HAND RECOGNIZING METHOD, SYSTEM, AND STORAGE MEDIUM

• US 20170169289A1
• Filed: 08/25/2016
• Published: 06/15/2017
• Est. Priority Date: 12/15/2015
• Status: Abandoned Application

• Current Assignee: Le Holdings (Beijing) Co., Ltd., Le Shi Zhi Xin Electronic Technology (Tianjin) Limited
• Original Assignee: Le Holdings (Beijing) Co., Ltd.
• Inventors: LI, Yanjie
• Application Number:
  US15/247,852
• Publication Number:
  US 20170169289A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20112 Image segmentation details
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 10/758 Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 10/761 Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 40/113 Recognition of static hand signs

Abstract

Disclosure is a hand recognizing method including: acquiring a binary image into which an image is segmented based upon the skin color of a human body; extracting a connectivity domain in the binary image as a connectivity domain to be recognized; calculating a feature vector of a corresponding sample of the connectivity domain to be recognized; calculating the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of hand sample connectivity domain, and the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of non-hand sample connectivity domains; obtaining K samples with the shortest distances, determining whether the number of hand samples among the K samples is more than the number of non-hand samples, and if so, then determining that the connectivity domain to be recognized is a hand feature, wherein K represents a positive odd number.

Claims

1. A hand recognizing method, at a hand recognizing system, comprising:

acquiring a binary image into which an image is segmented based upon the skin color of a human body;

extracting a connectivity domain in the binary image as a connectivity domain to be recognized;

calculating a feature vector of a corresponding sample of the connectivity domain to be recognized;

calculating the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of hand sample connectivity domain, and the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of non-hand sample connectivity domains; and

obtaining K samples with the shortest distances, determining whether the number of hand samples among the K samples is more than the number of non-hand samples, and if so, then determining that the connectivity domain to be recognized is a hand feature, wherein K represents a positive odd number.

2. The hand recognizing method according to claim 1, wherein the feature vector comprises at least one of features including the ratio of the square of the perimeter of the corresponding connectivity domain to the area of the corresponding connectivity domain, the area of the corresponding connectivity domain, the average of probabilities, derived using a Gaussian hybrid model, that pixies of the corresponding connectivity domain belong to the skin of a human body, and the average of probabilities, derived using a color histogram, that pixies of the corresponding connectivity domain belong to the skin of a human body.

3. The hand recognizing method according to claim 1, wherein the distance is a distance corresponding to an Lp norm, a cosine distance, or a power distance.

4. The hand recognizing method according to claim 1, wherein the hand recognizing method further comprises:

generating a list of current results corresponding to the connectivity domain to be recognized; and

if the distance between the feature vector of the connectivity domain to be recognized, and a feature vector of a sample connectivity domain is calculated, then add an entry comprising the calculated distance and a corresponding type of sample to the list of current results in an order of ascending distances; and

obtaining the K samples with the shortest distances comprises;

retrieving the mostly highly ranked K samples from the list of current results after the distances between the feature vector of the connectivity domain to be recognized, and the feature vectors of the respective sample connectivity domains are calculated.

5. The hand recognizing method according to claim 1, wherein the hand recognizing method further comprises:

updating the value of K with a preset value of K input by a user upon reception of the preset value of K input by the user.

6. A hand recognizing system, comprising:

at least one processor; and

a memory communicably connected with the at least one processor for storing instructions executable by the at least one processor, wherein execution of the instructions by the at least one processor causes the at least one processor;

to acquire a binary image into which an image is segmented based upon the skin color of a human body;

to extract a connectivity domain in the binary image as a connectivity domain to be recognized;

to calculate a feature vector of a corresponding sample of the connectivity domain to be recognized;

to calculate the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of hand sample connectivity domain, and the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of non-hand sample connectivity domains; and

to obtain K samples with the shortest distances, to determine whether the number of hand samples among the K samples is more than the number of non-hand samples, and if so, to determine that the connectivity domain to be recognized is a hand feature, wherein K is a positive odd number.

7. The hand recognizing system according to claim 6, wherein the feature vector comprises at least one of features including the ratio of the square of the perimeter of the corresponding connectivity domain to the area of the corresponding connectivity domain, the area of the corresponding connectivity domain, the average of probabilities, derived using a Gaussian hybrid model, that pixies of the corresponding connectivity domain belong to the skin of a human body, and the average of probabilities, derived using a color histogram, that pixies of the corresponding connectivity domain belong to the skin of a human body.

8. The hand recognizing system according to claim 6, wherein execution of the instructions by the at least one processor further causes the at least one processor:

to generate a list of current results corresponding to the connectivity domain to be recognized; and

if the distance between the feature vector of the connectivity domain to be recognized, and a feature vector of a sample connectivity domain is calculated, to add an entry comprising the calculated distance and a corresponding type of sample to the list of current results in an order of ascending distances; and

to retrieve the mostly highly ranked K samples from the list of current results after the distance calculating module calculates the distances between the feature vector of the connectivity domain to be recognized, and the feature vectors of the respective sample connectivity domains.

9. The hand recognizing system according to claim 6, wherein execution of the instructions by the at least one processor further causes the at least one processor:

to receive a preset value of K input by a user; and

to update the value of K with the preset value of K input by the user when the inputting module receives the preset value of K.

10. A non-transitory computer-readable storage medium storing executable instructions that, when executed by an electronic device with a touch-sensitive display, cause the electronic device:

to acquire a binary image into which an image is segmented based upon the skin color of a human body;

to extract a connectivity domain in the binary image as a connectivity domain to be recognized;

to calculate a feature vector of a corresponding sample of the connectivity domain to be recognized;

to calculate the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of hand sample connectivity domain, and the distances between the feature vector of the connectivity domain to be recognized, and feature vectors of non-hand sample connectivity domains; and

to obtain K samples with the shortest distances, to determine whether the number of hand samples among the K samples is more than the number of non-hand samples, and if so, to determine that the connectivity domain to be recognized is a hand feature, wherein K is a positive odd number.

11. The storage medium according to claim 10, wherein the feature vector comprises at least one of features including the ratio of the square of the perimeter of the corresponding connectivity domain to the area of the corresponding connectivity domain, the area of the corresponding connectivity domain, the average of probabilities, derived using a Gaussian hybrid model, that pixies of the corresponding connectivity domain belong to the skin of a human body, and the average of probabilities, derived using a color histogram, that pixies of the corresponding connectivity domain belong to the skin of a human body.

12. The storage medium according to claim 10, wherein execution of the instructions by the electronic device further causes the electronic device:

to generate a list of current results corresponding to the connectivity domain to be recognized; and

if the distance between the feature vector of the connectivity domain to be recognized, and a feature vector of a sample connectivity domain is calculated, to add an entry comprising the calculated distance and a corresponding type of sample to the list of current results in an order of ascending distances; and

to retrieve the mostly highly ranked K samples from the list of current results after the distance calculating module calculates the distances between the feature vector of the connectivity domain to be recognized, and the feature vectors of the respective sample connectivity domains.

13. The storage medium according to claim 10, wherein execution of the instructions by the electronic device further causes the electronic device:

to receive a preset value of K input by a user; and

to update the value of K with the preset value of K input by the user when the inputting module receives the preset value of K.