EMF- MHCRF: Enhanced Median Filter (EMF) Based Noise Removal and Multilayer Hidden Conditional Random Field (MHCRF) Model for Dense Depth Map Reconstruction (International Journal of Emerging Trends in Science and Technology, 2017)
Depth map reconstruction also named as disparity estimation and it becomes very difficult task incomputer 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, atthe same time as dense low-cost sensors be able to simply consistently generate sparse depthmeasurements. 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, itretaining the important information of patches or frames. And the major issue in processingstereophotogrammetry images is the presence of noise which presents as bright dots or dust particles morethan the image. So EMF is proposed to remove impulse noise from stereo images. EMF is proposed toremove the noisy pixel from the original pixel; here the noise is removed depending on the threshold valuecomputed from genetic operations. Secondly propose a novel Multilayer Hidden Conditional RandomField (MHCRF) model to restructure a dense depth map of a target scene known the sparse depthmeasurements and related to photographic measurements computed from stereo photogrammetric systems.This MHCRF model assured global optimum in the modeling of the temporal action dependenciesfollowing the Hidden Markov Model (HMM) stage. In MHCRF model, dense depth map is estimated byformulating the as a Maximum A Posteriori (MAP) inference problem wherever efficiency previous isassumed. The any middle representation, depth is computed directly from the Middlebury stereo visiondata for ground truth, it has been also deal with any number of cameras. Shows potential experimentalresults demonstrate the ability of this EMF- MHCRF model and compared to MCRF, CRF models.