Abstract:
A method performed by an electronic device is described. The method includes determining overlapping areas from neighboring images. The method also includes determining a difference measure between the overlapping areas. The method further includes determining a constraint measure corresponding to at least one of the overlapping areas. The method additionally includes determining a seam based on a combination of the difference measure and the constraint measure.
Abstract:
A method includes accessing, at a computing device, data descriptive of a graph representing a program. The graph includes multiple nodes representing execution steps of the program and includes multiple edges representing data transfer steps. The method also includes determining at least two heterogeneous hardware resources of the computing device that are available to execute code represented by one or more of the nodes, and determining one or more paths from a source node to a sink node based on a topology of the graph. The method further includes scheduling execution of code at the at least two heterogeneous hardware resources. The code is represented by at least one of the multiple nodes, and the execution of the code is scheduled based on the one or more paths.
Abstract:
A method for interactive image caricaturing by an electronic device is described. The method includes detecting at least one feature location of an image, The method further includes generating, based on the at least one feature location, an image mesh that comprises a grid of at least one horizontal line and at least one vertical line. The method additionally includes obtaining a gesture input. The method also includes determining at least one caricature action based on the at least one gesture input. The method further includes generating a caricature image based on the image mesh, the at least one caricature action and the image.
Abstract:
The present disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for providing virtual keyboards. In one aspect, a system includes a camera, a display, a video feature extraction module and a gesture pattern matching module. The camera captures a sequence of images containing a finger of a user, and the display displays each image combined with a virtual keyboard having a plurality of virtual keys. The video feature extraction module detects motion of the finger in the sequence of images relative to virtual sensors of the virtual keys, and determines sensor actuation data based on the detected motion relative to the virtual sensors. The gesture pattern matching module uses the sensor actuation data to recognize a gesture.
Abstract:
This disclosure describes techniques for modifying application program interface (API) calls in a manner that can cause a device to render native three dimensional (3D) graphics content in stereoscopic 3D. The techniques of this disclosure can be implemented in a manner where API calls themselves are modified, but the API itself and the GPU hardware are not modified. The techniques of the present disclosure include using the same viewing frustum defined by the original content to generate a left-eye image and a right-eye image and shifting the viewport offset of the left-eye image and the right-eye image.
Abstract:
In general, this disclosure describes techniques for providing a gesture-based user interface. For example, according to some aspects of the disclosure, a user interface generally includes a camera and a computing device that identifies and tracks the motion of one or more fingertips of a user. In some examples, the user interface is configured to identify predefined gestures (e.g., patterns of motion) associated with certain motions of the user's fingertips. In another example, the user interface is configured to identify hand postures (e.g., patterns of showing up of fingertips). Accordingly, the user can interact with the computing device by performing the gestures.
Abstract:
Method and apparatus for selecting a code vector in an algebraic codebook wherein the analysis window for the coder is extended beyond the length of the target speech frame. An input signal is filtered by a perceptual weighting filter (76). Then, the filter is set to ring out for a number of samples equal to the length of the perceptual weighting filter (76), while a zero input vector is applied as input. By extending the analysis window, the two dimensional impulse response matrix can be stored as a one dimensional autocorrelation matrix in memory (60, 80), greatly saving on the computational complexity and memory required for the search.
Abstract:
Examples are described of segmenting an image into image regions based on depicted categories of objects, and for refining the image regions semantically. For example, a system can determine that a first image region in an image depicts a first category of object. The system can generate a color distance map of the first image region that maps color distance values to each pixel in the first image region. A color distance value quantifies a difference between a color value of a pixel in the first image region and a color value of a sample pixel in a second image region in the image. The system can process the image based on a refined variant of the first image region that is refined based on the color distance map, for instance by removing pixels from the first image region whose color distances fall below a color distance threshold.
Abstract:
Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements, where each data packet of the plurality of data packets comprises one or more complementary pairs of Golay sequences. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with a random forest model, a physical object in the identification region.
Abstract:
A method performed by an electronic device is described. The method includes receiving an image. The image depicts a face. The method also includes detecting at least one facial landmark of the face in the image. The method further includes receiving a depth image of the face and determining at least one landmark depth by mapping the at least one facial landmark to the depth image. The method also includes determining a plurality of scales of depth image pixels based on the at least one landmark depth and determining a scale smoothness measure for each of the plurality of scales of depth image pixels. The method additionally includes determining facial liveness based on at least two of the scale smoothness measures. Determining the facial liveness may be based on a depth-adaptive smoothness threshold and/or may be based on a natural face size criterion.