Abstract: A structure for determining a plane in a depth image includes dividing a portion of a depth image into a plurality of areas, fitting a two-dimensional line to depth points in each of the plurality of areas, and combining two or more of the plurality of two-dimensional lines to form a three-dimensional plane estimate.

Abstract: A system and method for generating compact iris representations based on a database of iris images includes providing full-length iris codes for iris images in a database, where the full-length iris code includes a plurality of portions corresponding to circumferential rings in an associated iris image. Genuine and imposter score distributions are computed for the full-length iris codes, and code portions are identified that have a contribution that provides separation between imposter and genuine distributions relative to a threshold. A correlation between remaining code portions is measured. A subset of code portions having low correlations within the subset is generated to produce a compact iris representation.

Abstract: Embodiments are directed to a system for implementing a restricted-operation region. The system includes an instruction development module configured to be utilized in the development of a set of instructions that implement an operation policy of the restricted-operation region. The set of instructions is configured to, when interpreted, implement the operation policy by controlling at least one function of a vehicle that attempts to operate within the restricted-operation region. The system further includes a first transmitter configured to transmit the set of instructions to the vehicle, wherein a processor of the vehicle is configured to interpret the set of instructions based at least in part on a determination that the vehicle is attempting to operate within the restricted-operation region.

Abstract: Embodiments are directed to a system for implementing a restricted-operation region. The system includes an instruction development module configured to be utilized in the development of a set of instructions that implement an operation policy of the restricted-operation region. The set of instructions is configured to, when interpreted, implement the operation policy by controlling at least one function of a vehicle that attempts to operate within the restricted-operation region. The system further includes a first transmitter configured to transmit the set of instructions to the vehicle, wherein a processor of the vehicle is configured to interpret the set of instructions based at least in part on a determination that the vehicle is attempting to operate within the restricted-operation region.

Abstract: A set of conditions associated with monitoring a given environment is received. One or more locations in the given environment are determined, based on at least a portion of the received set of conditions, for which data is to be acquired. The given environment is traversed through the one or more locations. Data at the one or more locations is acquired. The acquired data is stored for subsequent review. One or more of the above steps are performed under control of a processing device at least a part of which is mounted on a mobile platform that is configured to move through the given environment. Determination of the one or more locations in the given environment may further include determining an extended region to be observed.

Abstract: Mechanisms are provided for classifying an obstacle as an asset type. The mechanisms receive a digital image of an obstacle from an image capture device of an automated robot. The mechanisms perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle. The mechanisms determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle. The mechanisms update a map data structure for a physical premises in which the obstacle is present based on the final asset type.

Abstract: Mechanisms are provided for classifying an obstacle as an asset type. The mechanisms receive a digital image of an obstacle from an image capture device of an automated robot. The mechanisms perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle. The mechanisms determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle. The mechanisms update a map data structure for a physical premises in which the obstacle is present based on the final asset type.

Abstract: A method includes the following steps. A speech input is received. At least two speech recognition candidates are generated from the speech input. A scene related to the speech input is observed using one or more non-acoustic sensors. The observed scene is segmented into one or more regions. One or more properties for the one or more regions are computed. One of the speech recognition candidates is selected based on the one or more computed properties of the one or more regions.

Abstract: A method includes the following steps. A vicinity from which speech input to a speech recognition system originates is determined. Non-acoustic data from the vicinity of the speech is obtained using one or more non-acoustic sensors. A subject speaker is identified as the source of the speech input from the obtained non-acoustic data. One or more non-acoustic attributes of the subject speaker is analyzed. A speech recognition system is adjusted based on the one or more analyzed non-acoustic attributes.

Abstract: A method includes the following steps. A vicinity from which speech input to a speech recognition system originates is determined. Non-acoustic data from the vicinity of the speech is obtained using one or more non-acoustic sensors. A subject speaker is identified as the source of the speech input from the obtained non-acoustic data. One or more non-acoustic attributes of the subject speaker is analyzed. A speech recognition system is adjusted based on the one or more analyzed non-acoustic attributes.

Abstract: A method includes the following steps. A speech input is received. At least two speech recognition candidates are generated from the speech input. A scene related to the speech input is observed using one or more non-acoustic sensors. The observed scene is segmented into one or more regions. One or more properties for the one or more regions are computed. One of the speech recognition candidates is selected based on the one or more computed properties of the one or more regions.

