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Mean-field stereo correspondence for natural images

机译:自然图像的平均场立体对应

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Abstract: This paper presents a new cooperative technique for solving the dense stereo correspondence problem in natural images using mean field theory (MFT). Given a gray scale stereo image pair, the disparity map for the scene is modeled as a locally interconnected network of graded neurons. The network encodes the correspondence problem as an energy function composed of terms representing disparity uniqueness, disparity continuity, and system stability evaluated at each neuron. A MFT approximation to the simulated annealing process commonly used to locate the minimum energy solution for the disparity map is introduced and developed. Results using this approach are compared with those from a standard simulated annealing algorithm and demonstrate a significant improvement in rate of convergence with comparable solution quality.!
机译:摘要:本文提出了一种新的合作技术,利用平均场论(MFT)解决自然图像中的密集立体对应问题。给定灰度立体图像对,将场景的视差图建模为渐变神经元的本地互连网络。网络将对应问题编码为一个能量函数,该能量函数由表示视差唯一性,视差连续性和在每个神经元处评估的系统稳定性的项组成。介绍并开发了通常用于为视差图定位最小能量解的模拟退火过程的MFT近似值。将使用这种方法的结果与标准模拟退火算法的结果进行比较,并证明在可比的解决方案质量下收敛速度有了显着提高。

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