Invariance properties of Gabor filter-based features-overview and applications
By Joni-Kristian Kamarainen, Ville Kyrki, Heikki Kälviäinen. IEEE Transactions on Image Processing, 2006
365 citations recorded by the source corpus.
For almost three decades the use of features based on Gabor filters has been promoted for their useful properties in image processing. The most important properties are related to invariance to illumination, rotation, scale, and translation. These properties are based on the fact that they are all parameters of Gabor filters themselves. This is especially useful in feature extraction, where Gabor filters have succeeded in many applications, from texture analysis to iris and face recognition. This study provides an overview of Gabor filters in image processing, a short literature survey of the most significant results, and establishes invariance properties and restrictions to the use of Gabor filters in feature extraction. Results are demonstrated by application examples.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/157d2c6dd8c9999b251099ef4211cff8030ae486
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In Computer Vision and Pattern Recognition
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Citations in the corpus
- Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition (2016, background): “Furthermore, they are robust against photometric disturbances, such as illumination change and image noise [45], [46].”
- CloudID: Trustworthy cloud-based and cross-enterprise biometric identification (2015, positive): “Furthermore, they are robust against photometric disturbances, such as illumination changes and image noise (Kamarainen, Kyrki, & Kalviainen, 2006; Liu & Wechsler, 2002; Meshgini, Aghagolzadeh, & Seyedarabi, 2013; Shen, Bai, & Fairhurst, 2007).”