The multiple view geometry in computer vision includes 3-D image and 3-D video processing techniques have received increasing interest due to the availability of high-end capturing, processing and rendering devices. The 3-D world is projected onto 2-D acquisition surfaces. During this process, depth information can be lost. Furthermore, the classical 3-D measurement procedures based on photogrammetric causes systematic errors at strong curved surfaces or steps in surfaces (depth discontinuities problem). The challenges arise from the foreshortening in one view of the stereo images, that occurs when the measured object have depth variation with the direction of the baseline. At some stage in the matching process N pixels on scanline may correspond to a different number of M pixels in another image. In the first part, we provide a survey of the long-standing stereo vision problems and the classical-based methods (Area-based, Feature-based) those are exhibited these problems. For that, we have developed two methods based on the local-spatial frequency and phase-difference-based techniques combine with structured light concept to improve the accuracy of the 3-D surface reconstruction. The first algorithm is based on the output of linear spatial filters tuned to a range of orientations and scales that make the correspondence analysis more reliable and robust. The responses of these filters at a given pixel constitute a vector called filter response vector (FRV). This vector is correlated instead of correlating area in the two images. The correspondence problem can be solved by seeking points in the other view where this vector is maximally similar. In addition, an automatic procedure is used to evaluate and optimize the filters degree by using Steering theorem and the Singular Value Decomposition (SVD). The projective distortion regions are detected to improve the quality of the disparity estimation by adapting the scale filter selection. The algorithm maintains a current best estimate of the viewing parameters (to constrain vertical disparity to be consistent with epipolar geometry), a visibility map (to record whether a point is binocularly visible or occluded) and a scale map (to record the largest scale of filter not straddling a depth discontinuity). Starting with an initial computed disparity map, the algorithm iteratively updates the disparity for each detected region. Then the 3-D surface reconstruction can be recovered. The second algorithm is a phase-difference based algorithm that uses an adaptive Gabor-scale space expansion. This algorithm demonstrates a theory of modeling the physical effects of perspective distortion (foreshortening problem) in stereo vision system. The central part of our model is the development of the dual scale factor that allows the reasoning of foreshortening in both the geometric domain of the world model and the frequency domain of the stereo images. The algorithm also provides a novel solution to the phase-wraparound problem that has limited the applicability of other phase-based methods. We combine the magnitude and phase information for estimating depth information from two-dimensional stereo image pairs. This method takes into account not only the instability of phase but also the surface perspective distortion (the foreshortening in one view). Magnitude information is used to detect "weak points" in the frequency domain, and only reliable phase values remain for a robust estimation disparity. The advantage of this algorithm is that the computed disparity values are obtained with sub-pixel accuracy without requiring explicit sub-pixel signal reconstruction. This relates the parameters in the image plane to the surface slope and does not require prior knowledge of the distance to the object. From the experimental results we conclude the fact that the foreshortening factor has its greatest impact when objects are sharply slanted and located near the cameras. The efficiency and performance is confirmed on the basis of analysis of rectified stereo images. The experimental results show that the performance of the proposed algorithm in terms of accuracy and density of the disparity estimates has greatly improved.