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Improvised Filter Approach for Estimating Depth from Monocular Images

Depth estimation or extraction refers to the set of techniques and algorithm’s aiming to obtain distance of each and every pixel from the camera view point. Depth Estimation poses various challenges and has wide range applications.Depth can be estimated using different monocular and binocular cues. In [1] depth is estimated from monocular images i.e. by using monocular cues. The filters used for texture gradient estimation in [1] detect false edges and are susceptible to noise. In this paper for depth estimation from monocular images, we have obtained a monocular cue named texture gradient using Canny edge detector which is more robust to noise and false edges as compared to six oriented edge detection filters (Nevatia Babu filters) used in [1]. We, have reduced the dimensions of feature vector as compared to [1] and hence feature optimization for texture gradient extraction is achieved.Even though reduced set of features are used our outputs gave better results as compared to [1].