Systems and methods provide a mathematical framework and corresponding hardware for defining, manipulating, and using perception according to a non-Euclidean space. Embodiments may be used to approximate existing mappings or to create new mappings for arbitrary stimuli and numerous tasks. An efficient electronic perceptual apparatus projects multivariate input data onto a perceptual manifold using Jacobian matrices, and methods presented herein generate Jacobians so as to replicate non-Euclidean perceptual spaces. Examples of perceptual stimuli that may benefit from the embodiments include digital images, audio, and video. In one example, lossy compression of digital images according to a non-Euclidean space advantageously produces compressed images with lower entropy for a given image quality, or better image quality for a given entropy, as compared to JPEG compression.

A pupil estimation device is provided as follows. A reference position is calculated using detected peripheral points of an eye in a captured image. A difference vector representing a difference between a pupil central position and the reference position is calculated with a regression function by using (i) the reference position and (ii) a brightness of a predetermined region in the captured image. The pupil central position is obtained by adding the calculated difference vector to the reference position.

System and method for classifying data objects occurring in an unstructured dataset, comprising: extracting feature vectors from the unstructured dataset, each feature vector representing an occurrence of a data object in the unstructured dataset; classifying the feature vectors into feature vector sets that each correspond to a respective object class from a plurality of object classes; for each feature vector set: performing multiple iterations of a clustering operation, each iteration including clustering feature vectors from the feature vector set into clusters of similar feature vectors and identifying outlier feature vectors, wherein for at least one iteration after a first iteration of the clustering operation, outlier feature vectors identified in a previous iteration are excluded from the clustering operation; and outputting a key cluster for the feature vector set from a final iteration of the multiple iterations, the key cluster including a greater number of similar feature vectors than any of the other clusters of the final iteration; and assembling a dataset that includes at least the feature vectors from the key clusters of the feature vector sets.

An imaging apparatus and a control method thereof are provided. The method for controlling the imaging apparatus includes acquiring images having a same focal length by performing continuous imaging in a predetermined time when a user's imaging command is input, calculating motion vectors using the images and separating a foreground and a background of a first image among the images based on the calculated motion vectors and color information of the first image, and performing out focusing based on the separated foreground and the separated background.

Embodiments of the present disclosure describe mechanisms for a radar-based indoor localization and tracking system. One example can include monitoring unit that includes a radar source, a camera unit, and one or more processors coupled to the radar element and the camera unit. The monitoring unit is configured to generate point cloud data associated with an object; execute Point Cloud Library (PCL) preprocessing based, at least, on the point cloud data; execute Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering; execute multi-object tracking on the object; and execute an image PCL overlay based on the point cloud data to generate real-time data associated with the object.

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.

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.

A method and apparatus for displaying digital photos on a digital display with a network connection module. The method includes identifying two different photos in a digital photo collection that include a matching photo content, and displaying the two different photos simultaneously in a split screen on the digital display. The two different photos can be matched from related photo clusters. An integrated camera can be used to automatically determine an identity of a frame viewer to determine photos to display, and can capture gesture-based feedback. The photos can be filtered and cropped at the receiver side.

Detecting multiple people in a room includes detecting a point cloud corresponding to at least one user moving in the room, forming a bounding box corresponding to coordinates of points of the point cloud, and determining if the point cloud corresponds to multiple people based on a size of the point cloud, a presence of separate clusters of points in the point cloud, and/or detecting one or more people entering or leaving the room. Detecting a point cloud may include using a tracking device. The size of the point cloud may be compared with a size of a point cloud corresponding to a single user of the room. Detecting one or more people entering or leaving the room may include previously detecting entrances of the room by tracking movement of people in the room and determining a location where movement results in more or less people in the room.

The present invention provides a method of generating a robust global map using a plurality of limited field-of-view cameras to capture an environment. Provided is a method for generating a three-dimensional map comprising: receiving a plurality of sequential image data wherein each of the plurality of sequential image data comprises a plurality of sequential images, further wherein the plurality of sequential images is obtained by a plurality of limited field-of-view image sensors; determining a pose of each of the plurality of sequential images of each of the plurality of sequential image data; determining one or more overlapping poses using the determined poses of the sequential image data; selecting at least one set of images from the plurality of sequential images wherein each set of images are determined to have overlapping poses; and constructing one or more map portions derived from each of the at least one set of images.