs to the technical field of machine vision, and the method comprises the steps: image collection and input: collecting an image containing a target line, transmitting the image data to an image processing module, and completing image format standardization and data loading; preprocessing the image; extracting edge information of the preprocessed image by adopting a Canny edge detection algorithm, and retaining edge features of the target line; performing straight line detection on the edge image by adopting probability Hough transform, setting a line segment length threshold and a gap threshold, screening to obtain a target line, and extracting pixel coordinates of the target line; and converting a target line pixel coordinate into an actual offset distance, correcting by combining with a system operation distance measured by an encoder to obtain a true value of the measured distance, and outputting the true value. According to the method, the problems of low image processing precision, poor robustness and insufficient real-time performance in a traditional automatic calibration system are solved, and the automatic calibration system is promoted to develop towards high precision, high automation and high robustness.

The application provides a kind of unmanned aerial vehicle attitude correction method and system based on visual feedback, it is related to unmanned aerial vehicle visual servo control technical field.The application obtains the original color image of fan blade after, based on HSV grass green threshold generation grass binary mask and carry out grass gray scale suppression, obtain grass suppression gray scale chart;Edge map is generated and is subjected to probability hough transform line segment detection, according to the angle distribution of line segment determines horizontal dominant mode or vertical dominant mode, respectively determines slope, intercept or inverse slope, inverse intercept and filters line segment, obtains blade center line equation by K-means clustering;Horizontal movement component and vertical movement component are calculated again, and are converted into unmanned aerial vehicle drift angle adjustment amount and gimbal pitch angle adjustment amount.The application can reduce the interference of complex background line segment, reduce the parameter instability caused by fixed slope expression under vertical imaging attitude.

The invention belongs to the field of computer vision, and particularly relates to a laser vision-based welding seam identification and feature point extraction method, which comprises the following steps of: firstly, carrying out Gaussian filtering on an acquired welding seam image to suppress noise, and then extracting sub-pixel coordinates of a laser stripe center by utilizing an improved Steger algorithm; performing three-dimensional reconstruction by combining a laser plane equation participating in the camera, and generating an XZ plane image through sampling and interpolation; probabilistic Hough transform is applied to detect line segments, and the line segments are combined through angle and distance constraints to improve integrity; based on the structural features of the welding seam, combining line segments by utilizing dislocation and gap constraint, and screening out an effective welding seam line segment set through a prior angle; and finally, end points or cross points of line segments are extracted as feature points according to the welding seam type, and welding spot coordinates are stably output by utilizing an inter-frame tracking mechanism. Strong interference in the welding process can be effectively resisted, and online, real-time and high-precision recognition and feature point extraction of various types of welding seams are achieved.

The invention belongs to the technical field of medical image processing and computer-aided diagnosis, and provides a child humerus supracondylar fracture typing auxiliary method based on a visual large model, which comprises the following steps: acquiring positive and lateral X-ray digital images of a child elbow joint; dimension standardization, lightweight non-local mean denoising, contrast limited adaptive histogram equalization, and Z-score gray normalization preprocessing are carried out; coarsely positioning the distal humerus through Yov8n, determining a bone main direction axis in combination with Canny edge detection and probabilistic Hough transform, and rotating and aligning to obtain a standardized ROI (Region of Interest); key point detection calculates geometric feature vectors such as a Baumann angle, a visual large model extracts a depth image feature vector, and an attention mechanism fuses the two; and the inference model obtains a preliminary Gartland typing probability according to the fused features, introduces child humerus development knowledge graph constraints, and finally outputs a typing probability. The child humerus supracondylar fracture Gardland typing auxiliary diagnosis method not only adapts to child skeletal development characteristics, but also improves the decision credibility through interpretable output, and achieves accurate and efficient auxiliary diagnosis of child humerus supracondylar fracture Gardland typing.

This invention provides a method and system for detecting power line defects based on visual recognition, belonging to the field of power line detection technology. Firstly, this invention employs dual-light registration and differential computation techniques using visible light and infrared images to generate a fused feature map that simultaneously reflects component thermal anomalies and material differences. Secondly, in the component localization stage, it abandons the black-box target detection model and instead utilizes probabilistic Hough transform combined with prior geometric knowledge specific to power lines for line segment selection and reconstruction. This not only reduces the dependence on large amounts of labeled data but also enhances the interpretability of the localization process, enabling the extraction of precise regions of key components such as conductors and insulators from complex backgrounds. In the core defect identification stage, the concept of a probabilistic saliency map is introduced, enhancing the prominence of defect areas. Subsequently, adaptive threshold segmentation and region growing algorithms are used to determine the complete defect contour, achieving accurate mapping from pixel-level features to object-level defects.

This invention relates to the fields of artificial intelligence and biomedical technology, specifically to a robotic automated patching method and system for peripheral nerve conduction detection, as follows: After the doctor selects the examination items on the electromyography instrument, the robot receives instructions and executes the operation; it extracts the patient's limb contour image through a binocular industrial camera system, and calls the nerve-electrode position mapping database to determine the relative positions of standard anatomical feature points; it uses probabilistic Hough transform to filter the lines corresponding to the joints, calculates the intersection points to obtain the feature point image coordinates, combines the relative positions to calculate the target patch position, and then solves the transformation matrix to obtain the three-dimensional coordinates of the robotic arm end; after determining that the electrode inventory is sufficient, it plans a path to grasp the electrodes, aligns and applies them with pressure, verifies the adhesion and contact resistance, and makes fine adjustments if they are unqualified or the impedance drifts; after completing the patching at multiple positioning points, it records the status to form a log, ensuring electrode stability before testing, achieving precise, stable and intelligent patching, reducing medical care intensity and improving diagnostic efficiency.