Abstract: Techniques related to generating a virtual view from multi-view images for presentation to a viewer are discussed. Such techniques include determining, based on a viewer position relative to a display region, first and second crop positions of planar image and cropping the planar image to a cropped planar image to fill the display region using the first and second crop positions such that the first and second crop positions define an asymmetric frustum between the cropped planar image and a virtual window corresponding to the display region.

Abstract: Techniques related to generating a virtual view from multi-view images for presentation to a viewer are discussed. Such techniques include determining, based on a viewer position relative to a display region, first and second crop positions of planar image and cropping the planar image to a cropped planar image to fill the display region using the first and second crop positions such that the first and second crop positions define an asymmetric frustum between the cropped planar image and a virtual window corresponding to the display region.

Abstract: Improved techniques for media item sharing are described. In one embodiment, for example, an apparatus may comprise a classification module to assign a media item to a content category, a correlation module to determine context information for the media item, and an estimation module to determine a set of relevance values for a set of contacts based at least in part on a sharing history and to generate a set of suggested recipients for the media item based at least in part on the set of relevance values and the set of contacts. Other embodiments are described and claimed.

Abstract: In certain embodiments of the invention, a plurality of images of one or more subjects may be captured using different imaging techniques, such as different modalities of scanning probe microscopy. Parameters may be estimated from the plurality of images, using one or more models of known molecular structures to provide a model-based analysis. The estimated parameters may be fused, with further input from physical models of known molecular structures. The fused parameters may be used to characterize the subjects. Such characterization may include the detection and/or identification of specific molecular structures, such as proteins, peptides and/or nucleic acids of known sequence and/or structure. In some embodiments of the invention the structural characterizations may be used to identify previously unknown properties of a subject molecule.

Abstract: Embodiments of the invention describe processing a first image data and 3D point cloud data to extract a first planar segment from the 3D point cloud data. This first planar segment is associated with an object included in the first image data. A second image data is received, the second image data including the object captured in the first image data. A second planar segment related to the object is generated, where the second planar segment is geometrically consistent with the object as captured in the second image data. This planar segment is generated based, at least in part, on the second image data, the first image data and the first planar segment. Embodiments of the invention may further augment the second image data with content associated with the object. This augmented image may be displayed such that the content is displayed geometrically consistent with the second planar segment.

Abstract: Systems, devices and methods are described including using object motion appearing in pre-capture images to perform 3D reconstruction of a scene. Objects may be segmented and tracked within the pre-capture images using image processing techniques such as image segmentation and/or object recognition. The image processing results may then be used to automatically tag subsequently captured images. Further, the image processing results may also be used to interactively control an imaging device's focusing mechanism prior to image capture.

Abstract: An image recognition algorithm includes a keypoints-based comparison and a region-based color comparison. A method of identifying a target image using the algorithm includes: receiving an input at a processing device, the input including data related to the target image; performing a retrieving step including retrieving an image from an image database, and, until the image is either accepted or rejected, designating the image as a candidate image; performing an image recognition step including using the processing device to perform an image recognition algorithm on the target and candidate images in order to obtain an image recognition algorithm output; and performing a comparison step including: if the image recognition algorithm output is within a pre-selected range, accepting the candidate image as the target image; and if the image recognition algorithm output is not within the pre-selected range, rejecting the candidate image and repeating the retrieving, image recognition, and comparison steps.

Abstract: Embodiments of the invention describe processing a first image data and 3D point cloud data to extract a first planar segment from the 3D point cloud data. This first planar segment is associated with an object included in the first image data. A second image data is received, the second image data including the object captured in the first image data. A second planar segment related to the object is generated, where the second planar segment is geometrically consistent with the object as captured in the second image data. This planar segment is generated based, at least in part, on the second image data, the first image data and the first planar segment. Embodiments of the invention may further augment the second image data with content associated with the object. This augmented image may be displayed such that the content is displayed geometrically consistent with the second planar segment.

