Pixel-Serial and Window-Parallel VLSI Processor for Stereo Matching Using a Variable Window Size

VLSI Processor for Reliable Stereo Matching Based on Adaptive Window-Size Selection

Window-based Stereo Matching Algorithm Using a Weighted Average of Costs Aggregated with Window Size Reduction

In the intensity-based stereo matching algorithm, window-based and coarse-to-fine algorithms are known as typical approaches. The window-based algorithm matches intensity values within windows between two stereo images. The conventional window-based algorithms, however, face a trade-off between error rates of the disparity map in disparity continuity and discontinuity regions due to the window ...

Fast Variable Window for Stereo Correspondence using Integral Images

We develop a fast and accurate variable window approach. The two main ideas for achieving accuracy are choosing a useful range of window sizes/shapes for evaluation and developing a new window cost which is particularly suitable for comparing windows of different sizes. The speed of our approach is due to the Integral Image technique, which allows computation of our window cost over any rectang...

Fast Stereo Matching using Adaptive Window based Disparity Refinement

Aiming at the trade-off between accuracy and efficiency in current local stereo matching, a fast stereo matching method based on adaptive window is proposed. The census transform combined with absolute differences (AD) is used for matching cost initialization. Then an iterative cost aggregation based on exponential step adaptive weight (ESAW) is adopted to improve the parallelism and execution ...

Real-Time Stereo Matching using Adaptive Window based Disparity Refinement

In this paper, we propose a real-time stereo matching method based on adaptive window, aiming at the trade-off between accuracy and efficiency in current local stereo matching, Considering that the Census transform has good adaptability to image amplitude distortion, but may introduce matching ambiguities in regions with noise or similar local structures, we combine the Census transform with AD...