Microsoft Technology Licensing, LLC

Facebook, Inc.

Matthieu Tony Uyttendaele

Michael F Cohen

Neel Suresh Joshi

Michael A Toelle

Johannes Peter Kopf

Wolf Kienzle

Daniel Lischinski

Zhengyou Zhang

Steven Mark Drucker

Patrice Y Simard

Hugues Herve Hoppe

Peter Vajda

Peizhao Zhang

Yanghan Wang

Merle Michael Robinson

Sisil Sanjeev Mehta

In one embodiment, a system accesses a probability model associated with an image depicting a body. The probability model includes probability values associated with regions of the image and each probability value represents a probability of the associated region of the image containing a particular body part. The system selects a subset (e.g., 3) of the probability values based on a comparison of the probability values. For each selected probability value, the system identifies surrounding probability values surrounding the selected probability value and computes a probabilistic maximum based on the selected probability value and the surrounding probability values. Each probabilistic maximum is associated with a location within the regions associated with the selected probability value and the surrounding probability values. One of the locations associated with the probabilistic maxima is then selected, which represents a determined location in the image that corresponds to the particular body part in the image.

Optimizations for structure mapping and up-sampling

Various technologies described herein pertain to creation of an output hyper-lapse video from an input video. Values indicative of overlaps between pairs of frames in the input video are computed. A value indicative of an overlap between a pair of frames can be computed based on a sparse set of points from each of the frames in the pair. Moreover, a subset of the frames from the input video are selected based on the values of the overlaps between the pairs of the frames in the input video and a target frame speed-up rate. Further, the output hyper-lapse video is generated based on the subset of the frames. The output hyper-lapse video can be generated without a remainder of the frames of the input video other than the subset of the frames.

Real-time hyper-lapse video creation via frame selection

Various technologies described herein pertain to juxtaposing still and dynamic imagery to create a cliplet. A first subset of a spatiotemporal volume of pixels in an input video can be set as a static input segment, and the static input segment can be mapped to a background of the cliplet. Further, a second subset of the spatiotemporal volume of pixels in the input video can be set as a dynamic input segment based on a selection of a spatial region, a start time, and an end time within the input video. Moreover, the dynamic input segment can be refined spatially and/or temporally and mapped to an output segment of the cliplet within at least a portion of output frames of the cliplet based on a predefined temporal mapping function, and the output segment can be composited over the background for the output frames of the cliplet.

Juxtaposing still and dynamic imagery for cliplet creation

A novel image view may be synthesized using a three-dimensional reference model. In an example embodiment, a device-implemented method for synthesizing a novel image view includes acts of registering, selecting, applying and synthesizing. An image is registered to at least one reference model. A source block of visual data from the image is selected with regard to a destination block of the reference model based on a source depth associated with the source block and a destination depth associated with the destination block. The destination position of the destination block of the reference model is not visible in the image. The source block of visual data from the image is applied to the destination block of the reference model to produce an image-augmented model. A novel image view is synthesized using the image-augmented model.

Image view synthesis using a three-dimensional reference model

A system for generating soft copy (digital) versions of hard copy documents uses images of the hard copy documents. The images may be captured using a device suitable for capturing images, like a camera phone. Once available, the images may be processed to improve their suitability for document generation. The images may then be processed to recognize and generate soft copy versions of the documents represented by the images.

Generation of documents from images

An image may be dehazed using a three-dimensional reference model. In an example embodiment, a device-implemented method for dehazing includes acts of registering, estimating, and producing. An image that includes haze is registered to a reference model. A haze curve is estimated for the image based on a relationship between colors in the image and colors and depths of the reference model. A dehazed image is produced by using the estimated haze curve to reduce the haze of the image.

Dehazing an image using a three-dimensional reference model

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Seattle, WA, United States of America

Matthieu Tony Uyttendaele