15 results about "Imaging order" patented technology

The invention discloses a remote sensing satellite area coverage method based on greedy search, and belongs to the technical field of remote sensing satellite earth observation. The method comprises the following steps: firstly, gridding an area target to be observed according to the imaging breadth of a remote sensing satellite; secondly, calculating a grid set of potential coverage of the satellite based on the breadth and the side-sway capability of the satellite within the top-crossing access time of the satellite to the regional target; and then local greedy search and multi-round iteration are carried out to determine a final imaging satellite set and an imaging sequence, and an optimized multi-remote sensing satellite collaborative observation region target scheme is obtained. The method has the advantages of being simple in processing logic, high in flexibility, high in resource utilization rate, high in optimization efficiency and the like, and is suitable for the problem that multiple remote sensing satellites cooperatively cover area targets.

The application is suitable for the technical field of data processing, and provides a method and device for image processing, electronic equipment and storage medium, the method comprising: in response to a first operation initiated by a user, acquiring multiple images; for any image in the multiple images, determining an image order of the image in the multiple images according to a feature parameter of the image in at least one visual feature dimension; and generating an album based on the image order of the multiple images, wherein each image in the album is sorted based on the image order. In the embodiment of the application, the order of the images in the album is determined according to the difference degree between the visual feature dimensions, the similarity of the image content between adjacent images is improved, the continuity of switching between different images in the album by the user is improved, the fragmentation of the picture is reduced, and the use experience of the user is improved.

A method, a computerized apparatus and a computer program product for adaptive learning for image classification. The method comprises applying a set of classification models on a calibration dataset and on a production dataset, and calculating disagreement measurements over the predictions thereof on each dataset. Based on similarity measurement between the disagreement measurement of the calibration and the production datasets, being below a predetermined threshold, a data drift is indicated in the production dataset. The method further comprises determining a training dataset for training a classification model for the production dataset. The training dataset is selected over a plurality of sets of images ordered according to time intervals in which images therein are obtained. The selection is performed based on weights determined for the plurality of sets.

An information processing apparatus according to an embodiment of the present disclosure includes at least one memory storing instructions; and at least one processor that, upon execution of the instructions, is configured to operate as: an acquisition unit configured to acquire imaging order information and an optical image related to radiographic imaging, an identification unit configured to identify a radiation exposure field using the imaging order information and the optical image, a detection unit configured to detect a human body part in the optical image, and an analysis unit configured to, when a plurality of human body parts is detected in the optical image, analyze a human body part closest to the radiation exposure field using the optical image.

This invention proposes a method and system for extracting full business information from multiple power systems using OCR for certificate issuance, comprising: receiving uploaded electronic image data; using OCR image recognition technology to identify the image order number in the uploaded electronic image data, storing the identified electronic image data in an electronic image pool; and classifying and archiving the identified electronic image data in the electronic image pool.

