Correspondence-Free Determination of the Affine Fundamental Matrix
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale curvature product for robust image corner detection in curvature scale space
Pattern Recognition Letters
3D point-of-regard, position and head orientation from a portable monocular video-based eye tracker
Proceedings of the 2008 symposium on Eye tracking research & applications
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Robust image corner detection based on scale evolution difference of planar curves
Pattern Recognition Letters
Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
A hand gesture controlled interface for intelligent space applications
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Robust Harris-Laplace Detector by Scale Multiplication
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Efficient Hypothesis Generation through Sub-categorization for Multiple Object Detection
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Object Detection and Localization in Clutter Range Images Using Edge Features
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Corner detection based on gradient correlation matrices of planar curves
Pattern Recognition
Automated scene-specific selection of feature detectors for 3D face reconstruction
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
A new contour corner detector based on curvature scale space
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance
Image and Vision Computing
Artistic line-drawings retrieval based on the pictorial content
Journal on Computing and Cultural Heritage (JOCCH)
Supervised learning of graph structure
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
A MLP classifier for both printed and handwritten bangla numeral recognition
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Interactive cartoon reusing by transfer learning
Signal Processing
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
An approach to offline handwritten Devanagari word segmentation
International Journal of Computer Applications in Technology
Underwater live fish recognition using a balance-guaranteed optimized tree
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
An improved image corner matching approach
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
A rule-based event detection system for real-life underwater domain
Machine Vision and Applications
A new geometric descriptor for symbols with affine deformations
Pattern Recognition Letters
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Corners play an important role in object identification methods used in machine vision and image processing systems. Single-scale feature detection finds it hard to detect both fine and coarse features at the same time. On the other hand, multi-scale feature detection is inherently able to solve this problem. This paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on Curvature Scale Space (CSS) technique. The proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced CSS methods. Second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. The proposed method has been evaluated over a number of images and compared with some popular corner detectors. The results showed that the proposed method offers a robust and effective solution to images containing widely different size features.