Using Texture Features To Perform Depth Estimation
| dc.contributor.author | Kotha, Bhavi Bharat | en |
| dc.contributor.committeechair | Wicks, Alfred L. | en |
| dc.contributor.committeemember | Asbeck, Alan T. | en |
| dc.contributor.committeemember | Southward, Steve C. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2018-01-23T09:01:03Z | en |
| dc.date.available | 2018-01-23T09:01:03Z | en |
| dc.date.issued | 2018-01-22 | en |
| dc.description.abstract | There is a great need in real world applications for estimating depth through electronic means without human intervention. There are many methods in the field which help in autonomously finding depth measurements. Some of which are using LiDAR, Radar, etc. One of the most researched topic in the field of depth measurements is Computer Vision which uses techniques on 2D images to achieve the desired result. Out of the many 3D vision techniques used, stereovision is a field where a lot of research is being done to solve this kind of problem. Human vision plays an important part behind the inspiration and research performed in this field. Stereovision gives a very high spatial resolution of depth estimates which is used for obstacle avoidance, path planning, object recognition, etc. Stereovision makes use of two images in the image pair. These images are taken with two cameras from different views and those two images are processed to get depth information. Processing stereo images has been one of the most intensively sought-after research topics in computer vision. Many factors affect the performance of this approach like computational efficiency, depth discontinuities, lighting changes, correspondence and correlation, electronic noise, etc. An algorithm is proposed which uses texture features obtained using Laws Energy Masks and multi-block approach to perform correspondence matching between stereo pair of images with high baseline. This is followed by forming disparity maps to get the relative depth of pixels in the image. An analysis is also made between this approach to the current state-of-the-art algorithms. A robust method to score and rank the stereo algorithms is also proposed. This approach provides a simple way for researchers to rank the algorithms according to their application needs. | en |
| dc.description.abstractgeneral | There is a great need in real world applications for estimating depth through electronic means without human intervention. There are many methods in the field which help in autonomously finding depth measurements. Some of which are using LiDAR, Radar, etc. One of the most researched topic in the field of depth measurements is Computer Vision which uses techniques on 2D images to achieve the desired result. Out of the many 3D vision techniques used, stereovision is a field where a lot of research is being done to solve this kind of problem. Human vision plays a important part behind the inspiration and research performed in this field. A large variety of algorithms are being developed to find the measure of depth of ideally each and every point on the pictured scene giving us a very high spatial resolution as compared to other methods. Real world needs of depth estimation and the benefits provided by using stereo vision are the main driving force behind this approach. Stereovision gives a very high spatial resolution which is used for obstacle avoidance, path planning, object recognition, etc. Stereovision makes use of image pairs taken from two cameras with different perspective to estimate depth. The two images in the image pair are taken with two cameras from different views (translational change in view) and those two images are processed to get depth information. The software tool developed is a new approach to perform correspondence matching to find depth using stereo vision concepts. This software tool developed in this work is written in MATLAB. The tools efficiency was evaluated using standard techniques which have been described in detail. The evaluation was also performed by using the software tool with the images collected using a pair of stereo cameras and a tape measure to measure the depth of an object by hand. A scoring method has also been proposed to rank the algorithms in the field of stereo vision. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:14168 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/81895 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Computer Vision | en |
| dc.subject | Stereo Vision | en |
| dc.subject | Depth Estimation | en |
| dc.subject | Scoring | en |
| dc.subject | Ranking | en |
| dc.title | Using Texture Features To Perform Depth Estimation | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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