Abstract:
A produce data collector and produce recognition system which illuminates a produce item with substantially uniform light to enhance the accuracy of collected produce data and subsequent identification of the produce item as part of a transaction in a transaction establishment. The produce data collector includes a light source for illuminating the produce item with substantially uniform light during the transaction, a light separating element for splitting light collected from the produce item into a plurality of different light portions having different wavelengths, a detector for converting energy in the plurality of light portions into a plurality of electrical signals, and control circuitry which digitizes the plurality of electrical signals to produce a digital spectrum from the produce item which contains information to identify the produce item for the purpose of determining its unit price.
Abstract:
A produce data collector and produce recognition system which illuminates a produce item with substantially uniform light to enhance the accuracy of collected produce data and subsequent identification of the produce item as part of a transaction in a transaction establishment. The produce data collector includes a light source for illuminating the produce item with substantially uniform light during the transaction, a light separating element for splitting light collected from the produce item into a plurality of different light portions having different wavelengths, a detector for converting energy in the plurality of light portions into a plurality of electrical signals, and control circuitry which digitizes the plurality of electrical signals to produce a digital spectrum from the produce item which contains information to identify the produce item for the purpose of determining its unit price.
Abstract:
Described herein are methods for imaging ocular devices. The methods generally involve (a) applying a scattering agent to the ocular device; and (b) imaging the ocular device using optical coherence tomography (OCT). The methods described herein provide useful structural information about the surface of the device with high sensitivity.
Abstract:
Techniques for using imaging information computed from examining a scanner signal are described. When one or more objects passes within a field of view of a scanner, scan patterns emerging from one or more scanner windows and reflected from the objects back into the scanner windows produce one or more scanner signals. The scanner signals are processed to obtain beam position and beam length information to improve the accuracy of bar code decoding and to compute imaging information for objects within the field of view of the scanner. The imaging information for the objects is compared with bar code information for the objects. The expected number, size and shapes of objects indicated by the bar code information is compared with the actual number, size and shapes of objects in order to determine if valid scans occurred or if missed, double or otherwise erroneous scans occurred.
Abstract:
Techniques for using imaging information computed from examining a scanner signal are described. When one or more objects passes within a field of view of a scanner, scan patterns emerging from one or more scanner windows and reflected from the objects back into the scanner windows produce one or more scanner signals. The scanner signals are processed to obtain beam position and beam length information to improve the accuracy of bar code decoding and to compute imaging information for objects within the field of view of the scanner. The imaging information for the objects is compared with bar code information for the objects. The expected number, size and shapes of objects indicated by the bar code information is compared with the actual number, size and shapes of objects in order to determine if valid scans occurred or if missed, double or otherwise erroneous scans occurred.
Abstract:
An optical scanner having enhanced item side coverage which scans all six sides of an item and an intermediate side. The optical scanner includes a scanner housing, a first optics assembly within the scanner housing including a horizontal aperture, and a second optics assembly including a second housing within the scanner housing including a substantially vertical aperture. The first and second optics assemblies are capable of scanning six sides of an item. At least one of the first and second optics assemblies additionally generates third scan lines for scanning an intermediate side of the item.
Abstract:
A produce recognition system and method which use a distance measure of likeness calculation to identify a produce item. The system includes a produce data collector, a library, and a computer. The produce data collector collects first data from the produce item. The library contains second data associated with classes of produce items. The computer reads the second data from a library, determines a distance measure of likeness value between the first data and each of the second data, determines third data and a corresponding class of produce items from the second data which produces a smallest distance measure of likeness value, and identifies the produce item to be within the corresponding class of produce items.
Abstract:
A produce recognition system and method which use an internal reference to calibrate a produce data collector. The produce data collector collects first data from an external reference, collects second and third data from an internal reference, and collects fourth data from a produce item. A computer determines a first calibration value from the first and second data and a second calibration value from the third data and applies the first and second calibration values to the fourth data to produce fifth data. The computer further obtains sixth data from reference produce data and compares the fifth and sixth data to identify the produce item.
Abstract:
A method of recognizing produce items which uses checkout frequency as an a priori probability. The method includes the steps of collecting produce data from the produce item, determining DML values between the produce data and reference produce data for a plurality of types of produce items, determining conditional probability densities for all of the types of produce items using the DML values, combining the conditional probability densities together to form a combined conditional probability density, determining checkout frequencies for the produce types, determining probabilities for the types of produce items from the combined conditional probability density and the checkout frequencies, determining a number of candidate identifications from the probabilities, and identifying the produce item from the number candidate identifications.
Abstract:
A produce data collector with minimal spectral distortion. The produce data collector includes a light pipe having entrance and exit ends through which a portion of light reflected from a produce item travels, and a spectrometer adjacent the exit end of the light pipe which splits the portion of light into a plurality of wavelengths and which produces signals associated with the wavelengths for identifying the produce item.