14 results about "Blob detection" patented technology

The invention relates to a method and apparatus for high resolution total body photography (TBP). The inventive method uses a multi-stage keypoints focused pipeline that begins with blob detection to rapidly and coarsely localize potential lesions within high-resolution TBP images. Once these regions of interest are identified, a deep learning classifier evaluates them for malignancy risk. Acknowledging that wide-field imaging can compromise classification precision due to variability in resolution and appearance, the process is further refined by integrating ugly duckling analysis and t-SNE clustering. The ugly duckling detection process groups suspicious regions across all images, effectively highlighting clusters of high-risk candidates for further clinical review utilizing the method of the invention.

This invention relates to the field of autonomous docking and recovery of underwater autonomous vehicles, and in particular to a method for underwater docking guide light identification and positioning for unmanned underwater vehicles. The method includes the following steps: S1, real-time acquisition of guide light image sequences, classification and filtering using an image classification model to obtain pre-filtered guide light images; S2, guide light signal spot detection and brightness-based noise removal to obtain spot detection information; S3, determination of whether the image contains apparent guide light signals to obtain actual guide light spot detection information; S4, calculation of the relative position between the underwater vehicle and the docking station based on the guide light spot information obtained in step S3. A lightweight neural network model is used for real-time optical image recognition, improving the extraction capability of optical feature information, reducing environmental noise interference, and increasing the success rate of guide light identification, positioning accuracy, and docking success rate.

Methods, systems, and media for validating objects within a container, such that a validation system confirms that objects within a container are valid i.e., ready to be shipped from a data center for reuse or ready to be destroyed. The validation system can be used to identify objects, e.g., media, disks, etc., and determine if valid objects are at the correct processing location to be shipped or destroyed. The methods may include identifying the objects, locating an identifier on the object, comparing the identifiers to known identifiers and determining, based on the comparison, if valid objects are at the correct processing location to be shipped or destroyed. The validation system may also use image processing techniques, such as blob detection, to identify invalid objects in the container. The system may reject the entire container if invalid objects are detected.

A system and method for enabling management of faults relating to a tracking managing subsystem and a tracking assembly communicatively connected thereto, include a method including: obtaining from the tracking assembly a tracking stream, which comprises tracking data maps, obtaining a set of system parameters, deriving a set of blobs by applying a blob detection procedure to at least two tracking data maps of the tracking stream, and sending the set of blobs and the set of system parameters to a rendering subsystem at which the set of system parameters are processed, thereby enabling fault management by the rendering subsystem.

A kind of offshore oil spill risk source fast intelligent identification classification method of high-resolution remote sensing image belongs to the cross technical field of marine remote sensing and artificial intelligence, including: 1) oil platform and ship as the main risk source of offshore oil spill, with the characteristics of pixel value higher than surrounding seawater, according to the method of pixel value gradient and spot detection, a squeezing excitation residual network combining Gaussian Laplacian operator and connected domain controller is designed;2) using prior knowledge such as pixel gradient and target size, suspected offshore oil spill risk source identification is carried out, the center position and size of the target are obtained using connected domain controller on multiple response points around the target, 3) finally, the suspected offshore oil spill risk source identified is classified by deep learning classification network.The present application only classifies the suspected offshore oil spill risk source target picture in the image, reduces the number of pictures, improves the running time, and the training set and the actual prediction picture size are consistent, only small-scale pictures are needed for training without using the whole remote sensing image, which is easier to train and saves time.

The invention discloses a dynamic mark point detection tracking method and system based on improved CSRT, and belongs to the technical field of bionic visual tactile sensors, and the method comprises the steps: determining a mark point, and obtaining a first frame image containing the mark point; acquiring an initial position of the mark point in the first frame image based on a morphological feature spot detection method; acquiring a CSRT tracker of the mark point based on the initial position, and acquiring motion information of the mark point through the CSRT tracker; obtaining vector modulus length of inter-frame displacement of the mark points based on the motion information, comparing the vector modulus length with a preset modulus length threshold value, and determining the mark points corresponding to the vector modulus length outside the modulus length threshold value as moving points; acquiring a region covering the moving points as a dynamic attention region based on the spatial distribution of the moving points; and performing displacement tracking of the contact object based on the dynamic region of interest. According to the morphological feature-based spot detection method, accurate initial positioning of the mark points is realized, and a reliable starting point is provided for subsequent tracking.

