Abstract: A user identification system includes an image recognition network to analyze image data and generate shape data based on the image data. The system also includes a generalist network to analyze the shape data and generate general category data based on the shape data. The system further includes a specialist network to compare the general category data with a characteristic to generate narrow category data. Moreover, the system includes a classifier layer including a plurality of nodes to represent a classification decision based on the narrow category data.

Abstract: Configurations are disclosed for presenting virtual reality and augmented reality experiences to users. The system may comprise an image capturing device to capture one or more images, the one or more images corresponding to a field of the view of a user of a head-mounted augmented reality device, and a processor communicatively coupled to the image capturing device to extract a set of map points from the set of images, to identify a set of sparse points and a set of dense points from the extracted set of map points, and to perform a normalization on the set of map points.

Abstract: Configurations are disclosed for presenting virtual reality and augmented reality experiences to users. The system may comprise a vision system having one or more optical elements to project one or more images to a user, a mapping database comprising map data corresponding to one or more real objects of the world, wherein the mapping database receives inputs from at least a component of one or more wearable augmented reality display systems, and a processor communicatively coupled to the mapping database for retrieving the map data and processing the retrieved map data to determine one or more output parameters, wherein the processor further controls the vision system in a manner such that the one or more images are projected to the user based at least in part on the determined output parameters.

Abstract: A non-binary affinity measure between any two data points for a supervised classifier may be determined. For example, affinity measures may be determined for tree, kernel-based, nearest neighbor-based and neural network supervised classifiers. By providing non-binary affinity measures using supervised classifiers, more information may be provided for clustering, analyzing and, particularly, for visualizing the results of data mining.

Abstract: A non-binary affinity measure between any two data points for a supervised classifier may be determined. For example, affinity measures may be determined for tree, kernel-based, nearest neighbor-based and neural network supervised classifiers. By providing non-binary affinity measures using supervised classifiers, more information may be provided for clustering, analyzing and, particularly, for visualizing the results of data mining.

Abstract: A non-binary affinity measure between any two data points for a supervised classifier may be determined. For example, affinity measures may be determined for tree, kernel-based, nearest neighbor-based and neural network supervised classifiers. By providing non-binary affinity measures using supervised classifiers, more information may be provided for clustering, analyzing and, particularly, for visualizing the results of data mining.

Abstract: For a first feature of a dataset having a plurality of features, training a classifier to predict the first feature in terms of other features in the data set to obtain a trained classifier; scrambling the values of a second feature in the data set to obtain a scrambled data set, executing the trained classifier on the scrambled data set, determining predictive importance of the seconds feature in predicting the first feature based at least in part on the accuracy of the trained classifier in predicting the first feature when executed with the scrambled data set and creating a graph of the data set in which each of the first and the second features is a node of the graph and a label on an edge between the first node and the second node is based at least in part on the predictive importance of the first feature in terms of the second feature.

Abstract: In one embodiment, the present invention includes a method of forming windows corresponding to a data point of a data set, successively expanding the windows, determining a local hill for the windows, re-centering the windows on the local hill, and merging any of the windows within a selected distance of each other. The windows formed may be substantially the same size as a single data point, in one embodiment. The merged windows may be recorded as possible merge points of a hierarchical cluster formed from the data set. Other embodiments are described and claimed.

Abstract: An imaging system configured to take panoramic pictures is disclosed. In one embodiment, the imaging system includes a camera, range finder associated with the camera and configured to provide depth information for objects within a field of view of the camera; and a processor coupled to receive information from the camera and depth information from the range finder, and configured to unwrap pictures taken by the camera according to the depth information.

Abstract: Method and apparatus of the present invention include an image-capturing device coupled to a computer system. An image captured by the image-capturing device is analyzed using a face tracking technique to learn behaviors of the user. This may include determining if the user is paying attention. The attention may be toward a display of the computer system. When the user does not pay attention, power consumption of one or more components of the computer system is reduced.

Abstract: In one embodiment, a method includes converting unsupervised data into supervised data using multiple processes and training multiple supervised classifiers with the supervised data of the processes. In such manner, supervised classifiers may be used to classify unsupervised data. Affinity measures may be determined and data clustered using the resulting trained classifiers. Other embodiments are described and claimed.

