Apparatus and method for taking dimensions of 3D object

• US 20020118874A1
• Filed: 10/09/2001
• Published: 08/29/2002
• Est. Priority Date: 12/27/2000
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: Kim, Jin-Seog, Chung, Yun-Su, Kim, Hye-Kyu, Lee, Hea-Won, Park, Kil-Houm, Park, Chee-Hang
• Application Number:
  US09/974,494
• Publication Number:
  US 20020118874A1
• Time in Patent Office: Days
• US Class Current: 382/154
• CPC Class Codes: G06T 17/10 Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
• CPC Class Codes: G06T 2207/20132 Image cropping
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume

Abstract

The present invention relates to an apparatus and method for real-time automatically taking the length, width and height of a rectangular object that is moved on a conveyor belt. The method of taking the dimensions of a 3D object, the method comprising the steps of: a) obtaining an object image having the 3D object; b) detecting all edges within a region of interest of the 3D object; c) extracting line segments from the edges of the 3D object and then extracting features of the 3D object from the line segments; and d) generating 3D models based on the features of the 3D object and taking the dimensions of the 3D object from the 3D models.

Claims

1. An apparatus for taking dimensions of a 3D object, comprising:

an image input means for obtaining an object image having the 3D object;

an image processing means for detecting all edges within a region of interest of the 3D object based on the object image obtained in said image input means;

a feature extracting means for extracting line segments of the 3D object and features of the object from the line segments based on the edges detected in said image processing means; and

a dimensioning means for generating 3D models using the features of the 3D object and for taking the dimensions of the 3D object from the 3D models.

2. The apparatus as recited in claim 1, further comprising a dimension storage means for storing the dimensions of the object.

3. The apparatus as recited in claim 1, wherein said image input means includes:

an image capture unit for capturing the object image; and

an object sensing unit for sensing whether the 3D object to be proceeded or not.

4. The apparatus as recited in claim 3, wherein said image input means further includes an image preprocessor for equalizing the object image obtained by said image capture unit to remove noise from the object image.

5. The apparatus as recited in claim 3, wherein said object sensing unit is an image sensor.

6. The apparatus as recited in claim 3, wherein said object sensing unit is a laser sensor.

7. The apparatus as recited in claim 3, wherein said image capture unit is a CCD camera.

8. The apparatus as recited in claim 7, wherein said image capture unit further includes at least an assistant camera.

9. The apparatus as recited in claim 1, wherein said image processing means includes:

a region of interest (ROI) extraction unit for comparing a background image and the object image, and extracting a region of the 3D object; and

an edge detecting unit for detecting all the edges within the region of the 3D object extracted by said ROI extraction unit;

10. The apparatus as recited in claim 1, wherein said feature extracting means includes:

a line segment extraction unit for extracting line segments from all the edges detected by said image processing means; and

a feature extraction unit for finding an outermost intersecting point of the line segments and extracting features of the 3D object.

11. The apparatus as recited in claim 1, wherein said dimensioning means includes:

a 3D model generating unit for generating a 3D model of the 3D object from the features of the 3D object obtained from the object image; and

a dimensions calculating unit for calculating a length, a width and a height of the 3D model and calculating the dimensions of the 3D object.

13. The method as recited in claim 12, further comprising the step of d) storing the dimensions of the 3D object taken in said step c).

14. The method as recited in claim 12, wherein said step a) includes the steps of:

a1) capturing the object image of the 3D object; and

a2) sensing whether an object is included in the object image.

15. The method as recited in claim 14, wherein said step a) further includes the step of a3) equalizing the object image to remove noise from the object image.

16. The method as recited in claim 15, wherein the step a3) is performed by an image sensor.

17. T he method as recited in claim 15, wherein the step a3) is performed by a laser sensor.

18. The method as recited in claim 12, wherein said step b) includes

b1) comparing a background image and the object image and then extracting a region of the 3D object;

and

b2) detecting all the edges within the region of the 3D object.

