Abstract: Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.

Abstract: Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

Abstract: Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

Abstract: Interactive product auditing with a mobile device is described. Example methods disclosed herein include performing, with an auditing device, image recognition based on a first set of candidate patterns accessed by the auditing device to identify a first product in a first region of interest of a segmented image. The disclosed example methods also include prompting, with the auditing device, a user to enter input associated with a first grid of the first region of interest displayed on a display, the first grid including the first product. The disclosed example methods further include determining, with the auditing device, a second set of candidate patterns to use to identify a second product in a second region of interest of the segmented image, the second set of candidate patterns determined based on the user input and a group of products identified in a neighborhood of the first region of interest.

Abstract: Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.

Abstract: Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a first photograph. The apparatus further includes a blurriness analyzer to determine a probability of blurriness of the first photograph. The probability of blurriness is based on an analysis of a portion of the first photograph, the portion excluding a region of the first photograph associated with an auto-focus operation. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph to replace the first photograph when the probability of blurriness exceeds a blurriness threshold.

Abstract: Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

Abstract: Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

Abstract: Interactive product auditing with a mobile device is described. Example methods disclosed herein include performing, with an auditing device, image recognition based on a first set of candidate patterns accessed by the auditing device to identify a first product in a first region of interest of a segmented image. The disclosed example methods also include prompting, with the auditing device, a user to enter input associated with a first grid of the first region of interest displayed on a display, the first grid including the first product. The disclosed example methods further include determining, with the auditing device, a second set of candidate patterns to use to identify a second product in a second region of interest of the segmented image, the second set of candidate patterns determined based on the user input and a group of products identified in a neighborhood of the first region of interest.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a first photograph. The apparatus further includes a blurriness analyzer to determine a probability of blurriness of the first photograph. The probability of blurriness is based on an analysis of a portion of the first photograph, the portion excluding a region of the first photograph associated with an auto-focus operation. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph to replace the first photograph when the probability of blurriness exceeds a blurriness threshold.

Abstract: Interactive product auditing with a mobile device is described. Example methods disclosed herein include performing, with an auditing device, image recognition based on a first set of candidate patterns accessed by the auditing device to identify a first product in a first region of interest of a segmented image. The disclosed example methods also include prompting, with the auditing device, a user to enter input associated with a first grid of the first region of interest displayed on a display, the first grid including the first product. The disclosed example methods further include determining, with the auditing device, a second set of candidate patterns to use to identify a second product in a second region of interest of the segmented image, the second set of candidate patterns determined based on the user input and a group of products identified in a neighborhood of the first region of interest.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a photograph with a mobile device. The apparatus further includes a blurriness analyzer to determine a probability of blurriness of the photograph. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph when the probability of blurriness exceeds a blurriness threshold.

Abstract: Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.

Abstract: Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.

Abstract: Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

Abstract: Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a photograph with a mobile device. The apparatus further includes a blurriness analyzer to determine a probability of blurriness of the photograph. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph when the probability of blurriness exceeds a blurriness threshold.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a photograph with a mobile device. the example apparatus further includes a perspective analyzer, implemented by the mobile device, to analyze the photograph to determine a probability of perspective being present in the photograph. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph when the probability of perspective exceeds a threshold.

Abstract: Methods and apparatus to capture photographs using mobile devices are disclosed. An example apparatus includes a photograph capturing controller to capture a photograph with a mobile device. the example apparatus further includes a perspective analyzer, implemented by the mobile device, to analyze the photograph to determine a probability of perspective being present in the photograph. The example apparatus also includes a photograph capturing interface to prompt a user to capture a new photograph when the probability of perspective exceeds a threshold.