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US 6,577,983 B1Granted
Produce recognition method
AssigneeNCR Corporation
InventorJie Zhu
Life of this patent
Estimating the term…
Abstract
A produce recognition method which determines an optimal number of candidate identifications in a candidate identification list. The method includes the steps of obtaining produce data associated with a produce item, determining distances between the produce data and reference produce data, determining confidence values from the distances, determining first confidence values which are greater than a threshold confidence value, displaying candidate identifications associated with the first confidence values, and recording an operator choice of one of the candidate identifications.
Claims7
A produce recognition system for recognizing a non-bar coded produce item comprising:
- a storage medium having a produce database and a look up data file, the produce database stores reference produce data items and item identification numbers;
- a produce data collector for receiving light data reflected from the non-bar coded produce item and converting the light data to a digitized collected produce data;
- wherein the produce data collector includes
- a housing;
- a window mounted within the housing;
- and
- a light source having one or more light emitted diodes for producing a white light spectral distribution, the white light transmitted through the window on to the non-bar coded produce item to create a reflected light;
- spectrometer having a light-separating element and a photodetector array, the light separating element converts the reflected light received through the window to a continuous band of wavelengths, the photodetector array spatially samples the continuous band of wavelengths to produce discrete signal levels;
- and
- a control circuit converts the discrete signal levels to digitized produce data;
- a transaction software component;
- a processor and produce recognition software for comparing the digitized collected produce data with the reference produce data items, retrieving the item identification number for the non-bar coded produce item on a successful match of the digitized collected produce data and the reference produce data item, and obtaining a corresponding price for the non-bar coded item from the look up data file;
- and
- a display for displaying the price retrieved from the look up data file.
11 more elements
A produce recognition system for recognizing a non-bar coded produce item comprising:
- a storage medium having a produce database, the produce database stores reference produce data items;
- a produce data collector for receiving light data reflected from the non-bar coded produce item and converting the light data to a digitized collected produce data;
- wherein the produce data collector includes
- a housing;
- a window mounted within the housing;
- a light source having one or more light emitted diodes for producing a white light spectral distribution, the white light transmitted through the window on to the non-bar coded produce item to create a reflected light;
- a spectrometer having a light separating element and a photodetector array, the light separating element converts the reflected light received through the window to a continuous band of wavelengths, the photodetector array spatially samples the continuous band of wavelengths to produce discrete signal levels;
- and
- a control circuit converts the discrete signal levels to digitized produce data;
- a transaction software component;
- a processor and produce recognition software comparing the digitized collected produce data with reference produce data items to define a candidate list of reference data describing like produce, the candidate list having a confidence interval for each chosen reference produce data item and sorted thereby, each chosen reference produce data item is chosen by a distance measuring algorithm, the list size determined by summing the confidence values to a value greater than or equal to a threshold probability representing success;
- and
- a display for displaying the candidate list and allowing the operator to choose from the list.
10 more elements
A non-bar coded produce recognition method comprising the steps of:
- (a) receiving light spectral data describing the produce item;
- (b) converting the light spectral data to digitized collected produce data;
- (c) comparing the digitized collected produce data with the reference produce data stored in the produce database, on a successful match an item identification number associated with the reference produce data is passed to a transaction software component, the transaction software component obtaining a corresponding unit price from a look up data file;
- (d) obtaining weight information associated with the produce item:
- and
- (e) calculating the overall produce data cost by multiplying the unit price by weight information.
3 more elements
A non-bar coded produce recognition method comprising the steps of:
- (a) receiving light spectral data reflected off of the produce item;
- (b) converting the light spectral data to a digitized collected produce data;
- (c) comparing the digitized collected produce data with reference produce data items stored in a produce database to define a sorted candidate list of reference data items, the candidate list having a confidence interval for each chosen reference produce data item within the list, each chosen reference produce data chosen by a distance measuring algorithm, the list size determined by summing the confidence values to a value greater than or equal to a threshold probability representing success;
- (d) displaying the list;
- (e) recording an operator choice for a selected item from a input device;
- (f) obtaining a unit price for the selected item;
- (g) obtaining weight information associated with the produce from a scale;
- and
- (h) calculating the overall produce data cost by multiplying the unit price by the weight information.
6 more elements
A non-bar coded produce recognition method comprising the steps of:
- (a) receiving light spectral data reflected off of the produce item;
- (b) converting the light spectral data to a digitized collected produce data;
- (c) comparing the digitized collected produce data with reference produce data items stored in a produce database to define a sorted candidate list of reference data items, the candidate list having a confidence interval for each chosen reference produce data item within the list, each chosen reference produce data chosen by a distance measuring algorithm, the list size determined by summing the confidence values to a value greater than or equal to a threshold probability representing success;
- and
- (d) displaying the list and images associated with each entry in the list.
2 more elements
Dependencies are inferred from the claim text.