A method for providing similar or related products based on deep-learning, which is performed by a data processing unit of a shopping mall server, includes: acquiring an item image and item information for an item registered in a shopping mall; detecting bounding boxes for one or more objects by object-detecting the item image; setting a bounding box for an object associated with the item based on the item information; creating a main bounding box image by cropping a portion of the item image in the set bounding box; creating a padding image by padding-processing the main bounding box image; extracting a feature vector for the padding image; matching the feature vector with the item and storing the feature vector in a database; and creating the database for a similar or related product search service.

A system and method for risk object identification via causal inference that includes receiving at least one image of a driving scene of an ego vehicle and analyzing the at least one image to detect and track dynamic objects within the driving scene of the ego vehicle. The system and method also include implementing a mask to remove each of the dynamic objects captured within the at least one image. The system and method further include analyzing a level of change associated with a driving behavior with respect to a removal of each of the dynamic objects. At least one dynamic object is identified as a risk object that has a highest level of influence with respect to the driving behavior.

Examples of methods for registering objects are described herein. In some examples, a method includes determining a set of overlap scores based on a set of orientations between a first bounding box of a three-dimensional (3D) object model and a second bounding box of a 3D scan of an object. In some examples, the method includes registering the 3D scan with the 3D object model based on the set of overlap scores.

A method and an apparatus for obtaining 3D information of a vehicle are provided. The method includes: first determining a body boundary line of a first vehicle, and then determining an observation angle and/or an orientation angle of the first vehicle based on the body boundary line.

An image identification method is applied to an image surveillance apparatus and includes utilizing an object detecting function to generate a first bounding box and a second bounding box within a surveillance image, utilizing a foreground detecting function to generate a plurality of markers within the surveillance image, defining some of the plurality of markers which conform to the first bounding box as a first marker group and further defining other markers which do not conform to the first bounding box as a second marker group, determining whether the second marker group conforms to the second bounding box, and deciding whether the second bounding box belongs to an error of the object detecting function in accordance with a determination result.

A system for capturing images for training an item identification model obtains an identifier of an item. The system detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system causes the platform to rotate. The system causes at least one camera to capture an image of the item while the platform is rotating. The system extracts a set of features associated with the item from the image. The system associates the item to the identifier and the set of features. The system adds a new entry to a training dataset of the item identification model, where the new entry represents the item labeled with the identifier and the set of features.

A computer-implemented method includes obtaining an input polygon mesh representing at least part of a three-dimensional scene and comprising a plurality of input polygons, and obtaining mapping data for mapping at least part of an image to a region of the input polygon when the three-dimensional scene is rendered. Said region extends at least partway across the plurality of input polygons. The method includes using the mapping data to generate one or more test polygons to match or approximate said region of the input polygon mesh. Each of the generated test polygons is distinct from each of said plurality of input polygons.

The present disclosure provides an image processing method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: obtaining a to-be-processed video comprising m frames of images, m being a positive integer greater than or equal to 2; placing a three-dimensional model on a target plane of a first frame of image of the to-be-processed video, a plurality of feature points of a model surface of the three-dimensional model falling on the target plane; determining three-dimensional coordinates of the plurality of feature points of the model surface in a world coordinate system and pixel coordinates of the plurality of feature points of the model surface on the first frame of image; determining, according to the three-dimensional coordinates of the plurality of feature points of the model surface in the world coordinate system and the pixel coordinates of the plurality of feature points of the model surface on the first frame of image, a pose of a camera coordinate system of the first frame of image relative to the world coordinate system; determining, according to the target plane, the three-dimensional coordinates of the plurality of feature points of the model surface in the world coordinate system, and the pixel coordinates of the plurality of feature points of the model surface on the first frame of image, poses of camera coordinate systems of a second frame of image to an m.sup.th frame of image of the to-be-processed video relative to the world coordinate system; and replacing the three-dimensional model with a target model and placing the target model on the world coordinate system to generate, according to the pose of the camera coordinate system of each frame of image of the to-be-processed video relative to the world coordinate system, a target video comprising the target model.

A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The mobile robot may include cameras for capturing images and videos and include microphones for capturing audio of its surroundings. To improve privacy by preventing confidential information from being transmitted, the mobile robot may detect text in images and modify the images to make the text illegible before transmitting the images. The mobile robot may also detect human voice in audio and modify audio to make the human voice unintelligible before transmitting the audio.

In aspects of text importance spatial layout, a computing device implements a processing device to receive a text importance vector that includes designations of visual properties for constituent words of a text phrase. Spatial layouts of the text phrase are determined, with each spatial layout being a different displayable representation of the constituent words arranged based on the designations of the visual properties in the text importance vector for each of the constituent words. Feature vectors are generated, each feature vector represents a spatial layout of the text phrase and includes measurement properties of each of the constituent words in the respective spatial layout. The spatial layouts are ranked based on a metric that indicates a degree of similarity of the measurement properties of each of the constituent words in a respective spatial layout matching to the visual properties for the constituent words as designated in the text importance vector.