At the heart of the binocular stereo approach lies the task of stereo matching, i.e. solving for correspondences. Solving the correspondence problem accurately, reliably, and efficiently depends on the type of features used and the computational strategy employed. Similarity is the guiding principle for solution, with the premise that corresponding features will remain similar in the two images. Yet, because of factors such as noise, shadows, occlusions, and perspective effects, the appearance of the corresponding features will differ in the two images. Moreover, derivation of a matching primitive that contains adequate power to resolve ambiguities and is truly invariant with respect to the viewing geometries is a difficult task. This paper introduces a developed competitive stereo correspondence (CSC) framework that solves for these ambiguities. It is heuristic, iterative, and feature-based. Extensive experimentation is successfully carried out on real world scenes, of varying complexity, to evaluate the performance of CSC framework.