The present disclosure relates to a data construction and learning system and method based on a method of splitting and arranging multiple images. The data construction and learning system based on a method of splitting and arranging multiple images includes an input unit configured to receive images captured by a plurality of cameras disposed in a vehicle, a memory in which a program for merging the images into a single image and estimating information on a road situation and an object has been stored, and a processor configured to execute the program. The processor merges and recognizes, as one situation, road situations and objects redundantly included in the images.

A dynamic prototype convolution network (DPCN) can achieve sufficient information interaction between support features from a support image and query features from a query image for performing Few-shot semantic segmentation (FSS). A dynamic convolution module (DCM) can generate dynamic filters from a support foreground. Then, information interaction can be achieved by convolution operations over query features, such as by using these dynamic filters. A support activation module (SAM) and a feature filtering module (FFM) can be used to mine context information from a query feature. The SAM can learn to generate a pseudo mask for a query image. The FFM can refine the pseudo mask to filter background information from a query feature. Thus, information both from query and support can be used to achieve more accurate prediction. The DPCN can be used to perform k-shot segmentation.

A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

Provided are an information processing apparatus, an information processing method, and a storage medium capable of acquiring feature information relating to sweat gland pores that can realize highly accurate identification of an individual. The information processing apparatus includes: a sweat gland pore extraction unit that extracts sweat gland pores from an image including a skin marking; and an information acquisition unit that acquires sweat gland pore information including position information about the sweat gland pore and directional information about the sweat gland pore for each of the sweat gland pores.

Provided are VR image processing method and apparatus. The method includes: rendering left-eye and right-eye viewpoint regions based on left-eye and right-eye view angles respectively, to obtain left-eye and right-eye viewpoint images; determining a candidate region based on positions of the left-eye and right-eye view angles, and selecting a point in the candidate region as a peripheral image view angle; rendering left-eye and right-eye viewpoint peripheral regions based on the peripheral image view angle, to obtain a same viewpoint peripheral image; and splicing the viewpoint peripheral image with the left-eye viewpoint image and with the right-eye viewpoint image to obtain a left-eye complete image and a right-eye complete image.

Embodiments are directed to video segmentation based on a query. Initially, a first segmentation such as a default segmentation is displayed (e.g., as interactive tiles in a finder interface, as a video timeline in an editor interface), and the default segmentation is re-segmented in response to a user query. The query can take the form of a keyword and one or more selected facets in a category of detected features. Keywords are searched for detected transcript words, detected object or action tags, or detected audio event tags that match the keywords. Selected facets are searched for detected instances of the selected facets. Each video segment that matches the query is re-segmented by solving a shortest path problem through a graph that models different segmentation options.

The present disclosure relates to method and system for extracting information based on user interactions performed on screen of a computing system. Method includes receiving processed input data comprising video capturing screen and user interactions events occurring from user interactions, and co-ordinate information. Thereafter, computing system determines Regions of Interest (RoI) on screen based on captured user interactions, events and co-ordinate information, and identifies type of user interaction performed with at least one screen element in the RoI, as either keyboard or mouse type interaction. Subsequently, computing system performs one of: determining type of screen element as a text box or a table based on pattern recognition when type of user interaction is identified as keyboard type interaction or determining type of screen element to be selectable User Interface elements and extracting content and label of selectable UI elements, when interaction is identified as mouse type interaction.

A method for assessing damage of vehicle, an apparatus for assessing damage of vehicle and an electronic device using same. The method for assessing the damage of the vehicle includes: acquiring vehicle images; processing the vehicle images by a first model to obtain a component identification result, and the component identification result includes a component name, and at least one of a component region and a component mask of a vehicle component; processing the vehicle images by a second model to obtain a damage identification result, and the damage identification result includes a damage morphology and at least one of a damage region and a damage region mask of the vehicle component; and fusing the component identification result and the damage identification result to obtain a damage assessment result.

An issuing apparatus is configured to acquire an image obtained by capturing a collation image printed on an information medium via a camera as a first captured image. A collation apparatus is configured to acquire an image obtained by capturing a collation image printed on an information medium to be collated via a camera as a second captured image, generate a corrected image obtained by performing density correction on the first captured image, by predicting a density change at a time point at which the second captured image is acquired based on an elapsed time from a time point at which the first captured image is acquired, and determine a collation region for collation between the first captured image and the second captured image based on a difference image obtained by obtaining a difference between the second captured image and the corrected image for each pixel.

The present disclosure accurately detects the position of a shelf label disposed on a display shelf. A shelf label detection device may be provided with a shelf label position correction unit and a shelf label position identification unit. The shelf label position correction unit corrects a manual shelf label position set including a shelf label position that has been set in advance for a reference camera image, using an automatic shelf label position set which includes a shelf label position detected from a monitoring camera image using image recognition, thereby generating a corrected shelf label position set. The shelf label position identification unit uses the corrected shelf label position set to identify the shelf label position in the monitoring camera image.