The Nielsen Comapny (US), LLC

Brian Schiller

Alejandro Terrazas

Jonathan Lee Sullivan

Michael R Sheppard

Michael Allen Bivins

Wei Xie

Kevin Keqiang Deng

Matthew B Reid

Peter Otto Robert Lipa

Steve Jones

Methods, apparatus, systems and articles of manufacture are disclosed to estimate demographics of users employing social media. An example method disclosed herein includes (1) identifying a social media message regarding an asset, the social media message associated with a user identifier associated with the user, (2) determining demographics associated with a group of people exposed to the asset, (3) associating the user identifier with the asset, and (4) repeating (1) to (3). The example method also includes (5) combining demographics associated with two or more different groups of people with which the user identifier is associated to estimate a demographic profile for the user.

Methods and apparatus to estimate demographics of users employing social media

Methods and apparatus to improve detection and false alarm rate over image segmentation are disclosed. An example method includes determining a first score based on a first pixel distance between a first feature of a first object in a reference image and a second feature in a second image of a search area, determining a second score corresponding to a second pixel distance between the second feature and a mathematical representation of a plurality of shapes representing a similarity between the second feature in the second image of the search area and the plurality of shapes observed simultaneously. The method further includes determining a normalized score by normalizing the first score based on the second score and identifying a second object in the second image of the search area as the first object in the reference image when the normalized score satisfies a threshold score.

Methods and apparatus to improve detection and false alarm rate over image segmentation

Example methods, apparatus, systems to perform context-based image recognition are disclosed. Disclosed example methods include comparing a first and second reference region of a reference image with a first and second region of a first captured image to determine a first and second match value. In response to determining the first and second match values satisfy a first and second threshold: associating the first captured image with a product associated with the first reference image, the product being associated with a plurality of second reference images stored in the database. Decrementing index values associated with ones of the second reference images. Removing a reference record associated with one of the second reference images from the database based on a corresponding one of the second index values satisfying a first condition, the removal of the reference record reducing the storage space required to store reference records in the database.

Context-based image recognition for consumer market research

Methods, apparatus, systems and articles of manufacture are disclosed herein. An example method to improve object detection and false alarm rate over image segmentation includes overlaying a first object of a first image onto a second image. A first score based on a first chamfer distance between first edges of the first object and second edges in the second image is determined. A second score corresponding to a second chamfer distance between the second edges and a mathematical representation of a plurality of shapes is determined, the second score representing a similarity between the second edges and the plurality of shapes observed simultaneously. A normalized score is determined by normalizing the first score based on the second score. A presence of the second object in the second image matching the first object is detected based on whether the normalized score satisfies a threshold score.

Example methods, apparatus, systems to perform context-based image recognition for consumer market research are disclosed. Disclosed example methods include comparing a first subset of feature points in a first region of interest of a reference image with a first subset of feature points of a first image residing within a first region corresponding spatially to the first region of interest and located at a first location in the first image to determine a first match value. Disclosed example methods also include, in response to determining the first match value satisfies a first threshold, comparing a second subset of feature points in a second region of interest of the reference image with a second subset of the feature points of the first image residing within a second region of the first image corresponding spatially to the second region of interest to determine a second match value.

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St. Louis, MO, United States of America

Brian Schiller