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.
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.
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:
Techniques are described for removing noise effects in processing a barcode signal. A photosignal produced by scanning a barcode is used to generate a digital first derivative signal. Positive and negative thresholds are generated based on the digital first derivative signal. Processing windows are defined for each region of the first derivative signal which exceeds a threshold. Each processing window is bounded by a window opening point at which the first derivative signal exceeds a threshold, and by a window closing point, which is the last crossing of the threshold by the first derivative signal before the first derivative signal makes a zero crossing. Each region is processed to locate a geometric center of the region. A logic transition is recognized for each geometric center of a region defined by a processing window.
Abstract:
Techniques are described for removing noise effects in processing a barcode signal. A photosignal produced by scanning a barcode is used to generate a digital first derivative signal. Positive and negative thresholds are generated based on the digital first derivative signal. Processing windows are defined for each region of the first derivative signal which exceeds a threshold. Each processing window is bounded by a window opening point at which the first derivative signal exceeds a threshold, and by a window closing point, which is the last crossing of the threshold by the first derivative signal before the first derivative signal makes a zero crossing. Each region is processed to locate a geometric center of the region. A logic transition is recognized for each geometric center of a region defined by a processing window.
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 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 which employs sound circuitry to convert analog produce data from a produce data collector to digital produce data. The sound circuitry is controlled by a computer and includes an analog-to-digital converter.