1. Name of the product in this design: The main body of the intelligent content creation graphical user interface for electronic devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface in the electronic device for which protection is sought. 4. The picture or photo that best illustrates the key design points: Design 1 Change State Diagram 1. 5. Design 1 is designated as the basic design. 6. Purpose of Graphical User Interface: A graphical user interface is used for content creation through intelligent agents, such as generating images or videos through intelligent agents. The part of the graphical user interface that requests protection is the main body of the graphical user interface for content creation by intelligent agents. 7. Human-computer interaction method of graphical user interface: In Design 1, when the user selects and marks the picture in the main view of Design 1 and clicks the "Magic Video" button, the graphical user interface changes from the main view of Design 1 to Design 1 Change State Figure 1 to display the picture order on which the generated video is based. When the user clicks the "Next" button in Design 1 Change State Figure 1, the graphical user interface changes from Design 1 Change State Figure 1 to Design 1 Change State Figure 2 to display the generated video and the video playback button. In Design 2, when the user selects and marks images in the main view of Design 2 and clicks the "Magic Video" button, the graphical user interface changes from the main view of Design 2 to Design 2 Change State Diagram 1 to display the image order on which the generated video is based. When the user clicks the "Next" button in Design 2 Change State Diagram 1, the graphical user interface changes from Design 2 Change State Diagram 1 to Design 2 Change State Diagram 2 to display the generated video and the video playback button. In Design 3, when the user clicks the "Magic Video" button in the main view of Design 3, the graphical user interface changes from the main view of Design 3 to Design 3 Change State Diagram 1 to display the image sequence on which the generated video is based. When the user clicks the "Next" button in Design 3 Change State Diagram 1, the graphical user interface changes from Design 3 Change State Diagram 1 to Design 3 Change State Diagram 2 to display the generated video and the video playback button. In Design 4, when the user clicks the "Magic Video" button in the main view of Design 4, the graphical user interface changes from the main view of Design 4 to Design 4 Change State Diagram 1 to display the image sequence on which the generated video is based. When the user clicks the "Next" button in Design 4 Change State Diagram 1, the graphical user interface changes from Design 4 Change State Diagram 1 to Design 4 Change State Diagram 2 to display the generated video and the video playback button. In Design 5, when the user clicks the "Magic Video" button in the main view of Design 5, the graphical user interface changes from the main view of Design 5 to Design 5 Change State Diagram 1 to display the image sequence on which the generated video is based. When the user clicks the "Next" button in Design 5 Change State Diagram 1, the graphical user interface changes from Design 5 Change State Diagram 1 to Design 5 Change State Diagram 2 to display the generated video and the video playback button. In Design 6, when the user clicks the "Magic Video" button in the main view of Design 6, the graphical user interface changes from the main view of Design 6 to Design 6 Change State Diagram 1 to display the image sequence on which the generated video is based. When the user clicks the "Next" button in Design 6 Change State Diagram 1, the graphical user interface changes from Design 6 Change State Diagram 1 to Design 6 Change State Diagram 2 to display the generated video and the video playback button. 8. Other situations requiring explanation: The portions of the graphical user interface shown by the dashed lines in the views of Design 1 to Design 6 do not constitute the parts protected by this design.

A medical image processing apparatus includes an obtaining unit configured to obtain a medical image based on imaging order information, and a determination unit configured to determine, using parameters obtained by machine learning, consistency between the imaging order information and the medical image.

The present application relates to the technical field of image detection, in particular to a panoramic image detection method and device based on image stitching, comprising: performing edge detection processing on each local detection image to obtain an edge image; extracting feature points from the edge image to obtain a feature point set of each local detection image; calculating the number of feature points and the proportion of feature point types in the overlapping area of the images according to the feature point set of each local detection image; and determining the stitching priority of the local detection image according to the number of feature points and the proportion of feature point types, combined with the image definition difference value. The present application determines the stitching priority according to the number and proportion of feature points, which helps to select the best image order and area in the stitching process, thereby reducing stitching errors and visual inconsistency and improving the overall quality of panoramic images.

The invention discloses an industrial DR equipment automatic defect detection method and system based on deep learning, and relates to the technical field of industrial nondestructive testing, and the method comprises the steps: obtaining industrial DR imaging data of a to-be-detected workpiece under a predetermined imaging attitude, and constructing an original image set according to an imaging sequence; and for the industrial DR imaging data in the original image set, based on the change consistency of the pixel gray in the spatial neighborhood, executing spatial continuity analysis, forming an abnormal region in which the gray change is continuously distributed in the space, and determining the abnormal region as a continuous gray abnormal region. Spatial continuity analysis is carried out on industrial DR imaging data, a continuous gray abnormal area is formed and identified, and a main extension direction is further determined based on a spatial distribution form of the continuous gray abnormal area in an image coordinate system, so that directivity description parameters are introduced in an abnormal area level, and the accuracy of the abnormal area is improved. And the continuous gray-scale abnormal region is not processed only as a gray-scale abnormal object any more.

