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Deep learning-based methods for stereo matching have shown superior performance over traditional ones. However, most of them ignore the inherent geometry prior of stereo matching when training, i.e. the reference image can be reconstructed from the second image in the visible regions. The reconstruction can be achieved by backward warping the second image using the disparity map of the reference image,...
Depth information plays an important role in the human visual system, however it is not yet well- explored in existing proposal generation models. In this paper we propose a new geocentric embedding for depth images that encodes depth to the camera, the structural edges and height above ground-plane for each pixel named DEH channels. We demonstrate that this geocentric embedding works can be use to...
The goal of region proposal approaches is to decrease the hunting zone for classifiers. An innovative objectness measure that combines several characteristics of proposals in a Bayesian framework is explicitly presented in this paper. We try to use Bayesian to respectively integrate four different features of proposals, and employ the posterior probability of positive samples as new score to guide...
In the area of computer vision, pattern recognition and image processing, image match is a research hotspot with important theoretical significance and practical value. Recently, the image matching algorithm based on SIFT has drown wide attention for its outstanding local feature matching performance. In view of high computation complexity, poor anti-noise ability, and difficulty for practical use...
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