Abstract: An image recognition algorithm includes a keypoints-based comparison and a region-based color comparison. A method of identifying a target image using the algorithm includes: receiving an input at a processing device, the input including data related to the target image; performing a retrieving step including retrieving an image from an image database, and, until the image is either accepted or rejected, designating the image as a candidate image; performing an image recognition step including using the processing device to perform an image recognition algorithm on the target and candidate images in order to obtain an image recognition algorithm output; and performing a comparison step including: if the image recognition algorithm output is within a pre-selected range, accepting the candidate image as the target image; and if the image recognition algorithm output is not within the pre-selected range, rejecting the candidate image and repeating the retrieving, image recognition, and comparison steps.

Abstract: In certain embodiments of the invention, a plurality of images of one or more subjects may be captured using different imaging techniques, such as different modalities of scanning probe microscopy. Parameters may be estimated from the plurality of images, using one or more models of known molecular structures to provide a model-based analysis. The estimated parameters may be fused, with further input from physical models of known molecular structures. The fused parameters may be used to characterize the subjects. Such characterization may include the detection and/or identification of specific molecular structures, such as proteins, peptides and/or nucleic acids of known sequence and/or structure. In some embodiments of the invention the structural characterizations may be used to identify previously unknown properties of a subject molecule.

Abstract: In certain embodiments of the invention, a plurality of images of one or more subjects may be captured using different imaging techniques, such as different modalities of scanning probe microscopy. Parameters may be estimated from the plurality of images, using one or more models of known molecular structures to provide a model-based analysis. The estimated parameters may be fused, with further input from physical models of known molecular structures. The fused parameters may be used to characterize the subjects. Such characterization may include the detection and/or identification of specific molecular structures, such as proteins, peptides and/or nucleic acids of known sequence and/or structure. In some embodiments of the invention the structural characterizations may be used to identify previously unknown properties of a subject molecule.

Abstract: Various embodiments for performing digital video stabilization based on robust dominant motion estimation are described. In one embodiment, an apparatus may receive an input image sequence and estimate dominant motion between neighboring images in the image sequence. The apparatus may use a robust estimator to automatically detect and discount outliers corresponding to independently moving objects. Other embodiments are described and claimed.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.

Abstract: A radiation sensing structure includes red, green and blue photodiodes stacked Above an infrared radiation sensing photodiode.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.

Abstract: A technique includes filtering a sampled representation of an object that might be observed in a scanning beam image with a plurality of filters to produce a plurality of intermediate images. The intermediate images are combined to generate a simulated image that predicts what would be observed in the scanning beam.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. Other embodiments are also described and claimed.

Abstract: According to an embodiment of the invention, a method and apparatus for image segmentation are described. An embodiment of a method comprises inserting a state comprising a set of image segmentations into a queue, the queue being ordered by priority, the set of image segmentations having a priority representing a bound for a quality of the segmentations; extracting the state in the queue having the highest priority; if the extracted state is a terminal state, halting and outputting the extracted state as a solution; if the extracted state is not a terminal state refining the extracted state into a plurality of sets of segmentations, each of the plurality of sets having a priority, inserting the plurality of sets of segmentations into the queue, and iteratively repeating the extraction of the state in the queue having the highest priority.

Abstract: In certain embodiments of the invention, a plurality of images of one or more subjects may be captured using different imaging techniques, such as different modalities of scanning probe microscopy. Parameters may be estimated from the plurality of images, using one or more models of known molecular structures to provide a model-based analysis. The estimated parameters may be fused, with further input from physical models of known molecular structures. The fused parameters may be used to characterize the subjects. Such characterization may include the detection and/or identification of specific molecular structures, such as proteins, peptides and/or nucleic acids of known sequence and/or structure. In some embodiments of the invention the structural characterizations may be used to identify previously unknown properties of a subject molecule.

Abstract: A radiation sensing structure includes red, green and blue photodiodes stacked Above an infrared radiation sensing photodiode.