Histogram of oriented phase (HOP): a new descriptor based on phase congruency

2016 ◽  
Author(s):  
Hussin K. Ragb ◽  
Vijayan K. Asari
Keyword(s):  
Phase Congruency ◽  
Oriented Phase

scholarly journals HOMPC: A Local Feature Descriptor Based on the Combination of Magnitude and Phase Congruency Information for Multi-Sensor Remote Sensing Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1234 ◽  
Author(s):  
Zhitao Fu ◽  
Qianqing Qin ◽  
Bin Luo ◽  
Hong Sun ◽  
Chun Wu
Keyword(s):  
Remote Sensing ◽  
Local Feature ◽  
Feature Descriptor ◽  
Phase Congruency ◽  
Remote Sensing Images ◽  
Non Linear ◽  
Aerial Vehicle ◽  
Region Description ◽  
Oriented Phase ◽  
Linear Radiation

Local region description of multi-sensor images remains a challenging task in remote sensing image analysis and applications due to the non-linear radiation variations between images. This paper presents a novel descriptor based on the combination of the magnitude and phase congruency information of local regions to capture the common features of images with non-linear radiation changes. We first propose oriented phase congruency maps (PCMs) and oriented magnitude binary maps (MBMs) using the multi-oriented phase congruency and magnitude information of log-Gabor filters. The two feature vectors are then quickly constructed based on the convolved PCMs and MBMs. Finally, a dense descriptor named the histograms of oriented magnitude and phase congruency (HOMPC) is developed by combining the histograms of oriented phase congruency (HPC) and the histograms of oriented magnitude (HOM) to capture the structure and shape properties of local regions. HOMPC was evaluated with three datasets composed of multi-sensor remote sensing images obtained from unmanned ground vehicle, unmanned aerial vehicle, and satellite platforms. The descriptor performance was evaluated by recall, precision, F1-measure, and area under the precision-recall curve. The experimental results showed the advantages of the HOM and HPC combination and confirmed that HOMPC is far superior to the current state-of-the-art local feature descriptors.

NSCT and focus measure optimization based multi-focus image fusion

2021 ◽  
pp. 1-13
Author(s):  
N. Aishwarya ◽  
C. BennilaThangammal ◽  
N.G. Praveena
Keyword(s):  
Simulation Analysis ◽  
Structural Information ◽  
Research Area ◽  
Processing Technique ◽  
The Novel ◽  
Phase Congruency ◽  
Focus Measure ◽  
Fused Image ◽  
Focus Image ◽  
Image Pairs

Getting a complete description of scene with all the relevant objects in focus is a hot research area in surveillance, medicine and machine vision applications. In this work, transform based fusion method called as NSCT-FMO, is introduced to integrate the image pairs having different focus features. The NSCT-FMO approach basically contains four steps. Initially, the NSCT is applied on the input images to acquire the approximation and detailed structural information. Then, the approximation sub band coefficients are merged by employing the novel Focus Measure Optimization (FMO) approach. Next, the detailed sub-images are combined using Phase Congruency (PC). Finally, an inverse NSCT operation is conducted on synthesized sub images to obtain the initial synthesized image. To optimize the initial fused image, an initial decision map is first constructed and morphological post-processing technique is applied to get the final map. With the help of resultant map, the final synthesized output is produced by the selection of focused pixels from input images. Simulation analysis show that the NSCT-FMO approach achieves fair results as compared to traditional MST based methods both in qualitative and quantitative assessments.

scholarly journals Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 894 ◽  
Author(s):  
Nasser Tamim ◽  
M. Elshrkawey ◽  
Gamil Abdel Azim ◽  
Hamed Nassar
Keyword(s):  
Blood Vessel ◽  
Performance Metrics ◽  
Feature Vector ◽  
Disease Diagnosis ◽  
Processing Technique ◽  
Ocular Disease ◽  
Multi Layer Perceptron ◽  
Phase Congruency ◽  
Hybrid Features ◽  
Local Intensity

Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.

Oriented phase III poly(vinylidene fluoride)

1979 ◽  
Vol 17 (9) ◽  
pp. 585-589 ◽  
Author(s):  
S. Weinhold ◽  
M. H. Litt ◽  
J. B. Lando
Keyword(s):  
Vinylidene Fluoride ◽  
Phase Iii ◽  
Poly Vinylidene Fluoride ◽  
Oriented Phase

Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency

2019 ◽  
Vol 57 (4) ◽  
pp. 1995-2008 ◽  
Author(s):  
Zhen Ye ◽  
Xiaohua Tong ◽  
Shouzhu Zheng ◽  
Chengcheng Guo ◽  
Sa Gao ◽  
...  
Keyword(s):  
Image Correlation ◽  
Phase Congruency ◽  
Correlation Methods
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