A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature detection from local energy
Pattern Recognition Letters
On the Edge Location Error for Local Maximum and Zero-Crossing Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection by scale multiplication in wavelet domain
Pattern Recognition Letters
Edge detection in multispectral images using the self-organizing map
Pattern Recognition Letters
Scale-adaptive detection and local characterization of edges based on wavelet transform
Signal Processing - Signal processing in communications
Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Phase congruence measurement for image similarity assessment
Pattern Recognition Letters
Palmprint image enhancement using phase congruency
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
A shearlet approach to edge analysis and detection
IEEE Transactions on Image Processing
Iris Recognition Using Phase Congruency
UKSIM '11 Proceedings of the 2011 UKSim 13th International Conference on Modelling and Simulation
IEEE Transactions on Signal Processing
A geometric approach to edge detection
IEEE Transactions on Fuzzy Systems
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
On optimal linear filtering for edge detection
IEEE Transactions on Image Processing
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
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The theory of phase congruency is that features such as step edges, roofs, and deltas always reach the maximum phase of image harmonic components. We propose a modified algorithm of phase congruency to detect image features based on two-dimensional (2-D) discrete Hilbert transform. Windowing technique is introduced to locate image features in the algorithm. Local energy is obtained by convoluting original image with two operators of removing direct current (DC) component over current window and 2-D Hilbert transform, respectively. Then, local energy is divided with the sum of Fourier amplitude of current window to retrieve the value of phase congruency. Meanwhile, we add the DC component of current window on original image to the denominator of phase congruency model to reduce the noise. Finally, the proposed algorithm is compared with some existing algorithm in systematical way. The experimental results of images in Berkeley Segmentation Dataset (BSDS) and remotely sensed images show that this algorithm is readily to detect image features.