Microsoft Technology Licensing, LLC

Other

Timothy R O'Connor

John Michael Thornton

Karthik G Anbalagan

Chunkit Jacky Chan

Scott Robert Ramsby

Alexander Sabo Brodie

Michael H Kim

Eric W Richards

Bryan P Ferguson

Tomasz S M Kasperkiewicz

Gyorgy K Schadt

Roger A Bischoff

Aanand Narayanan Pisharathu

Samuel J Pinson

Jason Matthew Cahill

Cha Zhang

Eyal Krupka

Igor Kviatkovsky

William David Sproule

Robert D Rambo

Igor Abramovski

Eric Wayne Doerr

Ryan A Kabir

Shanjeef Satchithanantham

Mindy C King

Shawn Lee Morrissey

Samuel James Pinson

Aanand Narayanan Pulamanthole Pisharathu

Ralph Pennino

An image to be shared with other users based on input from a first user is received. A second user is identified from a tag of the image, and information is provided, based at least in part on the tag, to one or both of the first user and the second user. Additionally, after editing of an image a determination can be made as to whether a region of the image having an associated tag has been affected by the editing. The tag associated with the region is altered if the region has been affected by the editing, otherwise the tag associated with the region is left unaltered. Furthermore, the tag can include a first portion storing data identifying a region of the image to which the tag corresponds, and a second portion storing data identifying a person shown in the region.

Tagging images with labels

A training set for a post-filter classifier is created from the output of a face detector. The face detector can be a Viola Jones face detector. Face detectors produce false positives and true positives. The regions in the training set are labeled so that false positives are labeled negative and true positives are labeled positive. The labeled training set is used to train a post-filter classifier. The post-filter classifier can be an SVM (Support Vector Machine). The trained face detection classifier is placed at the end of a face detection pipeline comprising a face detector, one or more feature extractors and the trained post-filter classifier. The post-filter reduces the number of false positives in the face detector output while keeping the number of true positives almost unchanged using features different from the Haar features used by the face detector.

Reducing false detection rate using local pattern based post-filter

A global tag for an image is received identifies one or more objects in the image, and a region-specific tag for the image identifies one or more objects in a region of the image. The global tag and the region-specific tag are stored with the image. Displayed, along with the image, is an identifier for each of the one or more objects identified in the global tag, and an identifier for each of the one or more objects identified in the region-specific tag. Different users are able to maintain different names for the same person, allowing the same tag of the image to be used as the basis for displaying the image with different names for the different users. Additionally, the tags can be used as a basis for generating a credits list of people that are included in a compilation of images.

Image tagging user interface

A determination is made for each of multiple regions in multiple images of how good that region is perceived as being. A base image is identified, and a combined image is generated from the multiple images by automatically replacing each region of the base image with a corresponding region of another image if the corresponding region has been determined as being better than the region of the base image. The generating of the combined image can include automatically selecting from one of the multiple images a region in which an object that is present in one or more corresponding regions of other images is absent. Additionally, for a particular region of the base image, corresponding regions of the other images can be displayed, and the particular region replaced with a user-selected one of the corresponding regions of the other images.

Generating a combined image from multiple images

An electro-surgical dissecting and cauterization tool comprises a linear, rigid insulating sleeve surrounding means providing an electric conducting path between a proximal, electric plug end and working tip electrode distal end. The plug attaches the tool to a conventional electro-surgical unit which supplies electrical energy to the working tip electrode end of the tool. A rigid arm extends between the sleeve and the working tip electrode and includes portions laterally offset from the main axis of the sleeve to increase visualization of the working tip electrode during surgery.

Electro-surgical dissecting and cauterizing instrument

Growing community of inventors

Kirkland, WA, United States of America

Timothy R O'Connor