An information display method, a device and a storage medium. The method includes: acquiring a first image including a first object in a video, determining whether a second object is present in the first image, and when it is determined that the second object is present in the first image and that the second object satisfies a preset positional relationship with the first object, superimposing a first material on an area where the second object is located in the first image. Using the above method, it is realized that when the second object is detected in the image, any material is superimposed on the area where the second object is located, so as to avoid the problem of not being able to use part of special effects or express information when the second object satisfies the preset positional relationship with the first object.

A method and apparatus for recognizing a three-dimensional gesture based on mark points, a device and a storage medium are provided. An image collected by each camera is acquired; for each image, an identifier and a corresponding two-dimensional coordinate position of each mark point in the image are determined; a three-dimensional coordinate position of each mark point in a coordinate system of a corresponding camera is determined according to the two-dimensional coordinate position of each mark point and a calibration parameter of the corresponding camera; the three-dimensional coordinate position is converted to an initial three-dimensional coordinate position in a coordinate system of the designated space; and a target three-dimensional coordinate position of each mark point in the coordinate system of the designated space is determined according to the initial three-dimensional coordinate position of each mark point in each image and the identifier of the corresponding mark point.

The present disclosure relates to a method for three-dimensionally modeling an organ through image segmentation. The three-dimensional modeling of an organ includes the operations of: receiving one or more pieces of medical image data for a specific bodily organ of a target object; setting a region of interest with respect to the bodily organ based on the one or more pieces of medical image data; forming one or more blocks corresponding to the region of interest, wherein the blocks include a portion of the bodily organ corresponding to the regions of interest; setting a segment algorithm for each of the blocks; generating first image data respectively performing 3D modeling of portions contained in the blocks based on algorithms set to the blocks; and merging the first image data, and generating a three-dimensional section image data with respect to the entire bodily organ.

The subject technology provides object recognition systems and methods that can be used to identify objects of interest in an image. An image such as live preview may be generated by a display component of the electronic device and an object of interest may be detected in the image. The detected object of interest may be classified using a classification model. Subsequent to classification, a confidence level in identifying the object of interest may be determined. In response to determining that the confidence level does not meet a confidence level threshold for identifying the object of interest, a request for a user input is generated. Based on the user input, the object of interest is identified using an object recognition model.

A data processing device comprises an analyser to analyse successive images captured by a camera and to detect an optically detectable marker in the captured images, a first location detector to detect a location of the optically detectable marker with respect to a location of the camera according to a first detection mode and to generate a first detection result, a second location detector to detect the location of the optically detectable marker with respect to the location of the camera according to a second detection mode different to the first detection mode and to generate a second detection result, and a processor to select at least one of the first detection result and the second detection result and to generate data indicative of the location of the optically detectable marker with respect to the location of the camera based on the selection.

A landslide recognition method based on Laplacian pyramid remote sensing image fusion includes: performing original remote sensing image reconstruction based on extracted local features and global features of remote sensing images through a Laplacian pyramid fusion module to generate a fused image, constructing a deep learning semantic segmentation model through a semantic segmentation network, labeling the fused image to obtain a dataset of landslide disaster label map, and training the deep learning semantic segmentation model by the dataset, and then storing when a loss curve is fitted and a landslide recognition accuracy of remote sensing image of the deep learning semantics segmentation model meets a requirement by modifying a structure of the semantic segmentation network and adjusting parameters of the deep learning semantics segmentation model. Combined with the image fusion model based on Laplacian pyramid, the method can provide effective decision-making basis for prevention and mitigation of landslide disasters.

A detection system method of color balancing an image includes receiving an image of a test area of a pad which includes a color of a reaction between a test substance and at least one reagent on the test area of the pad, an alignment code having detection system identification information, at least one color calibration block indicia for aligning with a camera as the image is being captured, and test identification information for analyzing the test substance during a colorimetric analysis. The method further includes collecting an array of pixels of RGB values for each pixel in the image, evaluating a captured color of the at least one color calibration block in the image, and performing the colorimetric analysis on the reaction between the test substance and the at least one reagent.

An object recognition processing apparatus includes: a model data acquisition unit configured to acquire three-dimensional model data of an object; a measurement unit configured to acquire measurement data including three-dimensional position information of the object; a position/orientation recognition unit configured to recognize a position/orientation of the object based on the three-dimensional model data and the measurement data; a similarity score calculation unit configured to calculate a similarity score indicating a degree of similarity between the three-dimensional model data and the measurement data in a position/orientation recognition result of the object; a reliability calculation unit configured to calculate an index indicating a feature of a three-dimensional shape of the object, and calculate a reliability of the similarity score based on the index; and an integrated score calculation unit configured to calculate an integrated score indicating a quality of the position/orientation recognition result based on the similarity score and the reliability.

A portable terminal configured to estimate the number of used wet towels, or oshiboris, stored in a collection box, including: an information receiving unit for receives pre-collection store information; a photographing unit for capturing an image of used oshiboris stored in a collection box from above; a learning model storing unit for storing a learning model trained by a neural network; an image acquiring unit for acquiring an image for estimation photographed by the photographing unit. The estimating unit estimates a number of used oshiboris from the image for estimation acquired by the image acquiring unit, with the learning model stored in the learning model storing unit, by the neural network. The display unit displays an estimation result estimated by the estimating unit. The information transmitting unit transmits post-collection store information added the estimation result to the pre-collection store information received by the information receiving unit to the core system.

A portable terminal configured to estimate the number of used wet towels, or oshiboris, stored in a collection box, including: an information receiving unit for receives pre-collection store information; a photographing unit for capturing an image of used oshiboris stored in a collection box from above; a learning model storing unit for storing a learning model trained by a neural network; an image acquiring unit for acquiring an image for estimation photographed by the photographing unit. The estimating unit estimates a number of used oshiboris from the image for estimation acquired by the image acquiring unit, with the learning model stored in the learning model storing unit, by the neural network. The display unit displays an estimation result estimated by the estimating unit. The information transmitting unit transmits post-collection store information added the estimation result to the pre-collection store information received by the information receiving unit to the core system.