Abstract: To integrate a sensory property such as occlusion, shadowing, reflection, etc. among physical and notional (e.g. virtual/augment) visual or other sensory content, providing an appearance of similar occlusion, shadowing, etc. in both models. A reference position, a physical data model representing physical entities, and a notional data model are created or accessed. A first sensory property from either data model is selected. A second sensory property is determined corresponding with the first sensory property, and notional sensory content is generated from the notional data model with the second sensory property applied thereto. The notional sensory content is outputted to the reference position with a see-through display. Consequently, notional entities may appear occluded by physical entities, physical entities may appear to cast shadows from notional light sources, etc.

Abstract: First and second images are captured at first and second focal lengths, the second focal length being longer than the first focal length. Element sets are defined with a first element of the first image and a corresponding second element of the second image. Element sets are identified as background if the second element thereof is more in-focus than or as in-focus as the first element. Background elements are subtracted from further analysis. Comparisons are based on relative focus, e.g. whether image elements are more or less in-focus. Measurement of absolute focus is not necessary, nor is measurement of absolute focus change; images need not be in-focus. More than two images, multiple element sets, and/or multiple categories and relative focus relationships also may be used.

Abstract: A wearable sensor vehicle with a bio-input sensor and a processor. When the vehicle is worn, the sensor is arranged so as to sense bio-input from the user. The sensor senses bio-input, the processor compares the bio-input to a standard, and if the standard is met the processor indicates a response. The user may be uniquely identified from the bio-input. One or more systems on or communicating with the vehicle may be controlled transparently, without requiring direct action by the user. Control actions may include security identification of the user, logging in to accounts or programs, setting preferences, etc. The sensor collects bio-input substantially without instruction or dedicated action from the user; the processor compares bio-input against the standard substantially without instruction or dedicated action from the user; and the processor generates and/or implements a response based on the bio-input substantially without instruction or dedicated action from the user.

Abstract: A machine-implemented method includes establishing a virtual or augmented reality entity, and establishing a state for the entity having a state time and state properties including a state spatial arrangement. The data entity and state are stored, and are subsequently received and outputted at a time other than the state time so as to exhibit a “virtual time machine” functionality. An apparatus includes a processor, a data store, and an output. A data entity establisher, a state establisher, a storer, a data entity receiver, a state receiver, and an outputter are instantiated on the processor.

Abstract: Disclosed are methods and apparatuses to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.

Abstract: A system and method for calculating step count and associated physical movement intensity. In some embodiments, the system includes at least a camera to capture video of user motions, which are analyzed, and the corresponding step count and physical exercise intensity are deduced by the disclosed method.

Abstract: First and second images are captured at first and second focal lengths, the second focal length being longer than the first focal length. Element sets are defined with a first element of the first image and a corresponding second element of the second image. Element sets are identified as background if the second element thereof is more in-focus than or as in-focus as the first element. Background elements are subtracted from further analysis. Comparisons are based on relative focus, e.g. whether image elements are more or less in-focus. Measurement of absolute focus is not necessary, nor is measurement of absolute focus change; images need not be in-focus. More than two images, multiple element sets, and/or multiple categories and relative focus relationships also may be used.

Abstract: To distinguish a region (e.g. hand) within a data set (e.g. digital image), data elements (e.g. pixels) representing a transition (e.g. hand outline) are identified. The direction toward the region is determined, for example using weighted direction matrices yielding a numerical maximum when aligned inward. A test element displaced one or more steps inward from the boundary element is tested against a standard for identifying the region. If the tested element meets the standard, that element is identified as part of the region. By examining data elements away from the transition, noise in the transition itself is avoided without altering the transition (e.g. by smoothing) while still only examining a linear data set (i.e. a contour or trace of the feature rather than a flooded interior thereof). The direction to the exterior of the region, an exterior contour, other features, and/or the transition also may be identified/followed.

Abstract: World data is established, including real-world position and/or real-world motion of an entity. Target data is established, including planned or ideal position and/or motion for the entity. Guide data is established, including information for guiding a person or other subject in bringing world data into match with target data. The guide data is outputted to the subject as virtual and/or augmented reality data. Evaluation data may be established, including a comparison of world data with target data. World data, target data, guide data, and/or evaluation data may be dynamically updated. Subjects may be instructed in positions and motions by using guide data to bring world data into match with target data, and by receiving evaluation data. Instruction includes physical therapy, sports, recreation, medical treatment, fabrication, diagnostics, repair of mechanical systems, etc.

Abstract: First and second images are captured at first and second focal lengths, the second focal length being longer than the first focal length. Element sets are defined with a first element of the first image and a corresponding second element of the second image. Element sets are identified as background if the second element thereof is more in-focus than or as in-focus as the first element. Background elements are subtracted from further analysis. Comparisons are based on relative focus, e.g. whether image elements are more or less in-focus. Measurement of absolute focus is not necessary, nor is measurement of absolute focus change; images need not be in-focus. More than two images, multiple element sets, and/or multiple categories and relative focus relationships also may be used.

