Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Automatic embryonic stem cells detection and counting method in fluorescence microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Medical image segmentation for brain tumor detection
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Multispectral scleral patterns for ocular biometric recognition
Pattern Recognition Letters
LBP-SURF descriptor with color invariant and texture based features for underwater images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Robust Duplicate Detection of 2D and 3D Objects
International Journal of Multimedia Data Engineering & Management
SIFER: Scale-Invariant Feature Detector with Error Resilience
International Journal of Computer Vision
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
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In this work we scrutinize a low level computer vision task - non-maximum suppression (NMS) - which is a crucial preprocessing step in many computer vision applications. Especially in real time scenarios, efficient algorithms for such preprocessing algorithms, which operate on the full image resolution, are important. In the case of NMS, it seems that merely the straightforward implementation or slight improvements are known. We show that these are far from being optimal, and derive several algorithms ranging from easy-to-implement to highly-efficient.