000			01945nam a22001577a 4500
082			_a629.8
100			_aRiaz, Sibgha _9120349
245			_aStitch Multiple Images for Generating Quality Panorama / _cSibgha Riaz
264			_aIslamabad : _bSMME- NUST; _c2022.
300			_a53p. _bSoft Copy _c30cm
500			_aStitching multiple images for achieving the 360 view of any environment is a challenging task. Traditionally, the whole process of image stitching is based on distinctive features that are very helpful for estimating the other parameters of the whole algorithm. As different images require different suitable parameters or weights for achieving the best results and we need to predict those suitable parameters for each case independently. In our proposed model first small neural network based techniques are implemented that are just used for estimating the quality panorama hyper parameters and then we apply the whole stitching algorithm on sample images by using those predicted parameters. Therefore, due to lack of labeled data we are unable to train any supervised model for those hyper parameter selection that’s why we build an unsupervised technique that makes decisions based on just extracted features quality, confidence and count of inliers etc. By estimating the good parameters we are able to stitch a quality panorama that doesn't have any ghosting artifacts, blending discontinuities, seamless and alignment errors as well. We evaluate the performance of our proposed model on three datasets and analyze performance in both perspective quality and computational time and conclude that our model outperforms with other state of the art stitching algorithms in both perspectives.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Karam Dad Kallu _9119537
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/32262
942			_2ddc _cTHE
999			_c607692 _d607692