Microsoft Technology Licensing, LLC

Other

Technical Arts Corporation

Steven J. White

Robert J. Thoreson

Steven J White

Denis Demandolx

Donald M Marsh

Antonio Criminisi

Tomasz S M Kasperkiewicz

Duncan Paul Robertson

Sayan Dev Pathak

Khan Mohammad Siddiqui

David Earl Heckerman

Christopher A Meek

Jamie Daniel Joseph Shotton

David R Blythe

Jingyang Xu

Richard Shailer Turner, Jr

Igor Cadez

Michael Dean Stokes

Glenn Frederick Evans

Bryan Dove

Eric Paul Bennett

Mahmood Gulam Qadir

Anantha Rao Kancherla

Douglas Alan Ricard

Bradley Phillip Gibson

Ender Konukoglu

Ashok Ramadass

Dilip Raghavan

Benjamin Ellett

Vladimir Farbman

Prabhu KrishnaMoorthy

Tomasz Sm Kasperkiewicz

Robert J Thoreson

Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.

Image registration

An example for processing patient image data workflows can temporarily serially arrange a set of semantic labeling modules in a patient image data workflow pipeline responsive to receiving an event trigger. The example can also remove the set of modules from the patient image data workflow pipeline responsive to receiving an event completion trigger.

Metadata extraction pipeline

Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

Automatic organ localization

The method uses several steps to collect, analyze, compare, and flag an image for inappropriate content. The image is first collected based on detection and is analyzed via a database of inappropriate images. The collected image is scanned for skin-tone and/or texture, then the method eliminates the background of the image. The method then analyzes the curvature of the image. The next step is body part detection for inappropriate body parts. The image is then given a score based on all of the above steps. If the score is above a point, the image is flagged. If it is below a preselected point, the image is released. If the score is between the two points, it is withheld for further analysis.

Method for visual image detection

A system, a method and computer-readable media for processing raw image data with a graphics processing unit (GPU). Raw image data generated by an imaging sensor is received. A set of instructions for demosaicing the raw image data is communicated to the GPU. The GPU is enabled to demosaic the raw image data by executing the set of instructions.

Raw image processing

Methods for mapping color data having at least one color associated therewith to an output device based on an input-device profile and an output-device profile, each profile having a tone curve and a color matrix, are provided. In one embodiment, the method includes receiving color data from an input device and determining whether the color data is in a linear space. If it is determined that the color data is not in a linear space, the method further includes applying the tone curve of the input device profile to the color data to convert it into a linear space. The method further includes converting the color(s) associated with the color data from the input linear space to an output linear space by applying the color matrix of the input device profile and the inverse color matrix of the output device profile to create color-converted image data.

Hardware-accelerated color data processing

Methods and a processing device are provided for restoring pixels damaged by artifacts caused by dust, or other particles, entering a digital image capturing device. A user interface may be provided for a user to indicate an approximate location of an artifact appearing in a digital image. Dust attenuation may be estimated and an inverse transformation, based on the estimated dust attenuation, may be applied to damaged pixels in order to recover an estimate of the underlying digital image. One or many candidate source patch may be selected based on having smallest pixel distances, with respect to a target patch area. The damaged pixels included in the target patch area may be considered when calculating the pixel distance with respect to candidate source patches. RGB values of corresponding pixels of source patches may be used to restore the damaged pixels included in the target patch area.

Automatic dust removal in digital images

A system, a method and computer-readable media for performing texture resampling algorithms on a processing device. A texture resampling algorithm is selected. This algorithm is decomposed into multiple one-dimensional transformations. Instructions for performing each of the one-dimensional transformations are communicated to a processing device, such as a GPU. The processing device may generate an output image by separately executing the instructions associated with each of the one-dimensional transformations.

Texture resampling with a processor

Methods and systems for processing digital image data utilizing vertically-oriented Effect graphs are provided. When processing digital image data utilizing Effect graphs, it is often necessary for certain Effects on the graph to render their outputs multiple times during a single rendering pass. To alleviate the exponential processing that such a scenario can cause, methods and systems for caching at least a portion of the digital image data being processed in image buffers associated with the output of one or more Effects in the Effect graph during processing are provided. Additionally provided are methods and systems for caching digital image data in image buffers associated with the output of one or more Effects across multiple processing passes of an Effect graph.

Caching digital image data

Systems for rendering Effect graphs for non-destructively processing digital image data which integrate Central Processing Unit (CPU) processing and Graphics Processing Unit (GPU) processing are provided. Additionally provided are systems for processing digital image data utilizing Effect graphs. The systems of the present invention integrate CPU processing and GPU processing to facilitate accelerated rendering of Effect graphs and, consequently, accelerated processing of digital images. Methods for processing digital image data utilizing the systems herein described are also provided.

Non-destructive processing of digital image data

A system, a method and computer-readable media for applying curve effects to a digital image. Multiple curve effects are selected to be aggregated. The curve effects are applied to test pixel values. The outputs from this application are used to generate an aggregated look-up table. The aggregated look-up table is used to apply the curve effects to the digital image.

Aggregation of non-linear pixel effects

Methods and systems for processing, e.g., non-destructively processing, digital image data utilizing vertically-oriented Effect graphs are provided. In non-destructive processing where and when data is transformed is fairly important, both in terms of quality and performance. The further down the vertically-oriented Effect graph a transformation occurs, the better. As such, methods for pushing transformations down an Effect graph to the lowest point possible and applying them at that point rather than the location at which they may have been placed are provided. Systems for implementing the methods herein disclosed are also provided.

Accumulating transforms through an effect graph in digital image processing

Visualizing Internet web traffic is disclosed. In one embodiment, a number of windows are displayed, corresponding to a number of clusters into which users have been partitioned based on similar web browsing behavior. The windows are ordered from the cluster having the greatest number of users to the cluster having the least number of users. Each window has one or more rows, where each row corresponds to a user within the cluster. Each row has an ordered number of visible units, such as blocks, where each block corresponds to a web page visited by the user. The blocks can be color coded by the type of web page they represent. In one embodiment, the corresponding cluster models for the clusters are alternatively displayed in the windows.

Cluster-based visualization of user traffic on an internet site

Methods and systems for processing, e.g., non-destructively processing, digital image data utilizing one or more Effect Layers are provided. Effect Layers combine Effects in useful ways that simplify the process of creating Effect graphs. Each Effect Layer contains a logical Effect sub-graph that includes a plurality of logical Effects, e.g., a main Effect, a blend Effect, and a mask Effect. In one embodiment, a method in accordance with an embodiment of the present invention includes receiving input regarding processing of digital image data, determining the impact that input will have on each logical Effect upon processing of digital image data, and creating a physical Effect sub-graph in accordance with the determined impact. The physical Effect sub-graph may closely resemble or appear nothing like the logical Effect sub-graph. Images may subsequently be rendered utilizing the physical Effect sub-graph.

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Seattle, WA, United States of America

Steven J White