Phase congruency: A low-level image invariant

By Peter Kovesi. Psychological Research, 2000

411 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Mechanical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/874ef16d1d5d9a4e7e876ea1e87e78a1cc8c1661

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. A. Bovik
2. Zhou Wang
3. Luc Van Gool
4. Eero P. Simoncelli
5. Dacheng Tao
6. Kede Ma
7. Radu Timofte
8. Ming-Hsuan Yang

In Mechanical Engineering

1. Zhou Wang
2. A. Bovik
3. Gaurav Bhatnagar
4. Zheng Liu
5. Q. Wu
6. Thomas Wiegand
7. Sebastian Bosse
8. Rafael Reisenhofer

In Media Technology

1. A. Bovik
2. Zhou Wang
3. Eero P. Simoncelli
4. Damon M. Chandler
5. Kede Ma
6. Weisi Lin
7. Radu Timofte
8. Lei Zhang

Citations in the corpus

• Complex Wavelet Structural Similarity: A New Image Similarity Index (2009, methodology): “In previous work, a similar phase correlation idea had been employed for feature localization [33], [34], texture description [37], and blur detection [35], but, to the best of our knowledge, has not been used for image similarity measurement.”
• RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, background): “…orientations, and introducing the noise compensation term T , the final 2D PC model is (more details about the 2D PC model can be found in Reference [50]): where w o ( x , y ) is a weighting function; ξ is a small value; (cid:3)·(cid:4) operator prevents the enclosed quantity from getting a…”
• A Haar wavelet-based perceptual similarity index for image quality assessment (2016, methodology): “In the definition of the FSIM, both local similarities and weights rely on the phase congruency measure [22], whose computation requires images to be convolved with 16 complex-valued filters and contains several non-trivial steps such as adaptive thresholding.”
• Modern Image Quality Assessment (2006, background): “In fact, consistency of global (Fourier) and local (wavelet) phases across scale and space has been used to characterize image features [65–67].”
• Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain (2013, methodology): “Phase congruency is a measure of feature perception in the images which provides a illumination and contrast invariant feature extraction method [30], [31].”
• Phase Congruency Detects Corners and Edges (2003, background): “[14], Venkatesh and Owens [15], and Kovesi [16–20, 11].”
• Metamers of the ventral stream (2011, background): “Correlations across frequencies encode features with aligned phase or magnitude (for example, sharp edges or lines”