IMAGE RECOGNITION DEVICE, IMAGE RECOGNITION METHOD, AND IMAGE RECOGNITION PROGRAM

• US 20130287251A1
• Filed: 01/31/2013
• Published: 10/31/2013
• Est. Priority Date: 02/01/2012
• Status: Abandoned Application

• Current Assignee: Honda Elesys Co., Ltd. (Nidec Corporation)
• Original Assignee: Honda Elesys Co., Ltd. (Nidec Corporation)
• Inventors: MITSUI, Tomokazu, AKIMOTO, Hiroyuki, KANAMOTO, Junji
• Application Number:
  US13/755,847
• Publication Number:
  US 20130287251A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/446 using Haar-like filters, e.g. using integral image techniques
• CPC Class Codes: G06V 10/7747 Organisation of the process, e.g. bagging or boosting

Abstract

An image recognition device includes an image acquiring unit configured to acquire an image, and an object recognition unit configured to calculate gradient directions and gradient values of intensity of the image acquired by the image acquiring unit, to scan the gradient values of each acquired gradient direction with windows, calculate a rectangular feature value, and extract a window in which a target object is recognized to be present using a classifier based on the calculated rectangular feature value through the use of a first recognition unit, and to calculate a predetermined feature value from the window extracted by the first recognition unit and recognize the target object using a classifier based on the predetermined feature value through the use of a second recognition unit.

Claims

1. An image recognition device comprising:

an image acquiring unit configured to acquire an image; and

an object recognition unit configured to calculate gradient directions and gradient values of intensity of the image acquired by the image acquiring unit, to scan the gradient values of each acquired gradient direction with windows, calculate a rectangular feature value, and extract a window in which a target object is recognized to be present using a classifier based on the calculated rectangular feature value through the use of a first recognition unit, and to calculate a predetermined feature value from the window extracted by the first recognition unit and recognize the target object using a classifier based on the predetermined feature value through the use of a second recognition unit.

2. The image recognition device according to claim 1, wherein the process performed by the first recognition unit is set to be shorter in operation processing time per window than the process performed by the second recognition unit.

3. The image recognition device according to claim 1, wherein the object recognition unit uses as the rectangular feature value one or more kinds of:

a single-rectangle feature value;

a Haar-like feature value;

feature values based on a plurality of features (Haar-like application) with different rectangular areas adjacent to each other in the same gradient direction;

feature values based on a plurality of features with equal or different rectangular areas separated in the same gradient direction; and

feature values based on a plurality of features with equal or different rectangular areas separated in the different gradient directions.

4. The image recognition device according to claim 1, wherein the object recognition unit applies any operation of four arithmetic operations to the rectangular feature values when a relationship between a plurality of rectangles is used as a feature value.

5. The image recognition device according to claim 1, wherein the object recognition unit performs normalization based on an illumination difference using as the rectangular feature value any one of:

an average value of a window unit;

a standard deviation value of the window unit;

an average value of a raster scan region; and

a standard deviation value of the raster scan region.

6. The image recognition device according to claim 1, wherein the object recognition unit selects a weak classifier obtained through learning of rectangular features as the classifier of the first recognition unit and uses the selected weak classifier for recognition.

7. The image recognition device according to claim 1, wherein the object recognition unit creates one or both of the classifier of the first recognition unit and the classifier of the second recognition unit through boosting or other ensemble learning.

8. The image recognition device according to claim 1, wherein the object recognition unit uses an AdaBoost classifier or a real AdaBoost classifier as one or both of the classifier of the first recognition unit and the classifier of the second recognition unit.

9. The image recognition device according to claim 1, wherein the object recognition unit uses coefficients for normalizing a range of the rectangular feature values for the classifier of the first recognition unit.

10. The image recognition device according to claim 9, wherein the coefficients for normalizing the range of rectangular feature values are values simultaneously learned and determined when creating the classifier of the first recognition unit through learning.

11. The image recognition device according to claim 1, wherein the object recognition unit uses a feature value different from the rectangular feature value used by the first recognition unit as the predetermined feature value used by the second recognition unit.

12. The image recognition device according to claim 11, wherein the object recognition unit uses an HOG feature value as the predetermined feature value used by the second recognition unit.

13. The image recognition device according to claim 1, wherein the object recognition unit uses the rectangular feature value of the gradient value of each gradient direction as the predetermined feature value used by the second recognition unit, and

wherein the first recognition unit and the second recognition unit employ weak classifiers different from each other.

14. The image recognition device according to claim 1, wherein the object recognition unit sets the number of weak classifiers of the first recognition unit to be smaller than the number of weak classifiers of the second recognition unit using boosting for both of the classifier of the first recognition unit and the classifier of the second recognition unit.

15. The image recognition device according to claim 1, wherein the object recognition unit uses coefficients for normalizing a range of the predetermined feature value for the classifier of the second recognition unit.

16. The image recognition device according to claim 15, wherein the coefficients for normalizing the range of the predetermined feature value are values simultaneously learned and determined when creating the classifier of the second recognition unit through learning.

17. The image recognition device according to claim 1, wherein the object recognition unit further scans the periphery of each window extracted by the first recognition unit by the use of the second recognition unit, calculates a predetermined feature value, and recognizes the target object using a classifier based on the predetermined feature value.

18. The image recognition device according to claim 1, wherein the object recognition unit causes the first recognition unit to calculate a rectangular feature value from the extracted window once or more and to extract a window in which a target object is recognized to be present using the classifier based on the calculated rectangular feature value.

19. The image recognition device according to claim 1, wherein one or both of the classifier of the first recognition unit and the classifier of the second recognition unit of the object recognition unit have a cascade configuration.

20. An image recognition method comprising:

causing an image acquiring unit to acquire an image; and

causing an object recognition unit to calculate gradient directions and gradient values of intensity of the image acquired by the image acquiring unit, to scan the gradient values of each acquired gradient direction with windows, calculate a rectangular feature value, and extract a window in which a target object is recognized to be present using a classifier based on the calculated rectangular feature value through the use of a first recognition unit, and to calculate a predetermined feature value from the window extracted by the first recognition unit and recognize the target object using a classifier based on the predetermined feature value through the use of a second recognition unit.

21. An image recognition program causing a computer to perform:

a sequence of causing an image acquiring unit to acquire an image; and

a sequence of causing an object recognition unit to calculate gradient directions and gradient values of intensity of the image acquired by the image acquiring unit, to scan the gradient values of each acquired gradient direction with windows, calculate a rectangular feature value, and extract a window in which a target object is recognized to be present using a classifier based on the calculated rectangular feature value through the use of a first recognition unit, and to calculate a predetermined feature value from the window extracted by the first recognition unit and recognize the target object using a classifier based on the predetermined feature value through the use of a second recognition unit.