METHOD AND APPARATUS FOR THREE DIMENSIONAL PARALLEL OBJECT SEGMENTATION

• US 20120121166A1
• Filed: 11/14/2011
• Published: 05/17/2012
• Est. Priority Date: 11/12/2010
• Status: Abandoned Application

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Ko, Dong-Ik, Cheng, Victor
• Application Number:
  US13/295,783
• Publication Number:
  US 20120121166A1
• Time in Patent Office: Days
• US Class Current: 382/154
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06V 40/162 using pixel segmentation or colour matching

Abstract

A method and apparatus for parallel object segmentation. The method includes retrieving at least a portion of a 3-dimensional point cloud data x, y, z of a frame, dividing the frame into sub-image frames if the sub-frame based object segmentation is enabled,

• • performing fast parallel object segmentation and object segmentation verification; and performing the 3-dimensional segmentation.

Claims

1. A method of a digital processor for parallel object segmentation, comprising:

retrieving at least a portion of a 3-dimensional point cloud data x, y, z of a frame;

dividing the frame into sub-image frames if the object segmentation is enabled;

performing fast parallel object segmentation and object segmentation verification; and

performing the 3-dimensional segmentation.

2. A method of claim 1, wherein the step of performing fast parallel object segmentation comprises:

applying 3-dimensional object parallel segmentation by replacing conditional operation features of segmentation operation for preventing vector processing of object segmentation with Boolean mask maps;

retrieving the 3-dimensional (3D) point cloud data x, y, z coordinates and the scene width (w) and the scene height (h) for obtaining an image and a membership of the image;

computing the distance map from Euclidian distance x, y, z against a centroid, wherein the distance map size is n×

w×

h, where n is the number of centroids;

computing the membership map utilizing the distance map and generating the minimum value from distance for each 3D point cloud i^th (xi, yi, zi), wherein the membership map size is w×

h;

computing a centroid computation Boolean map utilizing the membership map, wherein the centroid computation Boolean map size is n×

w×

h, where the value is set to “

1”

for the corresponding membership and to “

0”

for other columns; and

updating the centroid data and the membership data for the next iteration and analyzing the membership change utilizing the membership data from the previous iteration, wherein when there is a change, continuing segmentation and generating a segment objects when there is no change.

3. The method of claim 1 further comprising:

applying face detection on a foreground object segment;

performing sub-segmentation if more than one face is detected; and

labeling the foreground object segment as a human object segment when a face is detected.

4. An apparatus for parallel object segmentation, comprising:

means for retrieving at least a portion of a 3-dimensional point cloud data x, y, z of a frame;

means for dividing the frame into sub-image frames if the sub-frame based object segmentation is enabled;

means for performing fast parallel object segmentation and object segmentation verification; and

means for performing the 3-dimensional segmentation.

5. The apparatus of claim 4, wherein the means for performing fast parallel object segmentation comprises:

means for applying 3-dimensional object parallel segmentation by replacing conditional operation features of segmentation operation for preventing vector processing of object segmentation with Boolean mask maps;

means for applying retrieving the 3-dimensional (3D) point cloud data x, y, z coordinates and the scene width (w) and the scene height (h) for obtaining an image and a membership of the image;

means for applying computing the distance map from Euclidian distance x, y, z against a centroid, wherein the distance map size is n×

w×

h, where n is the number of centroids;

means for applying computing the membership map utilizing the distance map;

means for applying generating the minimum value from distance for each 3D point cloud i^th (xi, yi, zi), wherein the membership map size is w×

h;

means for applying computing a centroid computation Boolean map utilizing the membership map, wherein the centroid computation Boolean map size is n×

w×

h, where the value is set to “

1”

for the corresponding membership and to “

0”

for other columns; and

means for applying updating the centroid data and the membership data for the next iteration and analyzing the membership change utilizing the membership data from the previous iteration, wherein when there is a change, continuing segmentation and generating a segment objects when there is no change.

6. The apparatus of claim 4 further comprising:

means for applying face detection on a foreground object segment;

means for performing sub-segmentation if more than one face is detected; and

means for labeling the foreground object segment as a human object segment when a face is detected.

7. A non-transitory computer storage medium with executable instructions stored therein, when executed performs a method for 3-dimensional object fast parallel segmentation, the method comprising:

retrieving at least a portion of a 3-dimensional point cloud data x, y, z of a frame;

dividing the frame into sub-image frames if the sub-frame based object segmentation is enabled;

performing fast parallel object segmentation and object segmentation verification; and

performing the 3-dimensional segmentation.

8. The non-transitory computer storage medium of claim 7, wherein the step of performing fast parallel object segmentation comprises:

applying 3-dimensional object parallel segmentation by replacing conditional operation features of segmentation operation for preventing vector processing of object segmentation with Boolean mask maps;

retrieving the 3-dimensional (3D) point cloud data x, y, z coordinates and the scene width (w) and the scene height (h) for obtaining an image and a membership of the image;

computing the distance map from Euclidian distance x, y, z against a centroid, wherein the distance map size is n×

w×

h, where n is the number of centroids;

computing the membership map utilizing the distance map and generating the minimum value from distance for each 3D point cloud i^th (xi, yi, zi), wherein the membership map size is w×

h;

computing a centroid computation Boolean map utilizing the membership map, wherein the centroid computation Boolean map size is n×

w×

h, where the value is set to “

1”

for the corresponding membership and to “

0”

for other columns; and

updating the centroid data and the membership data for the next iteration and analyzing the membership change utilizing the membership data from the previous iteration, wherein when there is a change, continuing segmentation and generating a segment objects when there is no change.

9. The non-transitory computer storage medium of claim 7 further comprising:

applying face detection on a foreground object segment;

performing sub-segmentation if more than one face is detected; and

labeling the foreground object segment as a human object segment when a face is detected.