Abstract
Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.
Highlights
Determination of correspondence between two pictures from different viewpoints of the same scene is the primary contribution of stereo matching
The main contributions of our work are in the following: 1) we obtain a new 3D space by radial basis information (RBI); 2) we form a new energy function to calculate the cost for the matching; 3) we convert the proposed objective function to a solvable Hopfield neural network (HNN) energy function
For the formation of objective function, we use discontinuity information obtained from our novel 3D space to calculate matching cost, and constraints of uniqueness and position to reduce the search for a solution
Summary
Determination of correspondence between two pictures from different viewpoints of the same scene is the primary contribution of stereo matching. Various constraints have been used to find the optimal match, such as color consistency, gradient distribution, and geometric shape. These constraints provide less information about discontinuities which is the bottleneck of stereo matching. Compared with current matching methods, the proposed method is in our novel 3D space with less complexity, and has the constraint of discontinuities to find the optimal match. The main contributions of our work are in the following: 1) we obtain a new 3D space by radial basis information (RBI); 2) we form a new energy function to calculate the cost for the matching; 3) we convert the proposed objective function to a solvable Hopfield neural network (HNN) energy function
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