Abstract
In this paper, we propose a depth image generation method by stereo matching on super-pixel (SP) basis. In the proposed method, block matching is performed only at the center of the SP, and the obtained disparity is applied to all pixels of the SP. Next, in order to improve the disparity, a new SP-based cost filter is introduced. This filter multiplies the matching cost of the surrounding SP by a weight based on reliability and similarity and sums the weighted costs of neighbors. In addition, we propose two new error checking methods. One-way check uses only a unidirectional disparity estimation with a small amount of calculation to detect errors. Cross recovery uses cross checking and error recovery to repair lacks of objects that are problematic with SP-based matching. As a result of the experiment, the execution time of the proposed method using the one-way check was about 1/100 of the full search, and the accuracy was almost equivalent. The accuracy using cross recovery exceeded the full search, and the execution time was about 1/60. Speeding up while maintaining accuracy increases the application range of depth images.
Highlights
The depth image is an image in which the distance to the object in the three-dimensional space is projected as shading or color on the imaging plane set at the observer’s viewpoint
We newly propose SP-based cost filter, one-way check, and cross recovery, which are contributions of this work
Compared with CSC07 and CSO07, CSR07 shows that the lacks of objects such as cones in front are corrected by the cross recovery
Summary
The depth image is an image in which the distance to the object in the three-dimensional space is projected as shading or color on the imaging plane set at the observer’s viewpoint. In the method using a stereo camera, disparity is obtained from two images captured by two cameras on the left and right. Based on the principle of triangulation, we obtain the distance from the camera to the object using the disparity. In the stereo method, matching between right and left pixels is necessary, and the amount of calculation is huge. For this reason, compatibility between accuracy and processing speed is a problem of stereo matching
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