Fast Radial Symmetry for Detecting Points of Interest

By Gareth Blake Loy, A. Zelinsky. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

672 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/2c21e4808dcb8f9d935d98af07d733b8134525ab

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “The detection step utilizes some detection methods, like Hough transform [210], [211], radial symmetry detector [212], [213], SVM [207], [214] and cascaded classiﬁer [206], [215], to recognize the trafﬁc sign based on the extracted features from previous steps.”
Detecting Symmetry and Symmetric Constellations of Features (2006, methodology): “Symmetry detection has been used for numerous applications, including facial image analysis [23], vehicle detection [12, 38], reconstruction [1, 6, 14, 34], visual attention [17, 24, 27] indexing of image databases [28], completion of occluded shapes [36], object detection [19, 37] and detecting tum”
HERB: a home exploring robotic butler (2010, methodology): “Under these assumptions, the tops of mugs appeared as ellipses in an image which we extracted using a fast symmetry detector ( Loy and Zelinsky 2003 ).”
Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images (2013, background): “The fast radial symmetry transform (FRST) [23] is a”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “Fortunately, Loy and Zelinsky [107] have proposed a FRST to significantly decrease the time cost such that it is well suitable for real-time vision applications.”
Stain Specific Standardization of Whole-Slide Histopathological Images (2016, methodology): “To evaluate the color constancy of the nuclear staining, nuclei were first detected using fast radial symmetry transform [25].”
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze (2010, methodology): “A study on the comparative complexity of symmetry operators was conducted by Loy and Zelinsky [93].”
Fast shape-based road sign detection for a driver assistance system (2004, methodology): “The n-angle representation is not relevant to circles since a circle has edges at all orientations, however, in principle ‖Br‖ can still be computed by summing relevantly orientated (all) vectors voting on each point, giving ‖Br‖ = Or, which makes the circular radial symmetry algorithm [1] an extrem”
Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey (2012, methodology): “HOG is also used by Creusen et al. [65], but they augment the HOG feature vectors with color information to make them even more robust.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “A radial symmetry detector was proposed for sign detection [89], [90].”
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association (2019, methodology): “Their multi-stage method employed Loy and Zelinsky’s Radial Symmetry Transform (Loy & Zelinsky, 2003) to ﬁnd berry candidates, further ﬁltered by a K-nearest neighbors classiﬁer using color and texture features.”
Exploring Principles-of-Art Features For Image Emotion Recognition (2014, methodology): “Since the asymmetrical balance is difﬁcult to measure mathematically, in this paper we only consider symmetry, including bilateral symmetry, rotational symmetry [22] and radial symmetry [23, 28].”