Abstract: Image recognition methods, apparatus and articles or manufacture to support shelf auditing for consumer research are disclosed herein. Example methods disclosed herein include comparing a first image signature associated with an input image with a plurality of reference signatures associated with a plurality of reference images to identify a first reference image matching the input image. Such disclosed example methods also include identifying a first group of items depicted in the input image as corresponding to a first group of reference items registered with the first reference image. Such disclosed example methods further include determining a first region of the input image that differs from a corresponding first region of the first reference image, and processing the first region of the input image based on a template to identify a second item depicted in the input image.

Abstract: Methods and apparatus for identifying primary media content in a post-production media content presentation are disclosed. An example computer-implemented method to detect primary media content included in a secondary media content presentation disclosed herein comprises determining a first image corresponding to the secondary media content presentation, the first image comprising a plurality of image subregions, each image subregion representative of an inter-frame variation associated with a corresponding subregion of the secondary media content presentation, selecting a region of the first image comprising a plurality of connected image subregions of the first image together exhibiting a first type of inter-frame variation, and when a shape of the selected region of the first image corresponds to a predefined shape, processing a region of the first captured image corresponding to the selected region of the first synthetic image to identify the primary media content.

Abstract: Image recognition methods, apparatus and articles or manufacture to support shelf auditing for consumer research are disclosed herein. An example method disclosed herein comprises comparing an input image depicting a shelf to be audited with a set of reference images depicting reference shelves displaying respective sets of reference items, identifying a first reference image from the set of reference images that has been determined to match the input image, and determining an initial audit result for the shelf depicted in the input image based on a first set of reference items associated with the first reference image.

Abstract: Scene-based people metering for audience measurement is disclosed. Example methods disclosed herein include grouping successive image frames depicting a location of a media presentation to form a sequence of scenes, respective scenes including respective groups of the image frames. Such example methods also include grouping matching scenes into respective scene clusters having respective sizes, the scene clusters being represented by respective key frames. Such example methods further include assigning respective ranks to the key frames of the respective scene clusters, the respective ranks being determined based on the respective sizes of the scene clusters. Such example methods additionally include processing the key frames in accordance with the respective ranks to monitor an audience of the media presentation.

Abstract: Disclosed example methods to determine a time offset between a first media signature having a first sequence of first data pairs and a second media signature having a second sequence of second data pairs include comparing ones of the first data pairs in the first sequence with ones of the second data pairs in the second sequence to determine original time differences between respective combinations of the ones of the first data pairs and the ones of the second data pairs. Such example methods also include scaling and truncating the original time differences to determine scaled time differences between respective combinations of the ones of the first data pairs and the ones of the second data pairs. Such example methods further include determining a histogram comprising bins representative of the scaled time differences, and selecting a histogram bin to correspond to the time offset between the first and second signatures.

Abstract: Scene-based people metering for audience measurement is disclosed. An example method disclosed herein to perform people metering for audience measurement comprises segmenting image frames depicting a location in which an audience is expected to be present to form a sequence of scenes, for a first scene in the sequence of scenes, identifying a key frame representing a first sequence of image frames corresponding to the first scene, and processing the key frame to identify an audience depicted in the first scene.

Abstract: Disclosed example methods to determine a time offset between a first media content signature comprising a first sequence of first data pairs and a second media content signature comprising a second sequence of second data pairs include determining a histogram comprising a plurality of bins representative of a plurality of determined time differences. The plurality of determined time differences include each respective time difference determined between each first data pair and second data pair combination. Each respective bin of the histogram represents a respective window of time differences and has a respective value representative of a number of the plurality of determined time differences lying within the respective window of time differences represented by the respective bin. Such disclosed example methods also include selecting at least one bin of the histogram to correspond to the time offset between the first signature and the second signature.

Abstract: Hybrid active and passive people metering for audience measurement is disclosed. An example method disclosed herein to perform people metering for audience measurement comprises obtaining an image sequence depicting a scene in which an audience is expected to be present, comparing an image sequence signature representative of the image sequence with a set of reference signatures to determine a comparison result, and controlling audience prompting performed by a people meter based on the comparison result.

Abstract: Scene-based people metering for audience measurement is disclosed. An example method disclosed herein to perform people metering for audience measurement comprises segmenting image frames depicting a location in which an audience is expected to be present to form a sequence of scenes, for a first scene in the sequence of scenes, identifying a key frame representing a first sequence of image frames corresponding to the first scene, and processing the key frame to identify an audience depicted in the first scene.

Abstract: Methods and apparatus to detect differences between images are disclosed. Example methods disclosed herein to recognize different image versions include generating a first signature representative of a sample image, and obtaining a reference image associated with a second signature determined to substantially match the first image signature. Such example methods also include generating a third signature representative of a first region of the sample image corresponding spatially to a first region of the reference image associated with a first version of the reference image, the third signature being different from the first signature. Such example methods further include comparing the generated third signature and a fourth signature representative of the first region of the reference image to determine whether the sample image corresponds to the first version of the reference image.

