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Meal image feature-based identification and pricing system

A technology of image features and meals, which is applied in the field of identification and pricing systems, can solve problems such as rising operating costs, unsatisfactory sanitation and efficiency, and achieve the effect of convenient restaurant renovation and low operating costs

Inactive Publication Date: 2018-02-23
杨冠群
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the self-service settlement mode for this type of customers, some restaurants have to rely on the waiter to undertake the meal identification and pricing link. The waiter will tell the customer the total price after visual recognition and mental calculation, and then the customer will scan the code to pay. Therefore, there are still hygiene problems. The efficiency is unsatisfactory; another type of restaurant can automatically identify the meal selected by the customer according to the characteristics of the dish of each meal by the machine at the time of settlement, thus overcoming the disadvantages of the previous manual identification by the waiter, but also It has brought about the shortage that its dishes must be dedicated, which leads to the improvement of restaurant renovation and operating costs, which makes many restaurants discouraged and failed to follow up and adopt it.

Method used

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  • Meal image feature-based identification and pricing system

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0020] Example 1, 3 white plates and 1 small gray bowl are placed in a deep red plastic tray, in which are respectively filled with "fish-flavored pork shreds, braised lion head, scrambled eggs with green peppers, and white rice". After starting the recognition, the test parameters output by the system are:

[0021] Serial number: 1. Name: Fish-flavored pork shreds, similarity of the same type: 91.65%, similarity of other types: 86.9%;

[0022] Serial number: 2. Name: braised lion head, similarity of the same type: 79%, similarity of other types: 53.2%;

[0023] Serial number: 3. Name: green pepper scrambled eggs, similarity of the same category: 90.4%, similarity of other categories: 72.1%;

[0024] Serial number: 4, name: white rice, similarity of the same type: 98.95%, similarity of other types adjacent: 65.55%;

[0025] Recognition time-consuming: 1859ms.

[0026] The recognition results of Example 1 show that although there is a certain intra-class difference between t...

example 2

[0027] Example 2, 4 white plates are placed in a deep red plastic tray, and each plate is filled with 1 meal "vegetable meatball" taken from a different area of ​​the same dish. After starting the recognition, the test parameters output by the system are:

[0028] Serial number: 1. Name: Vegetable Meatballs, similarity of the same type: 92.15%, similarity of other types adjacent: 74.95%;

[0029] Serial number: 2. Name: Vegetable Meatballs, similarity of the same type: 89.9%, similarity of other types: 64.6%;

[0030] Serial number: 3. Name: Vegetable Meatballs, similarity of the same type: 90.4%, similarity of other types: 71.7%;

[0031] Serial number: 4. Name: Vegetable Meatballs, similarity of the same type: 90.7%, similarity of other types: 71.5%;

[0032] Recognition time-consuming: 1906ms.

[0033] The recognition results of Example 2 show that although there is a maximum difference of 2.25% between the 4 meals taken from different regions of the same species and the...

example 3

[0034] Example 3, a white plate with a diameter of 25 cm is placed in a dark red plastic tray, and four kinds of dishes without dishes are placed in the plate successively without overlapping: "vegetable meatballs, shredded pork with fish flavor, shredded pork with green pepper, and rice". After starting the recognition, the test parameters output by the system are:

[0035] Serial number: 1. Name: Vegetable Meatballs, similarity of the same type: 87.35%, similarity of other types: 83.65%;

[0036] Serial number: 2. Name: Fish-flavored shredded pork, similarity of the same type: 78.15%, similarity of other types: 70.6%;

[0037] Serial number: 3. Name: shredded pork with green peppers, similarity of the same type: 90.2%, similarity of other types: 86.3%;

[0038] Serial number: 4, name: white rice, similarity of the same category: 90.7%, similarity of other categories adjacent: 83.05%;

[0039] Recognition time-consuming: 1906ms.

[0040]The recognition results of Example 3...

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Abstract

The invention discloses a meal image feature-based identification and pricing system. Online identification can be realized by performing comparison with inventory samples according to color and shapefeatures of an on-sale meal image snapshot on site by a camera; in combination with a pricing basis given by a background and a possibly needed meal weight, a payable amount is obtained; and a customer can finish settlement by applying a general electronic payment means. Automatic identification and pricing settlement of sold meals unrelated to utensils are realized, so that the convenience and cost performance of a settlement mode of a self-service restaurant are greatly improved.

Description

technical field [0001] The invention relates to an identification and pricing system based on image features of meals, which is used for meal identification and pricing without relying on feature information of bowls and dishes when paying in cafeterias, and can greatly improve the convenience and cost performance of this type of settlement mode. Background technique [0002] In recent years, cafeterias have quickly become the first choice for a large number of office workers to eat because of their hygienic and convenient advantages. In particular, the convenience of allowing customers to scan codes with their mobile phones in the settlement process has won wide acclaim from people. However, in the self-service settlement mode for this type of customers, some restaurants have to rely on the waiter to undertake the meal identification and pricing link. The waiter will tell the customer the total price after visual recognition and mental calculation, and then the customer will...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q20/32G06Q50/12G06K9/46
CPCG06Q20/3274G06Q30/0283G06Q50/12G06V10/44G06V10/56
Inventor 杨冠群
Owner 杨冠群
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