Generating categorical depth maps using passive defocus sensing
First Claim
1. A method for generating a categorical depth map of a scene, the depth map comprising m depth map regions, where m≧
- 1, the method comprising the steps of;
detecting n images of the scene using n cameras focused at different distances, where n>
1;
dividing each of the n images into m predetermined two-dimensional image regions such that each depth map region corresponds to n similar image regions taken from the n images;
determining a sharpness value for each region of the m image regions in each image of the n images;
identifying, for each depth map region, an image number of a sharpest image selected from the n similar image regions taken from the n images, wherein the sharpest image has a maximal sharpness value; and
assigning a categorical depth value to each depth map region of the m depth map regions, where the depth value for a depth map region is determined from an image number of the sharpest image identified for the depth map region.
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Abstract
A method is disclosed for generating a categorical depth map of a scene using passive defocus sensing. In a preferred embodiment three synchronized CCD cameras focused at different distances detect three images of the same scene. An image processor partitions the images into an array of regions and calculates a sharpness value for each region. The sharpness value for a region is calculated by summing over all pixels (x,y) in the region the absolute difference in the intensity value of a pixel (x,y)( with pixel (x-k,y-l), where k and l are constants. The image processor then constructs a depth map of the scene by determining for each region the image with the greatest sharpness in that region. An application of the invention to a mobile robot control system is described in detail. Among other applications, the method may be used for collision avoidance, object detection, and speed measurement.
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Citations
20 Claims
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1. A method for generating a categorical depth map of a scene, the depth map comprising m depth map regions, where m≧
- 1, the method comprising the steps of;
detecting n images of the scene using n cameras focused at different distances, where n>
1;dividing each of the n images into m predetermined two-dimensional image regions such that each depth map region corresponds to n similar image regions taken from the n images; determining a sharpness value for each region of the m image regions in each image of the n images; identifying, for each depth map region, an image number of a sharpest image selected from the n similar image regions taken from the n images, wherein the sharpest image has a maximal sharpness value; and assigning a categorical depth value to each depth map region of the m depth map regions, where the depth value for a depth map region is determined from an image number of the sharpest image identified for the depth map region. - View Dependent Claims (2, 3, 4, 5, 6, 7)
- 1, the method comprising the steps of;
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8. A device for producing a categorical depth map of a scene, the depth map comprising m depth map regions, where m≧
- 2, the device comprising;
n camera means for detecting n images of the scene, where the n camera means are focused at different distances, where n>
1;image processing means for dividing each of the n images into m predetermined two-dimensional image regions such that each depth map region corresponds to n similar image regions taken from the n images, and for determining a sharpness value for each region of the m image regions in each image of the n images; comparing means for assigning a categorical depth value to each depth map region of the m depth map regions, where the depth value for a depth map region is determined from an image number of a sharpest image selected from the n similar image regions taken from the n images. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
- 2, the device comprising;
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20. A mobile robot comprising:
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n camera means for detecting n images, where the camera means are focused at different distances; image processing means for dividing each of the n images into m predetermined two-dimensional image regions such that n similar image regions taken from the n images are in correspondence with each other, and for determining a sharpness value for each image region in each image, where the sharpness value is given by the formula ##EQU2## comparing means for generating a depth map by comparing the sharpness value of each region with the sharpness value of a corresponding similar region in another image; and motion control means for controlling the movement of a robot in response to information contained in the depth map.
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Specification