The application discloses a kind of motion target detection method, system, electronic equipment and computer readable storage medium combined with foreground contour extraction, belong to motion target detection technical field.The method includes that CPU end obtains the multiple frames of images to be detected captured for the detection area to be detected, according to image order, corresponding background model is established;GPU end executes the following operation: according to current frame image, corresponding background model is acquired in real time, by VFCE algorithm, according to current frame image and corresponding background model, extract foreground contour image;By VIBE algorithm, current frame image is updated and foreground is extracted, and motion target entity is obtained;According to foreground contour image and motion target entity, obtain motion target sequence.In the process of motion target detection, external interference can be avoided, and clear motion target is obtained;Solve the problem of "motion target detection is susceptible to other factors interference, detection is not accurate" in prior art.

The present invention provides an information processing method that can suitably extract specific frames from contrast-enhanced video. [Solution] The information processing method acquires a contrast-enhanced video, calculates statistical values ​​of pixel values ​​for each frame constituting the contrast-enhanced video, and a computer performs a process to extract a specific frame from the contrast-enhanced video based on the statistical values. Preferably, the statistical values ​​are the variance or mean of the pixel values. More preferably, the information processing method extracts the frame in which the statistical values ​​take an extreme value in a graph where the first axis is the imaging order of the frames and the second axis is the statistical values ​​of each frame.

Provided are a radiography system (2), a radiography method, and a radiography program that can derive an appropriate imaging condition for general imaging in a radiography system (2) capable of executing fluoroscopy and general imaging in a switchable manner. A radiography system (2) derives an imaging condition in general imaging performed after fluoroscopy based on information used for dose control of radiation emitted from a radiation source (10) to a radiation detector (11) in the fluoroscopy and a preregistered imaging order (66) including the imaging condition in the general imaging.

An information processing apparatus generates list image data in which thumbnail images of image data included in an imaging data group imaged in a film camera mode are listed. The list image data is set in a template based on a maximum number of a captured image count in the film camera mode. The thumbnail images are disposed in the template in an imaging order.

The invention provides a tumor pathological image detection method. The tumor pathological image detection method comprises the following steps: sectioning an in-vitro tissue separated from a tumor focus along a preset plane to form a sectioning plane; the coloring agent is used for coloring the section cutting plane to form a to-be-detected object; translating, scanning and imaging along the profile contour of the to-be-detected object by using an imaging device so as to continuously obtain a plurality of local pathological images; and splicing the local pathological images according to the imaging sequence to form an annular image at least covering the profile contour, judging whether the annular image contains tumor cells or not through a machine learning algorithm, and judging that the in-vitro tissue contains a complete tumor when the annular image does not contain the tumor cells. According to the invention, the calculation cost for judging whether the annular image contains the tumor cells or not by a machine learning algorithm is remarkably reduced, and the detection time is remarkably shortened.

The invention provides a tumor pathological image detection method and electronic detection equipment. The tumor pathological image detection method comprises the following steps: sectioning an in-vitro tissue along a preset plane to form a sectioning plane, and staining the sectioning plane by a staining agent to form a to-be-detected object; determining an annular detection area completely covering the profile contour based on the profile contour, performing twice division on the annular detection area along the radial direction to form a plurality of fan-shaped sub-detection areas, reserving part of the fan-shaped sub-detection areas intersecting with the profile contour, and performing translation scanning imaging on the reserved part of the fan-shaped sub-detection areas to continuously obtain a plurality of local pathological images, splicing the local pathological images according to the imaging sequence to form a to-be-detected image area at least covering the profile contour, judging whether the to-be-detected image area contains tumor cells or not through a machine learning algorithm, and judging that the in-vitro tissue contains a complete tumor when the to-be-detected image area does not contain tumor cells. According to the invention, whether the tumor area is completely cut off can be rapidly and effectively judged, and the detection time is remarkably shortened.