Stitch Multiple Images for Generating Quality Panorama / (Record no. 607692)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01945nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Riaz, Sibgha |
| 245 ## - TITLE STATEMENT | |
| Title | Stitch Multiple Images for Generating Quality Panorama / |
| Statement of responsibility, etc. | Sibgha Riaz |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 53p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Stitching multiple images for achieving the 360 view of any environment is a<br/>challenging task. Traditionally, the whole process of image stitching is based on distinctive<br/>features that are very helpful for estimating the other parameters of the whole algorithm. As<br/>different images require different suitable parameters or weights for achieving the best<br/>results and we need to predict those suitable parameters for each case independently. In our<br/>proposed model first small neural network based techniques are implemented that are just<br/>used for estimating the quality panorama hyper parameters and then we apply the whole<br/>stitching algorithm on sample images by using those predicted parameters.<br/>Therefore, due to lack of labeled data we are unable to train any supervised model for<br/>those hyper parameter selection that’s why we build an unsupervised technique that makes<br/>decisions based on just extracted features quality, confidence and count of inliers etc.<br/>By estimating the good parameters we are able to stitch a quality panorama that<br/>doesn't have any ghosting artifacts, blending discontinuities, seamless and alignment errors<br/>as well. We evaluate the performance of our proposed model on three datasets and analyze<br/>performance in both perspective quality and computational time and conclude that our model<br/>outperforms with other state of the art stitching algorithms in both perspectives. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Karam Dad Kallu |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/32262">http://10.250.8.41:8080/xmlui/handle/123456789/32262</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 02/13/2024 | 629.8 | SMME-TH-819 | Thesis |