Abstract: To integrate a sensory property such as occlusion, shadowing, reflection, etc. among physical and notional (e.g. virtual/augment) visual or other sensory content, providing an appearance of similar occlusion, shadowing, etc. in both models. A reference position, a physical data model representing physical entities, and a notional data model are created or accessed. A first sensory property from either data model is selected. A second sensory property is determined corresponding with the first sensory property, and notional sensory content is generated from the notional data model with the second sensory property applied thereto. The notional sensory content is outputted to the reference position with a see-through display. Consequently, notional entities may appear occluded by physical entities, physical entities may appear to cast shadows from notional light sources, etc.

Abstract: World data is established, including real-world position and/or real-world motion of an entity. Target data is established, including planned or ideal position and/or motion for the entity. Guide data is established, including information for guiding a person or other subject in bringing world data into match with target data. The guide data is outputted to the subject as virtual and/or augmented reality data. Evaluation data may be established, including a comparison of world data with target data. World data, target data, guide data, and/or evaluation data may be dynamically updated. Subjects may be instructed in positions and motions by using guide data to bring world data into match with target data, and by receiving evaluation data. Instruction includes physical therapy, sports, recreation, medical treatment, fabrication, diagnostics, repair of mechanical systems, etc.

Abstract: First and second images are captured at first and second focal lengths, the second focal length being longer than the first focal length. Element sets are defined with a first element of the first image and a corresponding second element of the second image. Element sets are identified as background if the second element thereof is more in-focus than or as in-focus as the first element. Background elements are subtracted from further analysis. Comparisons are based on relative focus, e.g. whether image elements are more or less in-focus. Measurement of absolute focus is not necessary, nor is measurement of absolute focus change; images need not be in-focus. More than two images, multiple element sets, and/or multiple categories and relative focus relationships also may be used.

Abstract: Disclosed are method and apparatus to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.

Abstract: In the method, a processor generates a three dimensional interface with at least one virtual object, defines a stimulus of the interface, and defines a response to the stimulus. The stimulus is an approach to the virtual object with a finger or other end-effector to within a threshold of the virtual object. When the stimulus is sensed, the response is executed. Stimuli may include touch, click, double click, peg, scale, and swipe gestures. The apparatus includes a processor that generates a three dimensional interface with at least one virtual object, and defines a stimulus for the virtual object and a response to the stimulus. A display outputs the interface and object. A camera or other sensor detects the stimulus, e.g. a gesture with a finger or other end-effector, whereupon the processor executes the response. The apparatus may be part of a head mounted display.

Abstract: Disclosed are method and apparatus to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.

Abstract: World data is established, including real-world position and/or real-world motion of an entity. Target data is established, including planned or ideal position and/or motion for the entity. Guide data is established, including information for guiding a person or other subject in bringing world data into match with target data. The guide data is outputted to the subject as virtual and/or augmented reality data. Evaluation data may be established, including a comparison of world data with target data. World data, target data, guide data, and/or evaluation data may be dynamically updated. Subjects may be instructed in positions and motions by using guide data to bring world data into match with target data, and by receiving evaluation data. Instruction includes physical therapy, sports, recreation, medical treatment, fabrication, diagnostics, repair of mechanical systems, etc.

Abstract: Disclosed are methods and apparatuses for searching images. An image is received and a first search path is defined for the image. The first search path may be a straight line, horizontal, and/or near the bottom of the image, and/or may begin at one edge and move toward the other. A transition is defined for the image, distinguishing a feature to be found. The image is searched for the transition along the first search path. When the transition is detected, the image is searched along a second search path that follows the transition. The apparatus includes an image sensor and a processor. The sensor is adapted to obtain images. The processor is adapted to define a first search path and a transition for the image, to search for the transition along the first search path, and to search along a second search path upon detecting the transition, following the transition.

Abstract: Disclosed are methods and apparatuses to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.

Abstract: Comprehensive 2D learning images are collected for learning subjects. Standardized 2D gallery images of many gallery subjects are collected, one per gallery subject. A 2D query image of a query subject is collected, of arbitrary viewing aspect, illumination, etc. 3D learning models, 3D gallery models, and a 3D query model are determined from the learning, gallery, and query images. A transform is determined for the selected learning model and each gallery model that yields or approximates the query image. The transform is at least partly 3D, such as 3D illumination transfer or 3D orientation alignment. The transform is applied to each gallery model so that the transformed gallery models more closely resemble the query model. 2D transformed gallery images are produced from the transformed gallery models, and are compared against the 2D query image to identify whether the query subject is also any of the gallery subjects.