Fundamentals of digital image processing
Fundamentals of digital image processing
Digital image processing
Single viewpoint catadioptric cameras
Panoramic vision
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Matching in Catadioptric Images with Appropriate Windows, and Outliers Removal
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Visual Surveillance and Monitoring System Using an Omnidirectional Video Camera
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Catadioptric Camera Calibration Using Geometric Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Calibration Method for Misaligned Catadioptric Camera
IEICE - Transactions on Information and Systems
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
IEEE Transactions on Image Processing
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Omnidirectional cameras are useful in applications requiring rapid capture of image data representing the complete local environment. Feature detection from such image data is thus a prominent research issue. Transforming an omnidirectional image to a panoramic image may result in a sparse panoramic image with missing image data. Whilst image reconstruction techniques have been developed that enable the subsequent use of standard image processing algorithms, the development of image processing algorithms that can be applied directly to sparse image data has received less attention. We address the problem of corner point detection for sparse panoramic images by developing an algorithmic approach that can be applied directly to sparse unwarped omnidirectional images without the requirement of image reconstruction, and we illustrate the accurate performance of the algorithm through visual results and receiver operating characteristic curves.