A computer-implemented method of using augmented reality (AR) to detect a plane and a spatial configuration of an object, the method comprising the steps of detecting one or more edges of the object; identifying one or more lines of the object; filtering the one or more lines of the object; iteratively approximating one or more distributions of the one or more lines; and determining boundaries of the object.

This disclosure relates to a method for rendering images. First, a user request is received from a user interface to access an image effect renderer recipe, comprising conditional logic and non-visual image data, from an effect repository. Next, at least one image signal is received. Objects are identified within the image signal(s). The image effect renderer recipe is processed via an effect renderer recipe interpreter to generate image processing steps and image processing prioritizations. The image processing steps are then ordered in accordance with the image processing prioritizations. Next, an image processor applies the image processing steps to the identified objects of the image signal(s) to generate at least one processed image signal. The processed image signal(s) are then displayed on a display device.

The invention relates to a TMB classification method and system and a TMB analysis device based on a pathological image, comprising: performing TMB classification and marking and pre-processing on a known pathological image to construct a training set; training a convolutional neural network by means of the training set to construct a classification model; pre-processing a target pathological image of a target case to obtain a plurality of target image blocks; classifying the target image blocks by means of the classification model to acquire an image block TMB classification result of the target case; and acquiring an image TMB classification result of the target case by means of classification voting using all the image block TMB classification results. The invention further relates to a TMB analysis device based on a pathological image. The TMB classification method of the invention has advantages of accuracy, a low cost and fast rapid.

An imaging apparatus provided inside a vehicle is provided. The imaging apparatus is capable of capturing images with different degrees of sensitivity, and the imaging apparatus captures an image for recognizing an inside of the vehicle with high sensitivity, and captures an image for recognizing an outside of the vehicle with low sensitivity.

The degree of differentiation of a cell in tissue is precisely determined. An estimating device (1) includes: a binarizing section (41) configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section (42) configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b1) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section (43) configured to calculate statistics of the calculated numbers (b1, b0) and ratio (R); and an estimating section (44) configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.

Systems and methods for accelerated object detection in a surveillance system are provided. According to an embodiment, video frames captured by a camera are received by a processing resource of a surveillance system. Pixels of each video frame are partitioned into cells each representing a rectangular block of the pixels. The background cells within a particular video frame are estimated by comparing each of the cells of the particular video frame to a corresponding cell of other video frames. A number of ROIs within the particular video frame is detected by: (i) identifying active cells within the particular video frame based on the estimated background cells; and (ii) identifying the number of clusters of cells within the particular video frame by clustering the active cells. Then, object detection is caused to be performed within the number of ROIs by feeding the number of ROIs to a machine learning model.

An image processing device 10 includes: a segmentation unit 21 that segments an input image 200 representing a result of observing an ocean surface region and a land surface region from overhead into ocean surface block images 211 and land surface block images 212, based on a segmentation criterion 210; a first determination unit 22 that determines a binarization criterion 220 for the ocean surface block images 211, based on a scattering model for electromagnetic waves in the ocean surface region; a second determination unit 23 that determines a binarization criterion 230 for the land surface block images 212, based on the binarization criterion 220 and a positional relationship between the ocean surface block images and the land surface block images; and a generation unit 25 that generates a land mask image 250 by performing binarization processing on the input image 200 based on the binarization criterion 220 and the binarization criterion 230. Consequently, the image processing device 10 improves the accuracy in generating the land mask image for differentiating between the ocean surface region and the land surface region in remote sensing technology.

An image processing method executed by an image includes acquiring image data, acquiring region of interest data indicating a targeted region in recognition processing for an object included in the image data, acquiring information of a candidate region based on a degree of being similar to an object included in the image data, estimating an object region in the acquired image data based on the region of interest data and the information of a candidate, performing a preview display of a plurality of candidate images to be cropped on a display unit by using information of the estimate object region, and receiving a selection by a user from among the plurality of displayed candidate images. Cropping is performed on the image data that corresponds to the candidate image, for a received selection.

Provided are an image processing method and apparatus, and a computer storage medium. The method includes that: first segmentation is performed on a to-be-processed image to determine at least one target image region in the to-be-processed image; second segmentation is performed on the at least one target image region to determine first segmentation results of a target in the at least one target image region; and fusion and segmentation are performed on the first segmentation results and the to-be-processed image to determine a second segmentation result of the target in the to-be-processed image.

Provided in the present application are a method and a device for extracting a blood vessel wall, a medical imaging system, and a non-transitory computer-readable storage medium. The method for extracting a blood vessel wall comprises acquiring a medical image, determining at least one first-order feature in the medical image, and extracting, on the basis of the at least one first-order feature, a blood vessel wall image from the medical image.