Hewlett-Packard Development Company, L.p.

Seiko Epson Corporation

Kar-Han Tan

Jing Xiao

Ian N Robinson

Anoop Kumar Bhattacharjya

Jinman Kang

Daniel George Gelb

Qingxiong Yang

Daniel R Tretter

David Bradley Short

Wei Hong

Ali Zandifar

Irwin E Sobel

Bowon Lee

Yun David Tang

Ramin Samadani

Tomio Sonehara

David A Fattal

Marco Fiorentino

John George Apostolopoulos

Mitchell D Trott

Alan Andrew McReynolds

Otto Kenneth Sievert

Jian Fan

Victor Ivashin

Bradley Neal Suggs

Su Wang

Mehrban Jam

Paul William Martin

Amit A Marathe

Vuong Le

Divya Sharma

Yong Zhao

Claudio Rosito Jung

Henrique Weber

Irvin E Sobel

Edward W R Bagg

Edward Bagg

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.

Object capture and illumination

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.

Processing data representing images of objects to classify the objects

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.

Process image according to mat characteristic

According to one example for outputting image data, an image comprising a surface and an object are captured on a sensor. An object mask based on the captured image is created on a processor. A first composite image based on the object mask and a source content file is created. In an example, the first composite image is projected to the surface.

Image projection

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.

Coherent illumination for touch point identification

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.

Corner generation in a projector display area

In some examples, a computing device processes depth images to capture an interaction of an object relative to an interaction plane.

System and method for processing depth images which capture an interaction of an object relative to an interaction plane

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.

Touch sensitive mat of a system with a projector unit

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.

Image data segmentation

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.

Projection screen for specularly reflecting light

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.

Camera included in display

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.

View projection

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.

Focus-based touch and hover detection

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.

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Sunnyvale, CA, United States of America

Kar-Han Tan