(untitled) (International Journal of Emerging Trends in Science and Technology, 2017)
Depth map reconstruction also named as disparity estimation and it becomes very difficult task in computer vision three dimensional (3D) tasks which has been studied used for more than three decades. Present high quality depth sensors proficient of creating dense depth maps are costly, noise and bulky, at the same time as dense low-cost sensors be able to simply consistently generate sparse depth measurements. In this work, propose an Enhanced Median Filter (EMF) for noise removal of images. Since the preprocessing stage is an important and initial step in the depth map construction step, it retaining the important information of patches or frames. And the major issue in processing stereophotogrammetry images is the presence of noise which presents as bright dots or dust particles more than the image. So EMF is proposed to remove impulse noise from stereo images. EMF is proposed to remove the noisy pixel from the original pixel; here the noise is removed depending on the threshold value computed from genetic operations. Secondly propose a novel Multilayer Hidden Conditional Random Field (MHCRF) model to restructure a dense depth map of a target scene known the sparse depth measurements and related to photographic measurements computed from stereo photogrammetric systems. This MHCRF model assured global optimum in the modeling of the temporal action dependencies following the Hidden Markov Model (HMM) stage. In MHCRF model, dense depth map is estimated by formulating the as a Maximum A Posteriori (MAP) inference problem wherever efficiency previous is assumed. The any middle representation, depth is computed directly from the Middlebury stereo vision data for ground truth, it has been also deal with any number of cameras. Shows potential experimental results demonstrate the ability of this EMF- MHCRF model and compared to MCRF, CRF models.