19. The method as recited in claim 12, wherein said step c) includes:

c1) extracting a straight-line vector from all the edges; and

c2) finding an outermost intersecting point of the line segments and extracting the features.

20. The method as recited in claim 18, wherein said step b2) includes:

b2-1) sampling an input N×

N image of the object image and then calculating an average and variance of the sampled image to obtain a statistical feature of the object image, generating a first threshold;

b2-2) extracting candidate edge pixels of which brightness is rapidly changed, among all the pixels of the input N×

N image;

b2-3) connecting the candidate edge pixels extracted in to neighboring candidate pixels; and

b2-4) storing the candidate edge pixels as final edge pixels if the connected length is greater than a second threshold and storing the candidate edge pixels as non-edge pixels if the connected length is smaller than the threshold.

21. The method as recited in claim 20, wherein said step b2-2) includes the steps of:

b2-2-1) detecting a maximum value and a minimum value among difference values between a current pixel (x) and eight neighboring pixels; and

b2-2-2) classifying the current pixel as a non-edge pixel if the difference value between the maximum value and the minimum value is smaller than the first threshold, and classifying the current pixel as a candidate edge pixel if the difference value between the maximum value and the minimum value is greater than the first threshold.

22. The method as recited in claim 21, wherein said step b2-3) includes the steps of:

b2-3-1) detecting a size and a direction of the edge by applying a sobel operator to said candidate edge pixel; and

b2-3-2) classifying the candidate edge pixel as a non-edge pixel and connecting remaining candidate edge pixels to the neighboring candidate edge pixels, if the size of the candidate edge pixel of which the size and direction are determined is smaller than other candidate edge pixels.

23. The method as recited in claim 19, wherein said step cl) includes the steps of:

c1-1) splitting all the edge pixels detected in said step b); and

c1-2) respectively classifying the divided straight-line vectors depending on the angle to recombine the vector with neighboring straight-line vectors.

24. The method as recited in claim 23, wherein said step b3-1) uses a polygonal approximation method to divide said edge pixels lists into straight-line vectors.

25. The method as recited in claim 12, wherein said step d) includes the steps of:

d1) generating a 3D model of the 3D object from the features of the 3D object; and

d2) calculating a length, a width and a height of the 3D model to calculate the dimensions of the 3D object.

26. The method as recited in claim 25, wherein said step c1) includes the steps of:

d1-1) selecting major features necessary to generate a 3D model among the features of the 3D object; and

d1-2) recognizing world coordinate points using the selected features.

27. The method as recited in claim 26, wherein said step d1-1) includes the step of:

selecting a top feature and a lowest feature among the features of the 3D object by using the inclination between the top feature and its two neighboring features to select four features constituting a path to the lowest feature along the inclination.

28. The method as recited in claim 27, wherein a height of the object is calculated by an equation as:

29. The method as recited in claim 28, wherein an angle is calculated by an equation as:

30. The method as recited in claim 29, wherein a length between two points q′

and r′

is calculated by an equation as;

{overscore (q′

r′

)}={square root}{square root over (A²+(D*d)²−

2A(D−

d) cos θ

)}.

12. A method of taking dimensions of a 3D object, comprising the steps of:

a) obtaining an object image having the 3D object;

b) detecting all edges within a region of interest of the 3D object;

c) extracting line segments from the edges of the 3D object and then extracting features of the 3D object from the line segments; and

d) generating 3D models based on the features of the 3D object and taking the dimensions of the 3D object from the 3D models.

31. A computer-readable recording medium storing instructions for executing a method of taking dimensions of a 3D object, the method comprising the steps of:

a) obtaining an object image having the 3D object;

b) detecting all edges within a region of interest of the 3D object;

c) extracting line segments from the edges of the 3D object and then extracting features of the 3D object from the line segments; and

d) generating 3D models based on the features of the 3D object and taking the dimensions of the 3D object from the 3D models.