METHOD AND SYSTEM FOR RECOGNIZING OBJECTS IN AN IMAGE BASED ON CHARACTERISTICS OF THE OBJECTS

• US 20110044544A1
• Filed: 10/29/2010
• Published: 02/24/2011
• Est. Priority Date: 04/24/2006
• Status: Abandoned Application

• Current Assignee: Pixart Imaging Incorporation ROC
• Original Assignee: Pixart Imaging Incorporation ROC
• Inventors: Lee, Yi-Fang, Chen, Hsin-Chia
• Application Number:
  US12/915,316
• Publication Number:
  US 20110044544A1
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06V 10/421 by analysing segments intersecting the pattern
• CPC Class Codes: G06V 10/422 for representing the structure of the pattern or shape of an object therefor

Abstract

A characteristics-based image recognition method for recognizing objects in an image is implemented using an image sensor and a register. The image sensor has a plurality of pixel sensing elements. The method includes: setting a grayscale threshold value of the image; acquiring pixel values of each row sequentially in the image; identifying a background region and linear image segments of the objects in the image according to the grayscale threshold value; identifying the objects to which the linear image segments belong according to a spatial correlation between a newly detected linear image segment and a previously detected linear image segment; associating collected information of the linear image segments with the identified objects to which the linear image segments belong; and distinguishing the identified objects from each other based on solid, ring-shaped, long and short characteristics.

Claims

1. A method for recognizing objects in an image, said method being implemented using an image sensor and a register, the image sensor including a plurality of pixel sensing elements arranged in rows and capable of sensing the image in a row-by-row manner such that linear image segments of the objects in the image captured by the image sensor are sensed by corresponding rows of the pixel sensing elements, said method comprising the following steps:

(A) projecting light to generate an image, the light carrying a predefined pattern;

(B) sensing the image by a set of exposure parameters;

(C) setting a gray scale threshold value of the image with respective to the exposure parameters;

(D) acquiring pixel values of each row sequentially in the image;

(E) identifying a background region and the linear image segments in the image according to the grayscale threshold value;

(F) identifying the objects to which the linear image segments belong according to a spatial correlation between a newly detected linear image segment in a currently inspected row of the image and a previously detected linear image segment in an adjacent previously inspected row of the image;

(G) associating collected information of the linear image segments with the identified objects to which the linear image segments belong; and

(H) distinguishing the identified objects from each other based on at least one object characteristic.

2. The method as claimed in claim 1, wherein the step (E) including the following sub-steps:

(E1) determining and storing in the register a start point of the newly detected linear image segment located in the currently inspected row of the image;

(E2) collecting information of the newly detected linear image segment point-by-point starting from the start point, and storing the information in the register; and

(E3) determining and storing in the register an end point of the newly detected linear image segment, and wherein the spatial correlation in step (F) is performed in parallel at least with the determination of a start point of a next detected linear image segment.

3. The method as claimed in claim 1, wherein step (H) includes the following sub-steps:

(H1) determining whether the identified object surrounds the background region;

(H2) determining the identified object to be a solid object when the identified object does not surround the background region, and otherwise determining the identified object to include a hollow region when the identified object surrounds the background region;

(H3) calculating a quotient of an area of the hollow region divided by a sum of areas of the hollow region and the identified object; and

(H4) determining the identified object to be a ring-shaped object if the quotient is greater than a threshold value, and otherwise determining the identified object to be a solid object.

4. The method as claimed in claim 1, wherein step (H) includes the following sub-steps:

(H1) determining coordinates of four suitable corner points of the identified object which form a quadrilateral;

(H2) performing vector calculations for long and short sides of the quadrilateral;

(H3) calculating a quotient of square of length of the long side of the quadrilateral divided by an area of the quadrilateral; and

(H4) determining the identified object to be along object when the quotient is greater than a threshold value, and otherwise determining the identified object to be a short object.

5. The method as claimed in claim 1, wherein, in step (F), the object to which the newly detected linear image segment belongs is identified based on the following equations such that the newly detected linear image segment is determined to belong to the object i when the following equations are satisfied:

Seg-L≦

reline-Obj_i-R; and

Seg-R≧

reline-Obj_i-L

where, when the y^th row of the image is currently being inspected, Seg-L represents the X-axis coordinate of a left start point of the newly detected linear image segment found in the y^th row;

Preline-Obj_i-R represents the X-axis coordinate of a right end point of a previously detected linear image segment of the object i that was found in the (y−

1)^th row of the image;

Seg-R represents the X-axis coordinate of a right end point of the newly detected linear image segment found in the y^th row; and

Preline-Obj_i-L represents the X-axis coordinate of a left start point of the previously detected linear image segment of the object i that was found in the (y−

1)^th row.

6. The method as claimed in claim 1, wherein the step (A) includes:

projecting light through a diffractive optical element, or a MEMS mirror, or a combination of a diffractive optical element and a MEMS mirror.

7. The method as claimed in claim 1, wherein the light source includes a plurality of light emitting devices, and in the step (A), the pattern is generated by physical layout arrangement, timing sequence arrangement, or light spectrum arrangement of light emitting devices, or a combination of two or more of the above.

8. The method as claimed in claim 1, further comprising:

(I) determining a distance in a dimension perpendicular to a plane of the image according to the sensed image.

9. The method as claimed in claim 1, further comprising:

(I) adjusting the exposure parameters if a substantial portion of the pixel values is out of range.

10. A system for recognizing objects in an image, comprising:

a light source projecting light to generate an image, the light carrying a predefined pattern;

an image sensor including a plurality of pixel sensing elements arranged in rows and capable of sensing the image in a row-by-row manner such that linear image segments of the objects in the image captured by said image sensor are sensed by corresponding rows of said pixel sensing elements, said image sensor outputting said linear image segments as an analog output;

an analog-to-digital converter connected to said image sensor for converting the analog output to a digital output;

an image processor connected to said analog-to-digital converter and collecting information of the linear image segments from the digital output, said image processor being set with a grayscale threshold value of the image; and

a register connected to said image processor for temporary storage of the information of the objects collected by said image processor;

wherein said image processor identifies a background region and the linear image segments in the image according to the grayscale threshold value, identifies the object to which a newly detected linear image segment located in a currently inspected row of the image belongs according to a spatial correlation between the newly detected linear image segment and a previously detected linear image segment in an adjacent previously inspected row of the image, associates the collected information of the linear image segments with the identified objects, and distinguishes the identified objects from each other based on at least one object characteristic.

11. The system as claimed in claim 10, wherein the object characteristic is one of solid, ring-shaped, long and short characteristics.

12. The system as claimed in claim 10, wherein the light source includes (A) one or more light emitting devices;

and (B) a diffractive optical element, or a MEMS mirror, or a combination of a diffractive optical element and a MEMS mirror, the one or more light emitting devices projecting light through the diffractive optical element, the MEMS mirror, or the combination of the diffractive optical element and the MEMS mirror, to generate the light carrying the predefined pattern.

13. The system as claimed in claim 10, wherein the light source includes a plurality of light emitting devices, and the pattern is generated by physical layout arrangement, timing sequence arrangement, or light spectrum arrangement of light emitting devices, or a combination of two or more of the above.