Abstract: A method, system and computer program for categorizing heart diseases is presented. An example method includes receiving a series of cardiac images of a heart, the cardiac images including a myocardium, and interior fibrous muscles of the heart. Cardiac images are segmented, into a myocardium segmentation showing an anatomical shape and a motion of the myocardium, and an interior fibrous muscles segmentation showing an anatomical shape and a motion of the interior fibrous muscles. The myocardium segmentation is converted into a regional characterization of the anatomical shape and motion of the myocardium. The interior fibrous muscles segmentation is converted to a regional characterization of the anatomical shape and motion of the interior fibrous muscles. Heart conditions are characterized based on the regional characterizations of the anatomical shape and the motion of the myocardium and the interior fibrous muscles.

Abstract: A method and non-transitory program for determining a plane in a depth image includes dividing a portion of a depth image into a plurality of areas, fitting a two-dimensional line to depth points in each of the plurality of areas, and combining two or more of the plurality of two-dimensional lines to form a three-dimensional plane estimate.

Abstract: Mechanisms are provided for classifying an obstacle as an asset type. The mechanisms receive a digital image of an obstacle from an image capture device of an automated robot. The mechanisms perform a classification operation on the digital image of the obstacle to identify a proposed asset type classification for the obstacle. The mechanisms determine a final asset type for the obstacle based on the proposed asset type classification for the obstacle. The mechanisms update a map data structure for a physical premises in which the obstacle is present based on the final asset type.

Abstract: An approach that manages moving surveillance cameras is described. In one embodiment, there is a system for managing images generated from a movable camera. In this embodiment, the system comprises a motion estimation component configured to determine camera motion. A camera motion alert component is configured to generate an alert in response to a determination that the camera has moved or the camera has ceased moving.

Abstract: A method, system and computer program for categorizing heart diseases is presented. An example method includes receiving a series of cardiac images of a heart, the cardiac images including a myocardium, and interior fibrous muscles of the heart. Cardiac images are segmented, into a myocardium segmentation showing an anatomical shape and a motion of the myocardium, and an interior fibrous muscles segmentation showing an anatomical shape and a motion of the interior fibrous muscles. The myocardium segmentation is converted into a regional characterization of the anatomical shape and motion of the myocardium. The interior fibrous muscles segmentation is converted to a regional characterization of the anatomical shape and motion of the interior fibrous muscles. Heart, conditions are characterized based on the regional characterizations of the anatomical shape and the motion of the myocardium and the interior fibrous muscles.

Abstract: A display device comprises a plurality of light emitting elements in a layer on a substrate, a plurality of microprisms positioned over the layer, a plurality of light detectors on the substrate, each light detector respectively corresponding to a light emitting element of the plurality of light emitting elements, and a display screen, wherein the light detectors are used to sense at least one property of an item in contact with the display screen.

Abstract: One or more processors generate a set of facial appearance parameters that are derived from a first facial image. One or more processors generate a graphics control vector based, at least in part, on the set of facial appearance parameters. One or more processors render a second facial image based on the graphics control vector. One or more processors compare the second facial image to the first image. One or more processors generate an adjusted vector by adjusting one or more parameters of the graphics control vector such that a degree of similarity between the second facial image and the first facial image is increased. The adjusted vector includes a biometric portion. One or more processors generate a first face representation based, at least in part, on the biometric portion of the adjusted vector.

Abstract: Under the present invention, item verification is automated and expedited. Specifically, items to be purchased can be scanned by the shopper using a barcode reader (e.g., a scanner), attached to or positioned near the checkout station. As items are scanned, they are identified based on their barcode, and added to an item list. Item verification can then be performed at checkout using imaging technology. Specifically, as items are scanned, an item verification unit will capture an appearance thereof (via a camera). Item verification software within the item verification unit will access a database that associates items with their images/appearances. The appearance will be compared for consistency to the identity as determined based on the scan. In general, the item verification unit is a separate unit from the cash register, but adapted to work in conjunction therewith (e.g., as a pluggable system, via wireless communication, etc.).

Abstract: A set of conditions associated with monitoring a given environment is received. One or more locations in the given environment are determined, based on at least a portion of the received set of conditions, for which data is to be acquired. The given environment is traversed through the one or more locations. Data at the one or more locations is acquired. The acquired data is stored for subsequent review. One or more of the above steps are performed under control of a processing device at least a part of which is mounted on a mobile platform that is configured to move through the given environment. Determination of the one or more locations in the given environment may further include determining an extended region to be observed.