A spot detection device, a spot detection method, and a display device are provided. The spot detection device includes an imager and a spot detector, in which: the imager is configured to capture a display panel displaying a first image of a first gray scale and a second image of a second gray scale different from the first gray scale, and generate image data; and the spot detector is configured to receive the image data from the imager to detect a spot. The image data includes first image data obtained by capturing the first image, second image data obtained by capturing the second image, and a plurality of third image data obtained by capturing a portion of the first image.

The application provides a multi-scale glue point detection method, and belongs to the technical field of glue point detection; the method comprises the following steps: adopting an intelligent laser lens dispensing platform to collect images before laser chip bonding; establishing an LLAS data set based on the collected images; establishing a multi-level attention guide context aggregation network model; applying the multi-level attention guide context aggregation network model to process images in the LLAS data set, and segmenting out glue point targets; and obtaining glue point detection results based on the segmented glue point targets. The application proposes a multi-level attention guide context aggregation network (MACAN) based on a self-built LLAS data set. The purpose is to realize real-time and robust detection of glue points on laser chips, and to provide necessary quality assurance for realizing high coupling efficiency of high-power laser lenses.

This application provides a spot detection control method, device, and spot detection system, which are applied to spot detection control equipment. First, according to the change information between the laser spot sizes corresponding to different positions on the main optical axis when the laser emitted by the laser is transmitted to these positions, the spot analyzer is controlled to move to the focal position on the main optical axis; then, the spot detection result of the laser is determined based on the first spot information collected by the spot analyzer at the focal position. The first spot information includes at least one of spot size, spot shape, and spot intensity data. This realizes the automatic real-time movement adjustment of the spot analyzer, ensures the accuracy of movement adjustment, improves the efficiency of moving the spot analyzer to the focal position, and thus enhances the detection efficiency of the detection result.

A non-detected workpiece may occur in workpiece detection processing for detecting, on the basis of image data captured by a visual sensor, a workpiece depicted in the image data.　An image processing device 50 comprises: a workpiece detection unit 52 that executes workpiece detection processing for detecting, on the basis of image data captured by a visual sensor 14, a workpiece depicted in the image data; a pixel removal unit 54 that removes, from the image data, pixels that depict the workpiece detected in the workpiece detection processing; a blob detection unit 56 that detects, as a blob, an aggregate of pixels that depict a subject in the image data from which pixels have been removed by the pixel removal unit 54; and a non-detection determination unit 58 that determines, on the basis of the blob detected by the blob detection unit 56, whether a workpiece not detected in the workpiece detection processing is present.

Methods, system, and media for comparing a set of images to determine the existence and location of any differences between the image set. The differences may be located using image comparison techniques such as SURF and Blob Detection, as well as through techniques used to identify areas of data sliding and match probabilities. A logical match probability, as well as a physical match probability, may be included in an output report with a result image highlighting the differences between the comparison images in the image set.

A method and a tracking managing subsystem (111) for providing blobs relating to tracking of a sports projectile are disclosed. The method comprises obtaining (A110), from the tracking assembly (115), a tracking stream including tracking data maps, which are associated with timestamps, wherein each tracking data map of the tracking data maps is associated with a respective timestamp of the timestamps, and obtaining (A120) a set of transform parameters. Furthermore, the method comprises deriving (A130) a set of blobs by applying a blob detection procedure to at least two tracking data maps of the tracking stream, and sending (A140) the set of blobs and the set of transform parameters.

The invention provides an intelligent vision inspection method, system and device running on an embedded platform, and relates to the field of computer vision measurement. The method comprises the following steps: simultaneously acquiring two paths of images through an image sensor; performing AI target detection on one path of image to obtain a target frame and mapping the target frame to a display coordinate system; calculating the offset between the center of the target and the center of the picture to judge a visual alignment state; monocular distance estimation and smoothing processing are carried out based on the target pixel height, the physical height and the equivalent focal length; executing color threshold blob detection in the ROI region defined by the target frame to identify abnormity; a double-layer duplicate removal mechanism is adopted to avoid duplicate records; and performing automatic screenshot on a new abnormal event, recording a log, and overlapping and displaying detection information on a picture. According to the invention, in the embedded device with limited resources, the monocular camera is combined to realize low-cost and high-reliability intelligent inspection, and the problems of unstable distance measurement, abnormal false detection and repeated event recording are effectively solved.