Depth estimation from single image using machine learning techniques

Abstract

In this paper, the problem of depth estimation from single monocular image is considered. The depth cues such as motion, stereo correspondences are not present in single image which makes the task more challenging. We propose a machine learning based approach for extracting depth information from single image. The deep learning is used for extracting features, then, initial depths are generated using manifold learning in which neighborhood preserving embedding algorithm is used. Then, fixed point supervised learning is applied for sequential labeling to obtain more consistent and accurate depth maps. The features used are initial depths obtained from manifold learning and various image based features including texture, color and edges which provide useful information about depth. A fixed point contraction mapping function is generated using which depth map is predicted for new structured input image. The transfer learning approach is also used for improvement in learning in a new task through the transfer of knowledge from a related task that has already been learned. The predicted depth maps are reliable, accurate and very close to ground truth depths which is validated using objective measures: RMSE, PSNR, SSIM and subjective measure: MOS score.