Hand shape recognition apparatus and method

• US 20060284837A1
• Filed: 06/08/2006
• Published: 12/21/2006
• Est. Priority Date: 06/13/2005
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Stenger, Bjorn, Ike, Tsukasa
• Application Number:
  US11/448,802
• Publication Number:
  US 20060284837A1
• Time in Patent Office: Days
• US Class Current: 345/156
• CPC Class Codes: G06V 40/107 Static hand or arm

Abstract

A similarity calculation unit calculates a similarity between a hand candidate area image and a template image. A consistency probability calculation unit and an inconsistency probability calculation unit use probability distributions of similarities of a case where hand shapes of the template image and the hand candidate area image are consistent with each other and a case where they are not consistent, and calculate a consistency probability and an inconsistency probability of hand shapes between each of the template images and the hand candidate area image. A hand shape determination unit determines a hand shape most similar to the hand candidate area image based on the consistency probability and the inconsistency probability calculated for each hand shape, and outputs it as a recognition result.

Claims

1. An apparatus for recognizing a shape of a human hand, comprising:

an image input unit configured to input an image containing the human hand;

a hand candidate area detection unit configured to detect a hand candidate area from the input image;

a template storage unit configured to store a plurality of template images and a plurality of consistency probability distributions, the template images relating to a plurality of hand shapes, the consistency probability distributions corresponding to the respective template images, each of the consistency probability distributions indicating a probability distribution based on a distribution of first similarities between each of the template images and a plurality of example hand area images each containing a hand shape consistent with the hand shape of the respective template image; and

a hand shape recognition unit configured (1) to calculate second similarities between the hand candidate area image and each of the template images relating to the respective hand shapes, (2) to calculate plural consistency probabilities that each of the second similarities is included in the consistency probability distribution corresponding to each of the template images relating to the respective hand shapes, and (3) to obtain a hand shape most similar to the hand candidate area image based on the plural consistency probabilities.

2. The hand shape recognition apparatus according to claim 1, wherein the hand shape recognition unit recognizes a hand shape corresponding to the template image having the highest probability among the plural consistency probabilities to be the hand shape most similar to the hand candidate area image.

3. The hand shape recognition apparatus according to claim 1, wherein the template storage unit

calculates, for each of the template images relating to the respective hand shapes, the first similarities between the template image and the plurality of example hand area images, and

calculates the consistency probability distribution corresponding to each of the template image based on the distribution of the first similarities.

4. The hand shape recognition apparatus according to claim 1, wherein the template storage unit

(1) calculates third similarities between a template image in which an arbitrary hand shape is previously photographed and plural example hand area images in which a hand shape different from the arbitrary hand shape, together with a background, is previously photographed and which are different from each other in only the background and an illumination condition,

(2) calculates a distribution of the third similarities as an inconsistency probability distribution,

(3) obtains the inconsistency probability distribution for each of the template images relating to plural hand shapes, and

(4) stores the respective template images and the consistency probability distributions corresponding to the respective template images, and

the hand shape recognition unit

(1) calculates plural inconsistency probabilities included in the inconsistency probability distributions corresponding to the template images relating to the respective hand shapes, and

(2) obtains the hand shape most similar to the hand candidate area image based on the plural consistency probabilities and the plural inconsistency probabilities.

5. The hand shape recognition apparatus according to claim 4, wherein the hand shape recognition unit subtracts the plural consistency probabilities from the plural inconsistency probabilities for the respective hand shapes, and recognizes a hand shape with the largest difference to be the hand shape most similar to the hand candidate area image.

6. The hand shape recognition apparatus according to claim 1, further comprising a similarity calculation unit configured to obtain the first similarity, the second similarity or the third similarity,

wherein the similarity calculation part

(1) creates n kinds of feature images on one image,

(2) creates n kinds of feature images on another image to be compared with the one image,

(3) compares the feature images of the one image with the feature images of the another image for the respective kinds to calculate n distances of both, and

(4) determines the n distances to be the first similarity, the second similarity or the third similarity.

7. The hand shape recognition apparatus according to claim 1, further comprising an image transformation unit configured to create an image modified by applying an operation of rotation, enlargement/reduction, translation or combination of these to the input image,

wherein the hand shape recognition unit modifies the template image or the hand candidate area image by using the image transformation unit to maximize the consistency probability.

8. The hand shape recognition apparatus according to claim 1, wherein the hand candidate area detection unit obtains a mixture of normal distribution relating to a possibility of the hand candidate area from at least one kind of feature information extracted from the input image and a position of the hand candidate area in a past frame, and determines the hand candidate area image in a present frame based thereon.

9. A method for recognizing a shape of a human hand, comprising:

inputting an image containing the human hand;

detecting a hand candidate area from the input image;

storing a plurality of template images and a plurality of consistency probability distributions, the template images relating to a plurality of hand shapes, the consistency probability distributions corresponding to the respective template images, each of the consistency probability distributions indicating a probability distribution based on a distribution of first similarities between each of the template images and a plurality of example hand area images each containing a hand shape consistent with the hand shape of the respective template image;

(1) calculating second similarities between the hand candidate area image and each of the template images relating to the respective hand shapes;

(2) calculating plural consistency probabilities that each of the second similarities is included in the consistency probability distribution corresponding to each of the template images relating to the respective hand shapes; and

(3) obtaining a hand shape most similar to the hand candidate area image based on the plural consistency probabilities.

10. A program product for recognizing a shape of a human hand, the program product comprising instructions of:

inputting an image containing the human hand;

detecting a hand candidate area from the input image;

storing a plurality of template images and a plurality of consistency probability distributions, the template images relating to a plurality of hand shapes, the consistency probability distributions corresponding to the respective template images, each of the consistency probability distributions indicating a probability distribution based on a distribution of first similarities between each of the template images and a plurality of example hand area images each containing a hand shape consistent with the hand shape of the respective template image;

(1) calculating second similarities between the hand candidate area image and each of the template images relating to the respective hand shapes;

(2) calculating plural consistency probabilities that each of the second similarities is included in the consistency probability distribution corresponding to each of the template images relating to the respective hand shapes; and

(3) obtaining a hand shape most similar to the hand candidate area image based on the plural consistency probabilities.