Estimating depth from monocular video under varying illumination
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Ability to perceive depth and reconstruct 3D surface of an image is a basic function of many areas of computer vision. Since 2D image is the projection of 3D scene in two dimension, the information about depth is lost. Many methods were introduced to estimate the depth using single, two or multiple images. But most of the previous work carried out in the area of depth estimation is carried out in the field of stereo-vision. These stereo techniques need two images, a whole setup to acquire them and there are many setbacks in correspondence and hardware implementation. Many cues can be used to model the relation between depth and features to learn depth from a single image using multi-scale Markov Random fields. Here we use Gabor filters to extract texture variation cue and improvise the depth estimate using shape features. This same approach is used for estimating depth from videos by incorporating temporal coherence. In order to do this, optical flow is used and we introduce a novel method of computing optical flow using texture features. Since texture features extract dominant properties from an image which are almost invariant to illumination, the texture based optical flow is robust to large uniform illuminations which has lot of application in outdoor navigation and surveillance.
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