Abstract:
Techniques for improved image disparity estimation are described. In one embodiment, for example, an apparatus may comprise a processor circuit and an imaging management module, and the imaging management module may be operable by the processor circuit to determine a measured horizontal disparity factor and a measured vertical disparity factor for a rectified image array, determine a composite horizontal disparity factor for the rectified image array based on the measured horizontal disparity factor and an implied horizontal disparity factor, and determine a composite vertical disparity factor for the rectified image array based on the measured vertical disparity factor and an implied vertical disparity factor. Other embodiments are described and claimed.
Abstract:
Systems, apparatuses and methods to provide image data, augmented with related data, to be displayed on a mobile computing device are disclosed. An example mobile device includes a camera to provide images of a scene from different angles to a server, at least one sensor to sense a position and an orientation of the camera, and a screen to present augmented reality data over the scene based on the position and the orientation of the camera and on a three-dimensional representation of the scene based on the images.
Abstract:
Systems, apparatuses and methods to provide image data, augmented with related data, to be displayed on a mobile computing device are disclosed. An example mobile device includes a camera to provide images of a scene from different angles to a server, at least one sensor to sense a position and an orientation of the camera, and a screen to present augmented reality data over the scene based on the position and the orientation of the camera and on a three-dimensional representation of the scene based on the images.
Abstract:
Techniques are provided for calculating temporally coherent disparity values for pixels in a sequence of image frames. An example method may include calculating initial spatial disparity costs between a pixel of a first image frame from a reference camera and pixels from an image frame from a secondary camera. The method may also include estimating a motion vector for the pixel of the first reference camera image frame to a corresponding pixel from a second reference camera image frame. The method may further include calculating a confidence value for the estimated motion vector based on a measure of similarity between the colors of the pixels of the first and second image frames from the reference camera. The method may further include calculating temporally coherent disparity costs based on the initial spatial disparity costs weighted by the confidence value and selecting a disparity value based on those costs.
Abstract:
Systems and methods for determining point-to-point distances from 3D image data. In some embodiments, two measure points, for example specified by a user, represent endpoints on an object of interest within an image frame. Assuming all points lying between these endpoints also belong to the object of interest, additional 3D data associated with points that lie along a measurement line defined by the measure points may be leveraged to provide a robust distance measurement. In some embodiments, total least squares fitting is performed, for example through Robust Principal Component Analysis (RPCA) to identify linear structures within the set of the 3D coordinates on the measurement line. In some exemplary embodiments, the minimum covariance determinant (MCD) estimator of the covariance matrix of the data is computed for a highly robust estimate of multivariate location and multivariate scatter.
Abstract:
Techniques related to disparity search range compression are discussed. Such techniques may include determining a combination of disparity search values that do not coexist in any search range of multiple search ranges each associated with pixels of an initial disparity map, compressing the combination of disparity values to a single disparity label, and performing a disparity estimation based on a disparity search label set including the single disparity label.
Abstract:
Global matching of pixel data across multiple images. Pixel values of an input image are modified to better match a reference image with a slope thresholded histogram matching function. Visual artifacts are reduced by avoiding large pixel value modifications. For large intensity variations across the input and reference image, the slope of the mapping function is thresholded. Modification to the input image is therefore limited and a corresponding modification to the reference image is made to improve the image matching. More than two images may be matched by iteratively modifying a cumulative mass function of a reference image to accommodate thresholded modification of multiple input images. A device may include logic to match pixel values across a plurality of image frames generated from a plurality of image sensors on the device. Once matched, image frames may be reliably processed further for pixel correspondence, or otherwise.
Abstract:
A range is determined for a disparity search for images from an image sensor array. In one example, a method includes receiving a reference image and a second image of a scene from multiple cameras of a camera array, detecting feature points of the reference image, matching points of the detected features to points of the second image, determining a maximum disparity between the reference image and the second image, and determining disparities between the reference image and the second image by comparing points of the reference image to points of the second image wherein the points of the second image are limited to points within the maximum disparity.