Depth Estimation Method for Monocular Camera Defocus Images in Microscopic Scenes

Camera Pose Estimation in Unknown Environments using a Sequence of Wide-Baseline Monocular Images

In this paper, a feature-based technique for the camera pose estimation in a sequence of wide-baseline images has been proposed. Camera pose estimation is an important issue in many computer vision and robotics applications, such as, augmented reality and visual SLAM. The proposed method can track captured images taken by hand-held camera in room-sized workspaces with maximum scene depth of 3-4...

Bayesian depth estimation from monocular natural images.

Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world t...

3D Pose Estimation using Synthetic Data over Monocular Depth Images

We proposed an approach for human pose estimation over monocular depth images. We augment the data by sampling from existing dataset and generate synthesized images. The generated dataset covers a more continuous pose space than the existing one. We use the generated dataset to train a multi-pathway neural network. We also introduced an orientation and translation invariant embedding for poses ...

New Image Restoration Method Based on Multiple Aperture Defocus Images for Microscopic Images

Image deconvolution is an effective image restoration technique to improve the quality of digital microscopic images resulting from out-of-focus blur. To solve the severely ill-posed problem of traditional Richardson–Lucy method, considering the point spread difference of various directions, a new microscope image restoration method based on multiple defocused images of different aperture is pr...

Depth from Defocus Estimation in Spatial Domain