The present disclosure provides an object identification and tracking method and an object identification and tracking apparatus. The object identification and tracking method includes: detecting M first objects in a target image; obtaining N second objects in tracking data in first video data, a matching weight of each second object with each first object varying with a time interval between a current image where the second object is located and the target image; matching the M first objects with the N second objects, so as to determine a correspondence between each first object and each second object; and tracking the first object in accordance with a matching result of the M first objects and the N second objects.

An image analysis device that ease association between an SAR image and an object is provided. The image analysis device includes: a stable reflection point identification unit that identifies, based on a plurality of synthetic aperture radar (SAR) images, stable reflection points at which reflection is stable in the plurality of SAR images; a phase identification unit that identifies a phase at each of the stable reflection points, based on the plurality of SAR images and a location of the stable reflection point in the plurality of SAR images; and a clustering means that clusters the stable reflection points, based on a Euclidian distance between each of the stable reflection points and a correlation of the phases at each of the stable reflection points.

The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

This disclosure relates generally to adaptive learning based systems and methods for optimization of unsupervised clustering. The embodiments of present disclosure herein address unresolved problem of involving manual intervention in data preparation, annotating or labelling training data to train classifiers, and taking a number of clusters directly as an input from the users for data classification. The method of the present disclosure provides a fully unsupervised optimized approach for auto clustering of input data that automatically determines the number of clusters for the input data by leveraging concepts of graph theory and maximizing a cost function. The method of present disclosure is capable of handling a new data by continuously and incrementally improving the clusters. The method of present disclosure is domain agnostic, scalable, provides expected level of accuracy for real-world data, and helps in minimizing utilization of powerful processors leading to reduced overall cost.

A computer-implemented method for place recognition including: obtaining information identifying an image of a first scene; identifying a plurality of pixel clusters in the image; generating a set of feature vectors associated with the pixel clusters; generating a graph of the scene; adding a first edge between a first node and a second node in response to determining that a first property associated with a first pixel cluster is similar to a second property associated with a second pixel cluster; generating a vector representation of the graph; calculating a measure of similarity between the vector representation of the graph and a reference vector representation associated with a second scene; and determining that the first scene and the second scene are associated with a same place in response to determining that the measure of similarity is less than a threshold.

A gesture recognition method and a related apparatus are provided, to obtain a first point cloud data set by filtering an original point cloud data set collected by a radar apparatus. The first point cloud data set includes a plurality of frames of first point cloud subsets, the first point cloud subset includes a first cluster center, the first cluster center is a cluster center of a plurality of pieces of point cloud data in the first point cloud subset, a maximum horizontal distance between any two first cluster centers meets a first preset condition, and duration of the first point cloud data set meets the first preset condition. Point cloud data whose motion track does not match gesture motion can be effectively filtered out. Gesture recognition is performed by using the first point cloud data set obtained by filtering.

A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. The integrated camera is used to automatically determine an identity of a frame viewer and can capture gesture-based feedback. The displayed photos are automatically shown and/or changed according to the detected viewers. The photos can be filtered and cropped at the receiver side. Clustering photos by content is used to improve display and to respond to photo viewer desires.

Various embodiments described herein relate to classification of events by pattern recognition in multivariate time series data associated with one or more assets. In this regard, a request to classify events associated with one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aggregated multivariate data associated with the one or more assets is obtained based on the asset descriptor. Furthermore, one or more events associated with the aggregated multivariate data is labeled based respective defined data signatures for respective defined events associated with a defined event attribute. A dashboard visualization is then provided to an electronic interface of a computing device. The dashboard visualization includes data associated with the one or more events.

A method of processing multimedia data, a device, and a medium, which relates to a field of an artificial intelligence technology, in particular to fields of knowledge graph and deep learning. The method of processing the multimedia data includes: recognizing the multimedia data so as to obtain at least one key information of the multimedia data; querying a predetermined knowledge base according to the at least one key information, so as to determine a multimedia name associated with the at least one key information and an association degree between the multimedia name and the at least one key information; and determining, in the multimedia name, a name of the multimedia data based on a similarity between alternative multimedia data for the multimedia name and the multimedia data, in response to the association degree being less than a first threshold value.

The present disclosure provides an object detection method. The method includes: acquiring a scene image of a scene; acquiring a three-dimensional point cloud corresponding to the scene; segmenting the scene image according to the three-dimensional point cloud corresponding to the scene to generate a plurality of region proposals; and performing object detection on the plurality of region proposals to determine a target object to be detected in the scene image. In addition, The present disclosure also provides an object detection device, a terminal device, and a medium.