Abstract: The present invention relates to a simple to use intuitive interface for mobile devices and, in particular, to a method and apparatus to permit the motion of a mobile device to serve as an input to the mobile device. In various embodiments, the relative and/or absolute motion, distance and/or acceleration for instance, of the mobile device may be mapped to correspond to particular commands. In one embodiment, this permits the display screen of the mobile device to serve as a virtual window to view a large data set by modifying the view of the displayed data according to the motion of the mobile device. In another embodiment, the present invention allows the user to navigate a virtual space, displayed on the screen of the mobile device, by the motion of the mobile device in real space. Additionally, the user may also control the different displayed views of virtual objects by the relative and/or absolute motion of the mobile device.

Abstract: In one embodiment, the present invention includes a method of successively splitting an analog function into high and low ranges and calculating a binary mask for these ranges to obtain a plurality of data regions at a plurality of split levels, and training binary classifiers on the plurality of data regions of at least one of the split levels. In such manner, binary classifiers may be used to classify an analog function. Other embodiments are described and claimed.

Abstract: A radiation sensing structure includes red, green and blue photodiodes stacked Above an infrared radiation sensing photodiode.

Abstract: The present invention relates to a simple to use intuitive interface for mobile devices and, in particular, to a method and apparatus to permit the motion of a mobile device to serve as an input to the mobile device. In various embodiments, the relative and/or absolute motion, distance and/or acceleration for instance, of the mobile device may be mapped to correspond to particular commands. In one embodiment, this permits the display screen of the mobile device to serve as a virtual window to view a large data set by modifying the view of the displayed data according to the motion of the mobile device. In another embodiment, the present invention allows the user to navigate a virtual space, displayed on the screen of the mobile device, by the motion of the mobile device in real space. Additionally, the user may also control the different displayed views of virtual objects by the relative and/or absolute motion of the mobile device.

Abstract: The present invention relates to a simple to use intuitive interface for mobile devices and, in particular, to a method and apparatus to permit the motion of a mobile device to serve as an input to the mobile device. In various embodiments, the relative and/or absolute motion, distance and/or acceleration for instance, of the mobile device may be mapped to correspond to particular commands. In one embodiment, this permits the display screen of the mobile device to serve as a virtual window to view a large data set by modifying the view of the displayed data according to the motion of the mobile device. In another embodiment, the present invention allows the user to navigate a virtual space, displayed on the screen of the mobile device, by the motion of the mobile device in real space. Additionally, the user may also control the different displayed views of virtual objects by the relative and/or absolute motion of the mobile device.

Abstract: Method and apparatus of the present invention include an image-capturing device coupled to a computer system. An image captured by the image-capturing device is analyzed using a face tracking technique to learn behaviors of the user. This may include determining if the user is paying attention. The attention may be toward a display of the computer system. When the user does not pay attention, power consumption of one or more components of the computer system is reduced.

Abstract: A system and method for recognizing gestures. The method comprises obtaining image data and determining a hand pose estimation. A frontal view of a hand is then produced. The hand is then isolated the background. The resulting image is then classified as a type of gesture. In one embodiment, determining a hand pose estimation comprises performing background subtraction and computing a hand pose estimation based on an arm orientation determination. In another embodiment, a frontal view of a hand is then produced by performing perspective unwarping and scaling. The system that implements the method may be a personal computer with a stereo camera coupled thereto.

Abstract: The present invention allows for the locations of points of interest in a calibration object in a calibration image for a digital camera to be identified automatically. The image is an array of pixels corresponding to the calibration object, which has a known reference pattern. In a preferred embodiment, the invention includes receiving an array of pixels produced by a camera, classifying each pixel of the image as light or dark, extracting contours from the image by identifying lines between light and dark pixels, comparing the extracted contours to the shapes of the known reference pattern, and identifying the shapes of the known reference pattern in the image using the extracted contours. Preferably, the image is a color image, and the color information in the pixels of the image are converted into gray scale values to render the image as a gray scale image before the pixels are classified.

Abstract: A radiation sensing structure includes red, green and blue photodiodes stacked Above an infrared radiation sensing photodiode.