An image processor includes an edge detection portion that scans an image and detects, as an edge, an arrangement of pixels in which brightness value difference or color parameter difference between the pixels is equal to or greater than a threshold; a grouping portion that groups the detected edge based on the edge length, a distance between endpoints of the edges, and an angle between the edges; a determination portion that determines the grouped edges as a dashed line edge group when a pattern in which the brightness value difference or color parameter difference between the pixels is detected matches a predetermined pattern; a correction portion that performs a linear approximation process on the dashed line edge group to correct a coordinate value of an endpoint of the dashed line edge; and a parking frame setting portion that sets a parking frame using the corrected dashed line edge.

A computer-implemented detection method includes, in response to inputting a first image including a region of one or more objects to a learned model, identifying a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object, detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects, and determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

A method for process control of a semiconductor structure fabricated by a series of fabrication steps, the method comprising obtaining an image of the semiconductor structure indicative of at least two individual fabrication steps; wherein the image is generated by scanning the semiconductor structure with a charged particle beam and collecting signals emanating from the semiconductor structure; and processing, by a hardware processor, the image to determining a parameter of the semiconductor structure, wherein processing includes measuring step/s from among the fabrication steps as an individual feature.

Embodiments are directed to the association of concurrent tracks using graph crossings. Signal beams may be employed to scan paths across an object such that sensors separately detect signals from the signal beams reflected by the object. Crossing points may be determined based on a plurality of trajectories that intersect each other during the scan of the object. Graphs may be generated based on the portion of trajectories and the crossing points such that each edge in the graphs corresponds to a crossing point and such that each node in the one or more graphs corresponds to a trajectory. The graphs may be compared to determine one or more matched graphs that may share a common topology. Common trajectories may be determined based on the matched graphs such that each common trajectory may be associated with a same path across the object and a separate sensor.

A method of processing a given region of interest (ROI) of an X-ray image of a person's breast to determine presence of a malignancy, the X-ray image having X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image, the method comprising: for each given X-ray pixel in the given ROI and each of a selection of J(r) X-ray pixels at respective pixel radii PR(r), 1≤r≤R, from the given x-ray pixel, determining a binary number that provides a measure X-ray intensity indicated by the selected X-ray pixel relative to X-ray intensity indicated by the given X-ray pixel; using the determined binary numbers for the selected X-ray pixels at each pixel radius PR(r) to determine a decimal number for the pixel radius PR(r); histogramming the frequency of occurrence of values of the determined decimal numbers as a function of pixel radius for the given X-ray pixels in the given ROI; determining a texture feature vector, for the given ROI having components that are equal to the frequencies of occurrence for a selection of M histogrammed values; and processing the histogrammed frequencies of occurrence for the M values to determine whether the given ROI is malignant.

A single item image is captured of an item situated within a given zone of a transaction area is captured, each different zone for each given item is associated a plurality of single item images captured by different cameras at different angles and perspectives of the transaction area. The single item images are passed to an existing segmentation Machine-Learning Model (MLM) and accurate masks for the items produced by the existing MLM are retained. A background image of an empty transaction area is obtained, each retained single item image is cropped and superimposed into the background image with one or more different cropped and superimposed singe item images creating a composite multi-item image. The composite multi-item images are labeled to identify the boundaries of each single item image and the existing segmentation MLM is trained on the composite multi-item and labeled images producing an enhanced segmentation MLM.

Provided are a method and apparatus for labeling point cloud data, an electronic device, and a computer-readable storage medium. In the embodiments, object recognition is firstly performed on to-be-recognized point cloud data to obtain a bounding box of an object in the to-be-recognized point cloud data; subsequently, to-be-labeled point cloud data is determined according to the bounding box of a recognized object in the to-be-recognized point cloud data; subsequently, a manual annotation box of an object in the to-be-labeled point cloud data is acquired; and finally annotation boxes of objects in the to-be-recognized point cloud data are determined according to the bounding box and the manual annotation box.

An image processor includes an edge detection portion for scanning an image and detecting, as edges, an arrangement of pixels in which brightness value difference or color parameter difference between the pixels is equal to or greater than a threshold; a grouping portion for grouping the detected edge based on edge length, a distance between endpoints of the edges, and an angle between the edges; a determination portion for determining the grouped edges as a dashed line edge group when a pattern, in which the brightness value difference or the color parameter difference between the pixels is detected, matches a predetermined pattern; a correction portion for performing a linear approximation process on the dashed line edge group to correct a coordinate value of an endpoint of the dashed line edge; and a parking frame setting portion for setting a parking frame using the corrected dashed line edge.

An example method of detecting an element using an autonomous vehicle includes the following operations: using a sensor on the autonomous vehicle to capture image data in a region of interest containing the element, where the image data represents components of the element; filtering the image data to produce filtered data having less of an amount of data than the image data; identifying the components of the element by analyzing the filtered data using a deterministic process; and detecting the element based on the components.

A method for generating a panoramic image with reduced overlapping of neighboring teeth, including: acquiring 2D x-ray images respectively at a different radiographic directions by a rotating an x-ray source and an x-ray detector around the jaw of a patient. It includes a step of identifying one or more regions each including at least one pair of overlapping neighboring teeth in the 2D x-ray images and/or in temporary panoramic images reconstructed from the 2D x-ray images for which an optimal radiographic directions are determined, among the corresponding radiographic directions, which reduces the overlap in the panoramic image to be reconstructed. It includes determining one or more optimal radiographic directions respectively among the corresponding radiographic directions of the 2D x-ray images for which one or more regions each including at least one pair of overlapping neighboring teeth has been identified, to reduce the overlaps in the panoramic image to be reconstructed.