Techniques for providing a virtual touch screen are described. An example of a computing device with a virtual touch screen includes a projector to project a user interface image and a depth camera to detect objects in the vicinity of the user interface image. The computing device also includes a touch service that receives image data from the depth camera and analyzes the image data to generate touch event data. The computing device also includes a User Input (UI) device driver that receives the touch event data from the touch service and reports the touch event data to an operating system of the computing device. The touch service and UI device driver are system level software that is operable prior to a user logging onto the computing device.

Provided is a method for reconstructing content image data. The method includes selecting a first point and a second point in a first image of first content, selecting a third point and a fourth point in a second image of second content (the second image is an image corresponding to the first image and the third point and the fourth point are points in an image corresponding to the first point and the second point, respectively), generating a first reference vector using the first point and the second point, generating a second reference vector using the third point and the fourth point, calculating a rotation, scale, and transformation (RST) value from the first image to the second image using the first reference vector and the second reference vector; and reconstructing the second content using the calculated RST value.

Methods and apparatuses for watermark embedding and extracting are provided. A method for watermark embedding includes obtaining a carrier object and watermark information to be embedded in the carrier object; generating at least one encoding region including the watermark information according to the watermark information, the at least one encoding region including a plurality of template lattices; obtaining a watermark image according to the at least one encoding region; and embedding the watermark image in the carrier object.

A system is provided for retargeting advertisements and is based on a database storing user behavior data related to information for a plurality of consumers. An index-lookup processes the user behavior data and translates the user behavior data into a plurality of indices. A data loading module processes the plurality of indices and generates a multi-indexed user behavior cube. An online query module receives a request for an advertisement and processes the request to determine one or more related user attributes and retrieves user behavior data from the multi-indexed user behavior cube using the related user attributes. An ad recommendation module receives the set of user behaviors and generates a list of recommended advertisements using the set of user behaviors data and an advertiser retargeting criterion.

Digital fingerprints include data indicative of interior features or structures of an object. The physical object may be rigid or malleable. The digital fingerprints may also include data indicative of features on an exterior surface of the object. Digital fingerprints may uniquely identify an object with respect to other objects, even with respect to other objects of a same type or class of objects. The technology may be relatively invariant to changes in scale, rotation, affine, homography, perspective, and illumination as between a reference digital fingerprint and a later acquired or generated digital fingerprint. Digital fingerprints may be used to authenticate an object as being a second instance or appearance of a previously digitally fingerprinted object.

An object (e.g., a driver's license) is tested for authenticity using imagery captured by a consumer device (e.g., a mobile phone camera). Corresponding data is sent from the consumer device to a remote system, which has secret knowledge about features indicating object authenticity. The phone, or the remote system, discerns the pose of the object relative to the camera from the captured imagery. The remote system tests the received data for the authentication features, and issues an output signal indicating whether the object is authentic. This testing involves modeling the image data that would be captured by the consumer device from an authentic object—based on the object's discerned pose (and optionally based on information about the camera optics), and then comparing this modeled data with the data sent from the consumer device. A great variety of other features and arrangements are also detailed.

Arrangements involving portable devices (e.g., smartphones and tablet computers) are disclosed. One arrangement enables a content creator to select software with which that creator's content should be rendered—assuring continuity between artistic intention and delivery. Another utilizes a device camera to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some technologies concern improvements to the user interfaces associated with such devices. For example, some arrangements enable discovery of both audio and visual content, without any user requirement to switch modes. Other technologies involve use of these devices in connection with shopping, text entry, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern computational photography. A great variety of other features and arrangements are also detailed.

The geometric pose of a patch of watermark data is estimated based on the position of a similar, but non-identical, patch of information within a data structure. The information in the data structure corresponds to a tiled array of calibration patterns that is sampled along at least three non-parallel paths. In a particular embodiment, the calibration patterns are sampled so that edges are globally-curved, yet locally-flat. Use of such information in the data structure enables enhanced pose estimation, e.g., speeding up operation, enabling pose estimation from smaller patches of watermark signals, and/or enabling pose estimation from weaker watermark signals. A great variety of other features and arrangements are also detailed.

An image processing method determines a geometric transform of a suspect image by efficiently evaluating a large number of geometric transform candidates in environments with limited processing resources. Processing resources are conserved by using complementary methods for determining a geometric transform of an embedded signal. One method excels at higher geometric distortion, and specifically, distortion caused by greater tilt angle of a camera. Another method excels at lower geometric distortion, for weaker signals. Together, the methods provide a more reliable detector of an embedded data signal in image across a larger range of distortion while making efficient use of limited processing resources in mobile devices.

Differential modulation schemes encode a data channel within host signal or noisy environment in a manner that is robust, flexible to achieve perceptual quality constraints, and provides improved data capacity. Differential arrangements enable a decoder to suppress host signal or other background signal interference when detecting, synchronizing and extracting an encoded data channel. They also enable the incorporation of implicit or explicit synchronization components, which are either formed from the data signal or are complementary to it.