Abstract: Systems and methods for assisting a user in capturing images for three-dimensional (3D) reconstruction are provided. A system includes an input module configured to receive current video image data from a device. The system also includes an analysis module configured to determine a 3D reconstruction indicator based on the current video image data and a previous image (or images) of a target captured by the device. The analysis module is also configured to determine whether the 3D reconstruction indicator is sufficient to allow a 3D reconstruction of the target based on the current video image data and the previous image. The system also includes a guidance module configured to assist the user in capturing a current image of the target from the current video image data based on the determination of whether the 3D reconstruction indicator is sufficient.

Abstract: In some embodiments, a method of processing a video sequence may include receiving an input video sequence having an input video sequence resolution, aligning images from the input video sequence, reducing noise in the aligned images, and producing an output video sequence from the reduced noise images, wherein the output video sequence has the same resolution as the input video sequence resolution. Other embodiments are disclosed and claimed.

Abstract: Methods and apparatuses receiving a plurality of images and metadata associated with each respective image, determining a viewpoint of one of the images of the plurality, the viewpoint to represent the location and orientation of the image capture device when the image was captured and creating a view including the plurality of images, wherein placement of the images based on each images respective metadata and the determined viewpoint.

Abstract: In one embodiment a thread management method identifies in a main program a set of instructions that can be dynamically activated as speculative precomputation threads. A wait/sleep operation is performed on the speculative precomputation threads between thread creation and activation, and progress of non-speculative threads is gauged through monitoring a set of global variables, allowing the speculative precomputation threads to determine its relative progress with respect to non-speculative threads.

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: Methods and apparatuses receive receiving a plurality of images and metadata associated with each respective image, determining a viewpoint of one of the images of the plurality, the viewpoint to represent the location and orientation of the image capture device when the image was captured and creating a view including the plurality of images, wherein placement of the images based on each images respective metadata and the determined viewpoint.

Abstract: In some embodiments, a method of processing a video sequence may include receiving an input video sequence having an input video sequence resolution, aligning images from the input video sequence, reducing noise in the aligned images, and producing an output video sequence from the reduced noise images, wherein the output video sequence has the same resolution as the input video sequence resolution. Other embodiments are disclosed 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. Other embodiments are also described and claimed.

Abstract: In one embodiment a thread management method identifies in a main program a set of instructions that can be dynamically activated as speculative precomputation threads. A wait/sleep operation is performed on the speculative precomputation threads between thread creation and activation, and progress of non-speculative threads is gauged through monitoring a set of global variables, allowing the speculative precomputation threads to determine its relative progress with respect to non-speculative threads.