Abstract: Examples described herein may include a computing system that may include movable surface and a model acquisition engine configured to acquire three-dimensional model data representing a first object disposed on the movable surface. The computing system may also include a communication engine to send the model data to another computing system and to receive from the other computing system manipulation data associated with the model data. The computing system may further include a movement and projection engine to move the movable surface in accordance with the received manipulation data.

Abstract: An example method is described in which files are received by a computer system. A first user interface is displayed on a first display of the computer system. The first user interface includes multiple user interface elements representing the files. In response to detecting a first user gesture selecting a selected user interface element from the multiple user interface elements via the first display, a second user interface is generated and displayed on a second display of the computer system. The second user interface includes a detailed representation of a file represented by the selected user interface element. In response to detecting a second user gesture interacting with the selected user interface element via the first display, the first user interface on the first display is updated to display the interaction with the selected user interface.

Abstract: An example processor-implemented method for generating corners of a display area is provided. The method comprises detecting a dominant line for each side of the display area, each dominant line used to identify corners, detecting subline segments on each side of the display area, determining a distance between the corners identified by the dominant lines and the sub-line segments on each side, and generating the corners of the display area based on the distance.

Abstract: Recognizing a command may include monitoring a tangible reference with a depth sensor, maintaining a virtual reference approximately on calibrated three dimensional coordinates of the tangible reference, maintaining a touch space adjacent the virtual reference, and recognizing a command when a predetermined object enters the touch space.

Abstract: An example system, including a projector unit, an all-in-one computer comprising a calibration module and attachable to the projector unit, and a plurality of sensors communicatively coupled to the all-in-one computer is provided. In addition, the all-in-one computer stores mapping information relating to mappings between the plurality of sensors and the projector unit in a common coordinate system. Further, the calibration module calibrates the plurality of sensors and the projector unit using the mapping information.

Abstract: A plurality of homography operators define respective mappings between pairs of coordinate spaces, wherein the coordinate spaces include a coordinate space of a first visual sensor, a virtual coordinate space, and a coordinate space of a second visual sensor. Calibration between the first and second visual sensors is provided using the plurality of homography operators.

Abstract: Examples disclosed herein relate to aligning content displayed from a projector on to a touch sensitive mat. Examples include detecting a border of the mat, wherein the mat includes a surface area of a first spectral reflectance characteristic on to which the projector is to project the content, and the border of a second spectral reflectance characteristic different from the first spectral reflectance characteristic surrounding a perimeter of the surface area. As an example, detecting the border of the mat generally includes differentiating the second spectral reflectance characteristic of the border from the first spectral reflectance characteristic of the surface area. Examples include detecting a border of the content displayed on to the mat, and adjusting projector settings for the border of the content displayed on to the mat to fit within the detected border of the mat.

Abstract: Examples disclosed herein relate to determining a segmentation boundary based on images representing an object. Examples include an IR image based on IR light reflected by an object disposed between an IR camera and an IR-absorbing surface, a color image representing the object disposed between the color camera and the IR-absorbing surface, and determining a segmentation boundary for the object.

Abstract: Examples disclosed herein relate to identifying a target touch region of a touch-sensitive surface based on an image. Examples include a touch input detected at a location of a touch-sensitive surface, an image representing an object disposed between a camera that captures the image and the touch-sensitive surface, identifying a target touch region of a touch-sensitive surface based on an image, and rejecting the detected touch input when the location of the detected touch input is not within any of the at least one identified target touch region of the touch-sensitive surface.

Abstract: Detecting an object includes receiving depth data and infrared (IR) data from a depth sensor. A first background subtraction is performed on the IR data to create a first mask, and a second background subtraction is performed on the IR data to create a second mask. The first and second masks and the depth data are merged to create a third mask.

Abstract: A plurality of homography operators define respective mappings between pairs of coordinate spaces, wherein the coordinate spaces include a coordinate space of a first visual sensor, a virtual coordinate space, and a coordinate space of a second visual sensor. Calibration between the first and second visual sensors is provided using the plurality of homography operators.

Abstract: A method for assessing events detected by a surveillance system includes assessing the likelihood that the events correspond to events being monitored from feedback in response to a condition set by a user. Classifiers are created for the events from the feedback. The classifiers are applied to allow the surveillance system improve its accuracy when processing new video data.

Abstract: Detecting an object includes receiving depth data and infrared (IR) data from a depth sensor. A first background subtraction is performed on the IR data to create a first mask, and a second background subtraction is performed on the IR data to create a second mask. The first and second masks and the depth data are merged to create a third mask.

Abstract: Recognizing a command may include monitoring a tangible reference with a depth sensor, maintaining a virtual reference approximately on calibrated three dimensional coordinates of the tangible reference, maintaining a touch space adjacent the virtual reference, and recognizing a command when a predetermined object enters the touch space.

Abstract: Among other things, methods, systems and computer program products are described for detecting and tracking a moving object in a scene. One or more residual pixels are identified from video data. At least two geometric constraints are applied to the identified one or more residual pixels. A disparity of the one or more residual pixels to the applied at least two geometric constraints is calculated. Based on the detected disparity, the one or more residual pixels are classified as belonging to parallax or independent motion and the parallax classified residual pixels are filtered. Further, a moving object is tracked in the video data. Tracking the object includes representing the detected disparity in probabilistic likelihood models. Tracking the object also includes accumulating the probabilistic likelihood models within a number of frames during the parallax filtering.

Abstract: Among other things, methods, systems and computer program products are described for detecting and tracking a moving object in a scene. One or more residual pixels are identified from video data. At least two geometric constraints are applied to the identified one or more residual pixels. A disparity of the one or more residual pixels to the applied at least two geometric constraints is calculated. Based on the detected disparity, the one or more residual pixels are classified as belonging to parallax or independent motion and the parallax classified residual pixels are filtered. Further, a moving object is tracked in the video data. Tracking the object includes representing the detected disparity in probabilistic likelihood models. Tracking the object also includes accumulating the probabilistic likelihood models within a number of frames during the parallax filtering.