In this paper, a novel local stereo matching algorithm is introduced, providing precise disparity maps with low computational complexity. Following the common steps of local matching methods, namely cost calculation, aggregation, minimization and occlusion handling; the time consuming intensity dependent aggregation procedure is improved in terms of both speed and precision. For this purpose, a novel approach, denoted as permeability filtering (PF), is introduced, engaging computationally efficient two pass integration approach by weighted and connected support regions. The proposed approach exploits a new paradigm, separable successive weighted summation (SWS), among horizontal and vertical directions enabling constant operational complexity for adaptive filtering, as well as providing connected 2D support regions. Once aggregation of the cost values for each disparity candidate is performed independently, minimization is achieved by winner-take-all approach. The same procedure is also utilized to diffuse information through overlapped pixels during occlusion handling, after detecting unreliable disparity assignments. According to the experimental results on Middlebury stereo benchmark, the proposed method outperforms the state-of-the-art local methods in terms of precision and computational efficiency through unifying constant time filtering and weighted aggregation.