Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
Efficient edge detection and object segmentation using Gabor filters
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
Kernel Based Multi-object Tracking Using Gabor Functions Embedded in a Region Covariance Matrix
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Hypotheses-driven affine invariant localization of faces in verification systems
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A kernel particle filter multi-object tracking using Gabor-based region covariance matrices
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Analysis of variance of Gabor filter banks parameters for optimal face recognition
Pattern Recognition Letters
Model based selection and classification of local features for recognition using gabor filters
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Gabor parameter selection for local feature detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Automated defect detection in uniform and structured fabrics using Gabor filters and PCA
Journal of Visual Communication and Image Representation
| Hi-index | 0.00 |
Gabor filters are a widely used feature extraction method in image analysis. In this study, a new method is presented that utilises Gabor filters for extracting fundamental frequencies of objects. The fundamental frequencies represent the shape of an object and can be used to classify objects with dissimilar spatial dimensions. Theoretical results are verified by experiments with real images of electronic components. Experiments indicate that the fundamental frequency Gabor filters are a robust tool for rotation and translation invariant object recognition.