Abstract: According to one example for segmenting image data, image data comprising color pixel data, IR data, and depth data is received from a sensor. The image data is segmented into a first list of objects based on at least one computed feature of the image data. At least one object type is determined for at least one object in the first list of objects. The segmentation of the first list of objects is refined into a second list of objects based on the at least one object type. In an example, the second list of objects is output.

Abstract: A methods and system for recognizing a three dimensional object on a base are disclosed. A three dimensional image of the object is received as a three-dimensional point cloud having depth data and color data. The base is removed from the three dimensional point cloud to generate a two-dimensional image representing the object. The two-dimensional image is segmented to determine object boundaries of a detected object. Color data from the object is applied to refine segmentation and match the detected object to a reference object data.

Abstract: A system includes a sensor to capture multiple images of a portion of a first object illuminated by coherent illumination and a time of capture of each of the images; and a processor to compare two images of the multiple images to identify one or more touch points. Each touch point has a difference in value between the two images that is greater than a threshold. Upon determining a spatial shape formed by the identified touch points that corresponds to a pointing end of a pointing object, the system provides at least one of: i) a touch location of the pointing end relative to the first object, where the touch location is based on the spatial shape formed by the identified touch points, or ii) the time of capture of a second image of the two images that produced the spatial shape.

Abstract: A display has a capture mode. A camera is included in the display. A first region of the display including a field of view (FOV) of the camera may be dimmed during the capture mode. A second region of the display is outside the FOV. At least part of the second region is not dimmed during the capture mode.

Abstract: Examples disclosed herein relate to detecting misalignment of a touch sensitive mat Examples include detecting corners of the touch sensitive mat, determining a set of reference corners, performing a comparison of the detected corners of the mat with the set of reference corners, and determining a level of misalignment based on the comparison.

Abstract: A method includes receiving data representing an image captured of an object disposed on a surface in the presence of illumination by a flash light. The technique includes processing the data to identify an object type associated with the object and further processing the data based at least in part on the identified object type.

Abstract: According to one example for capturing three-dimensional data of an object, illumination of an object is initiated, the object is scanned with a sensor, and a 3D representation of the object is created. Illumination of the object may comprise illumination of a surface of the object within a field of view of the sensor.

Abstract: Examples disclosed herein relate to a mat characteristic to process images. Examples include to acquire an image of a mat from a camera in a computing device; to process the image according to the mat characteristic in the computing device; and to display the processed image.

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: A first derived matrix of transport coefficients and a second derived matrix of transport coefficients are derived from a primary matrix of transport coefficients. Each of the transport coefficients describes transport of a respective image forming element from a first position onto one or more image forming elements at a second position. An approximate inverse of the first derived matrix is ascertained. A modified version of a projection image is determined from the projection image, the approximated inverse of the first derived matrix, and the second derived matrix. The modified version of the projection image is rendered from the first position onto a physical medium at the second position.

Abstract: A display has a capture mode. A camera is included in the display. A first region of the display including a field of view (FOV) of the camera may be dimmed during the capture mode. A second region of the display is outside the FOV. At least part of the second region is not dimmed during the capture mode.

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: Embodiments of the present invention disclose a system and method for focus-based touch detection. According to one embodiment, a reference pattern is formed on a surface and an imaging device is configured to detect the reference pattern and the presence of an object within proximity of a contact area of the surface. Furthermore, focus information associated with the reference pattern and/or object is used to determine if the detected object is touching or hovering said contact area.

Abstract: Examples disclosed herein relate to determining a segmentation boundary based on an image representing an object. Examples include capturing, from an infrared (IR) camera disposed above and pointed at a projection screen, an IR image representing an object disposed between the IR camera and the projection screen, based on an intensity of IR light reflected by the object and the projection screen including a surface to specularly reflect IR light. Examples include determining a segmentation boundary based at least in part on at least one boundary between a first set of IR light intensity values and a second set of IR light intensity values, wherein the segmentation boundary represents at least one outer edge of the object based on the IR image.

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: 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: A video sequence control system that includes an input video frame buffer and an output video frame selection component is described. The input video frame buffer receives input video frames from a video source. The output video frame selection component determines the video frame to be output according to a scheduler that provides timing information and modulation information. The timing information includes information regarding when a video frame will be output. The modulation information varies dependent upon the frame types available to be output, wherein the available frame types include at least image frames from the input video frame buffer and functional frames, wherein the at least image frames and functional frames are output according to a pattern defined by the scheduler. In addition based on the timing information, a synchronization output signal is output corresponding to the output of the functional frame.

Abstract: According to an example, a display-camera system includes a transparent display panel and a transparent backlight panel. Light sources emit light into the edge of the transparent backlight panel. A first polarizer, between the light sources and the edge of the transparent backlight panel, polarizes the light emitted from the light sources and the transparent backlight panel directs the polarized light towards the transparent display panel. A camera, adjacent a back surface of the transparent backlight panel captures an image of a scene through the transparent display panel and the transparent backlight panel.

Abstract: A video sequence control system comprising: a video frame sequence component for providing video frames for display by a projector, the video frames having a sequence of at least one crosstalk reduction frame followed an image frames; and a synchronization control component, the synchronization control component communicatively coupled to an image capture device the synchronization control component providing a timing signal to synchronize image capture with the occurrence of the at least one crosstalk reduction frame in the video frame sequence.

Abstract: According to an example, 3D modeling motion parameters may be simultaneously determined for video frames according to different first and second motion estimation techniques. In response to detecting a failure of the first motion estimation technique, the 3D modeling motion parameters determined according to the second motion estimation technique may be used to re-determine the 3D modeling motion parameters according to the first motion estimation technique.