Visual localization method and related apparatus are disclosed. In the method, a first candidate image sequence is determined from image library, the image library being configured to construct electronic map, image frames in the first candidate image sequence being sequentially arranged according to degrees of matching with first image, and the first image being an image collected by a camera; an order of the image frames in the first candidate image sequence is adjusted according to target window to obtain second candidate image sequence, the target window being multiple successive image frames including target image frame and determined from the image library, the target image frame being an image matching with second image, which is collected by the camera before the first image is collected, in the image library; and target posture of the camera when the first image is collected is determined according to the second candidate image sequence.

An embodiment in accordance with the present invention includes a novel informatics method for the integration of different variables or ontologies using an advanced unsupervised machine learning method which will be used to visualize factors not visible to the human observer. These factors can be unknown characteristics between imaging datasets and other factors which will provide insights into the structure of the data. This invention is called the Informatics Radiomics Integration System (IRIS), For example, using clinical data and radiological images, the present invention can determine the relationship between, the structure, clinical variables, histological variables and “distance or relationship” between each input parameter. This integration of information can be used to determine the association between the variables and the relationship to each other for improved diagnosis or for treatment response.

Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Aspects of the subject disclosure may include, for example, storing, in a database, a decorating style preference of a user; receiving, from user equipment of the user, one or more images (and/or one or more 2D environment models and/or one or more 3D environment models) depicting an environment in which remodeling is desired; generating, via a machine learning process, a first model to present by the user equipment, the generating the first model being based upon the decorating style preference and the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), the first model comprising a first remodeling proposal for the environment; sending, to the user equipment, the first model, the sending of the first model facilitating display by the user equipment of a first depiction of the environment as proposed by the first remodeling proposal; receiving, from the user equipment, feedback information regarding the first remodeling proposal; generating, via the machine learning process, a second model to present by the user equipment, the generating the second model being based upon the decorating style preference, the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), and the feedback information, the second model comprising a second remodeling proposal for the environment; and sending, to the user equipment, the second model, the sending of the second model facilitating display by the user equipment of a second depiction of the environment as proposed by the second remodeling proposal. Other embodiments are disclosed.

Embodiments of the present disclosure provide an image processing method and apparatus, a computer device and a computer readable storage medium. The method includes: in response to detecting that a number of images in an image set to be processed exceeds a preset threshold, acquiring a first cluster dimension corresponding to the images in the image set; selecting a second cluster dimension from a plurality of pre-stored dimensions, the second cluster dimension being different from the first cluster dimension; and clustering the images in the image set according to the second cluster dimension to acquire a plurality of first image subsets of the image set.

The present disclosure provides various approaches for smart area monitoring suitable for parking garages or other areas. These approaches may include ROI-based occupancy detection to determine whether particular parking spots are occupied by leveraging image data from image sensors, such as cameras. These approaches may also include multi-sensor object tracking using multiple sensors that are distributed across an area that leverage both image data and spatial information regarding the area, to provide precise object tracking across the sensors. Further approaches relate to various architectures and configurations for smart area monitoring systems, as well as visualization and processing techniques. For example, as opposed to presenting video of an area captured by cameras, 3D renderings may be generated and played from metadata extracted from sensors around the area.

This application discloses an image clustering method performed by a computer device. This method includes: determining a first clustering parameter based on M image groups; for a target image group, dividing it into two image groups to obtain M+1 reference image groups, and determining a reference clustering parameter based on the M+1 reference image groups as a second clustering parameter for the target image group representing a clustering degree of images in the M+1 reference image groups; choosing, among respective second clustering parameters of the M image groups, a target second clustering parameter representing the highest clustering degree of images in the M image groups; and when the target second clustering parameter has a clustering degree not lower than the first clustering parameter, dividing a target image group corresponding to the target second clustering parameter into two image groups to obtain M+1 image groups.

Examples of the present disclosure describe systems and methods for detecting monotone speech. In aspects, audio data provided by a user may be received a device. Pitch values may be calculated and/or extracted from the audio data. The non-zero pitch values may be divided into clusters. For each cluster, a Pitch Variation Quotient (PVQ) value may be calculated. The weighted average of PVQ values across the clusters may be calculated and compared to a threshold for determining monotone speech. Based on the comparison, the audio data may be classified as monotone or non-monotone and an indication of the classification may be provided to the user in real-time via a user interface. Upon the completion of the audio session in which the audio data is received, feedback for the audio data may be provided to the user via the user interface.

A method of controlling a mobile robot includes a learning initial operation of acquiring images for respective points, generating descriptors that respectively correspond to a plurality of feature points extracted from the images, and generating nodes that correspond to the images acquired at the respective points, a label generation operation of generating a label descriptor based on the plurality of descriptors, a localization initial operation of acquiring a localization image when a jumping case occurs, and generating respective localization descriptors corresponding to a plurality of localization feature points extracted from the localization image, a comparison target selection operation of matching the label descriptor to each of the localization descriptors and selecting one or more comparison target nodes corresponding to the matched label descriptor, and a last node selection operation of selecting a node estimated as the current position among the one or more comparison target node.

A method of performing object recognition is performed by an electronic device and includes obtaining a spatial map of a space, using a first recognition model, recognizing one or more objects in the space, to obtain first object information of the objects, and dividing the space into a plurality of subset spaces, based on the obtained spatial map and the obtained first object information. The method further includes determining at least one second recognition model to be allocated to each of the plurality of subset spaces into which the space is divided, based on characteristic information of each of the plurality of subset spaces, and using the determined at least one second recognition model allocated to each of the plurality of subset spaces, performing object recognition on each of the plurality of subset spaces, to obtain second object information.