Systems and methods are described for navigating through a data space. The navigating comprises detecting a gesture of a body from gesture data received via a detector. The gesture data is absolute three-space location data of an instantaneous state of the body at a point in time and physical space. The detecting comprises identifying the gesture using the gesture data. The navigating comprises translating the gesture to a gesture signal, and navigating through the data space in response to the gesture signal. The data space is a data-representational space comprising a dataset represented in the physical space.

Methods and apparatus provide for: capturing an image of an object, which includes a face of a person wearing an optical display apparatus by which to observe a stereoscopic image that contains a first parallax image and a second parallax image obtained when the object in a three-dimensional (3D) space is viewed from different viewpoints; identifying the optical display apparatus included in the image of the object; and generating an image of the face of the person that does not include the optical display apparatus by excluding the identified optical display apparatus, and instead by adding features of the face of the person to a region in which the identified optical display apparatus is excluded.

In an example embodiment, may be embodied in a data analysis apparatus comprises a control unit configured to provide data representative of a three dimensional image, the three dimensional image including at least a three dimensional coordinate space which includes at least one plane that divides the three dimensional coordinate space into at least two regions, a display unit configured to produce the three dimensional image based on the data representative of the three dimensional image, and an input unit configured to provide data representative of at least one of a movement and a position of the at least one plane. In other example embodiments, the present disclosure may be embodied in a data analysis server, a data analysis system, and/or a computer readable medium.

A functional device is disclosed that can collect and process data/information/parameter values from one or more sensors and compares the same with one or more predefined/threshold value to suggest one or more actions and/or generate alerts/messages/suggestions to be performed by one or a combination of remote system, wearer, home automation network, healthcare provider, doctor, caretaker, among other stakeholders. Communication between the functional device, home automation server and a computational server that stores the data is also disclosed.

This document describes methods, systems and computer program products directed to imaging blood cells. The subject matter described in this document can be embodied in a method of classifying white blood cells (WBCs) in a biological sample on a substrate. The method includes acquiring, by an image acquisition device, a plurality of images of a first location on the substrate, and classifying, by a processor, objects in the plurality of images into WBC classification groups. The method also includes identifying, by a processor, objects from at least some classification groups, as unclassified objects, and displaying, on a user interface, the unclassified objects and at least some of the classified objects.

Apparatus, methods, and articles of manufacture for implementing crowdsourcing pipelines that generate training examples for machine learning expression classifiers. Crowdsourcing providers actively generate images with expressions, according to cues or goals. The cues or goals may be to mimic an expression or appear in a certain way, or to “break” an existing expression recognizer. The images are collected and rated by same or different crowdsourcing providers, and the images that meet a first quality criterion are then vetted by expert(s). The vetted images are then used as positive or negative examples in training machine learning expression classifiers.

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.

An apparatus and a method are provided for detecting entities in a numerical image, wherein the apparatus includes a computing unit configured for detecting, based on a histogram vector determined on the basis of gradient and partitioning information, the presence of at least one of the entities in the image, a signaling unit in signal communication with the computing unit, and configured for being activated when the computing unit detects the presence of at least one of the entities in the image, memory containing partitioning information, and configured for allowing access to the partitioning information on the basis of the gradient information, wherein each piece of partitioning information identifies at least one of the partitioning elements that allow the computing unit to quantize the gradient information.

Disclosed is a method and apparatus for detecting and ranging cloud features. The method comprises: obtaining image data(e.g. using a camera (200); classifying, as a cloud feature, an image segment (502-508) of the image data; determining a plurality of moments of the image segment (502-508); using the determined plurality of moments, determining a geometric representation of that image segment (502-508); and, using the geometric representation, determining a distance between the cloud feature represented by that image segment (502-508) and an entity that obtained the image data.