@inproceedings {Simula.simula.1644,
	title = {Evaluating Performance of Feature Extraction Methods for Practical 3D Imaging Systems},
	journal = {27th International Conference Image and Vision Computing New Zealand},
	year = {2012},
	month = {December},
	publisher = {ACM - International Conference Proceedings Series (ICPS)},
	type = {Conference},
	abstract = {Smart cameras are extensively used for multi-view capture and 3D rendering applications. To achieve high quality, such applications are required to estimate accurate position and orientation of the cameras (called as camera calibration-pose estimation). Traditional techniques that use checkerboard or special markers, are impractical in larger spaces. Hence, feature-based calibration (auto-calibration), is necessary. Such calibration methods are carried out based on features extracted and matched between stereo pairs or multiple cameras. Well known feature extraction methods such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) have been used for auto-calibration. The accuracy of auto-calibration is sensitive to the accuracy of features extracted and matched between a stereo pair or multiple cameras. In practical imaging systems, we encounter several issues such as blur, lens distortion and thermal noise that affect the accuracy of feature detectors. In our study, we investigate the behaviour of SIFT, SURF and ORB through simulations of practical issues and evaluate their performance targeting 3D reconstruction (based on epipolar geometry of a stereo pair). Our experiments are carried out on two real-world stereo image datasets of various resolutions. Our experimental results show significant performance differences between feature extractors\&$\#$39; performance in terms of accuracy, execution time and robustness to blur, lens distortion and thermal noise of various levels. Eventually, our study identifies suitable operating ranges that helps other researchers and developers of practical imaging solutions.},
	keywords = {Conference},
	author = {Dwarakanath, Deepak and Eichhorn, Alexander and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {drefereedinproceedingsreference.2012-12-10.5290080794,
	title = {Faster and More Accurate Feature-Based Calibration for Widely Spaced Camera Pairs},
	journal = {Digital Information and Communication Technology and it{\textquoteright}s Applications (DICTAP)},
	year = {2012},
	month = {May},
	pages = {87-92},
	publisher = {IEEE},
	type = {Conference},
	abstract = {The increasing demand for live multimedia systems in gaming, art and entertainment industries, has resulted in the development of multiview capturing systems that use camera arrays. We investigate sparse (widely spaced) camera arrays to capture scenes of large volume space. A vital aspect of such systems is camera calibration, which provides an understanding of the scene geometry used for 3D reconstruction. Traditional algorithms make use of a calibration object or identifiable markers placed in the scene, but this is impractical and inconvenient for large spaces. Hence, we take the approach of features-based calibration. Existing schemes based on SIFT (Scale Invariant Feature Transform), exhibit lower accuracy than marker-based schemes due to false positives in feature matching, variations in baseline (spatial displacement between the camera pair) and changes in viewing angle. Therefore, we propose a new method of SIFT feature based calibration, which adopts a new technique for the detection and removal of wrong SIFT matches and the selection of an optimal subset of matches. Experimental tests show that our proposed algorithm achieves higher accuracy and faster execution for larger baselines of up to \approx2 meters, for an object distance of \approx4.6 meters, and thereby enhances the usability and scalability of multi-camera capturing systems for large spaces.},
	keywords = {Conference},
	author = {Dwarakanath, Deepak and Eichhorn, Alexander and Griwodz, Carsten and Halvorsen, P{\r a}l and Dwarakanath, Deepak}
}