Abstract: Methods and apparatus are disclosed to identify audience members. An example method includes detecting selection an identification key on a remote control. The identification key is input by a user using an input on the remote control. The remote control is to control a consumer electronic device. The example method includes generating an audio signal to identify the user based on the identification key. The audio signal is electronically detectable. The example method includes outputting the audio signal via a speaker.

Abstract: Methods and apparatus to methods and apparatus to identify images in print advertisements are disclosed. An example method comprises computing a first image feature vector for a first presented image, comparing the first image feature vector to a second image feature vector, and when the first image feature vector matches the second image feature vector, storing printed-media information associated with the first presented image in a database record associated with the second image feature vector.

Abstract: Methods and apparatus to detect differences between images are disclosed. An example method to identify image differences disclosed herein comprises electronically determining whether a difference between a first block of pixels of a sample image and a second block of pixels of a reference image is resolvable using a transformation operation, when the difference between the first and second blocks is determined to be unresolvable, but not when the difference between the first and second blocks is determined to be resolvable, including the first block in an unresolved difference region of the sample image, and electronically generating a difference signature representative of the unresolved difference region, the difference signature to identify the unresolved difference region of the sample image.

Abstract: Image recognition methods, apparatus and articles or manufacture to support shelf auditing for consumer research are disclosed herein. An example method disclosed herein comprises comparing an input image depicting a shelf to be audited with a set of reference images depicting reference shelves displaying respective sets of reference items, identifying a first reference image from the set of reference images that has been determined to match the input image, and determining an initial audit result for the shelf depicted in the input image based on a first set of reference items associated with the first reference image.

Abstract: Methods and apparatus to specify regions of interest in video frames are disclosed. Example disclosed methods to mark a region in a graphical presentation include selecting a first point located at a substantially central position within the region, selecting a plurality of second points to define a boundary of the region, and comparing a plurality of stored templates with the selected first and second points to identify a first one of the stored templates to represent the region.

Abstract: Methods and apparatus for identifying primary media content in a post-production media content presentation are disclosed. An example computer-implemented method to detect primary media content included in a secondary media content presentation disclosed herein comprises determining a first image corresponding to the secondary media content presentation, the first image comprising a plurality of image subregions, each image subregion representative of an inter-frame variation associated with a corresponding subregion of the secondary media content presentation, selecting a region of the first image comprising a plurality of connected image subregions of the first image together exhibiting a first type of inter-frame variation, and when a shape of the selected region of the first image corresponds to a predefined shape, processing a region of the first captured image corresponding to the selected region of the first synthetic image to identify the primary media content.

Abstract: Methods and apparatus to specify regions of interest in video frames are disclosed. An example disclosed method comprises determining an initial template region to represent a region of interest whose location is based on a first point selected in a graphical presentation, determining a first modification to perform on the initial template region in response to a second point selected in the graphical presentation, detecting the second selected point in the graphical presentation, and reshaping the initial template region toward the second selected point, the reshaping corresponding to the first modification, the reshaping being performed in response to detecting the second selected point without also requiring the user to select any point substantially on the boundary defining the initial template region to initiate the reshaping.

Abstract: Disclosed example methods to determine a time offset between a first media content signature comprising a first sequence of first data pairs and a second media content signature comprising a second sequence of second data pairs include determining a histogram comprising a plurality of bins representative of a plurality of determined time differences. The plurality of determined time differences include each respective time difference determined between each first data pair and second data pair combination. Each respective bin of the histogram represents a respective window of time differences and has a respective value representative of a number of the plurality of determined time differences lying within the respective window of time differences represented by the respective bin. Such disclosed example methods also include selecting at least one bin of the histogram to correspond to the time offset between the first signature and the second signature.

Abstract: Methods and apparatus for identifying primary media content in a post-production media content presentation are disclosed. An example computer-implemented method to detect primary media content included in a secondary media content presentation disclosed herein comprises determining a first image corresponding to the secondary media content presentation, the first image comprising a plurality of image subregions, each image subregion representative of an inter-frame variation associated with a corresponding subregion of the secondary media content presentation, selecting a region of the first image comprising a plurality of connected image subregions of the first image together exhibiting a first type of inter-frame variation, and when a shape of the selected region of the first image corresponds to a predefined shape, processing a region of the first captured image corresponding to the selected region of the first synthetic image to identify the primary media content.

Abstract: A signature is extracted from the audio of a program received by a tunable receiver such that the signature characterizes the program. In order to extract the signature, blocks of the audio are converted to corresponding spectral moments. At least one of the spectral moments is then converted to the signature. Also, a test audio signal from a receiver is correlated to a reference audio signal by converting the test audio signal and the reference audio signal to corresponding test and reference spectra, determining test slopes corresponding to coefficients of the test spectrum and reference slopes corresponding to coefficients of the reference spectrum, and comparing the test slopes to the reference slopes in order to determine a match between the test audio signal and the reference audio signal.