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Depth Reconstruction using Geometric Correction with Anaglyph Approach for Satellite Imagery

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TLDR
In this article, the first phase of pre-processing is used to make the satellite images free from such errors and then the image is reconstructed with depth dimension/depth map generation for the anaglyph image for better interpretation of satellite imagery.
Abstract
The Images obtained through remote sensing systems are not often sufficient for high precision applications due to various distortions. The distortions can be due to errors like geometric errors, etc. Also multi-date satellite images of the same area under different conditions are difficult to compare because of change in atmospheric propagation, sensor response and illuminations. Keeping these points in view, in this paper, we deal with the first phase of pre-processing and we make the satellite images free from such errors and use clustering techniques With Geometric Correction (WGC) and Without Geometric Correction (WOGC) applied to the satellite images using our proposed algorithm. Finally, the image is reconstructed with depth dimension/depth map generation for the anaglyph image for better interpretation of satellite imagery. We have made experimental analysis of our algorithm using suitable satellite images and found the results to be very encouraging.

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Citations
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Remote Sensing And Image Interpretation

Ute Beyer
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Journal ArticleDOI

Mining Educational Data for Students' Placement Prediction using Sum of Difference Method

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A Comparative Analysis of Depth Computation of Leukaemia Images using a Refined Bit Plane and Uncertainty Based Clustering Techniques

TL;DR: A combination of the RBP and RIFCM is used to propose an approach and apply it to leukemia images to establish the superiority of the approach in medical diagnosis in comparison to the conventional, as well as uncertainty based approaches.
Dissertation

Absolute depth using low-cost light field cameras

TL;DR: The novel output of this work is development of bespoke calibration algorithms that remove radial/tangential distortion from the data captured using any MLA based camera, and, a light field camera independent algorithm that allows the delivery of 3D coordinate data in absolute units within a well-defined measurable range from a given camera.
Journal Article

Prediction of leukaemia cancer images using parallel saliency algorithm

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References
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Book

Remote sensing and image interpretation

TL;DR: In this article, the authors present a textbook for introductory courses in remote sensing, which includes concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; air photo interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.
Proceedings ArticleDOI

DTAM: Dense tracking and mapping in real-time

TL;DR: It is demonstrated that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application is shown.

Remote Sensing And Image Interpretation

Ute Beyer
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Book ChapterDOI

Real-time dense geometry from a handheld camera

TL;DR: A novel variational approach to estimate dense depth maps from multiple images in real-time by using robust penalizers for both data term and regularizer, which preserves discontinuities in the depth map.
Journal ArticleDOI

Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

TL;DR: The RFPCM comprises a judicious integration of the principles of rough and fuzzy sets that incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy C-means and the coincident clusters of PCM.
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