Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Liu, Xiaomin; Pan, Jeng‐Shyang

doi:10.3390/s19194244

Cited by 25 publications

(12 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those accuracy results were classified by radial basis function NNs for AR database. Also, the matching of infrared and visible images using DWPC and log-Gabor filters improved the recognition accuracy by 50% as [15] approved that. Most biometric systems do not include the run-time result in their calculations, as their systems are not designed to run for real-time applications.…”

Section: Experimantal Resultsmentioning

confidence: 77%

“…The PC approach could improve the recognition accuracy by 10% for some combination like NMPCA [14]. A distinct wavelength PC (DWPC) and log-Gabor filters were proposed for matching a visible and infrared image, the PC theory was utilized to determine PC images with affluent and intrinsic image features for noisy or complex intensity-change images [15].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

View full text Add to dashboard Cite

Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing a low-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associated angles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation, three competitive classifiers: Euclidean distance (ED), cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental results show promising recognition rates with a reasonable matching time.

show abstract

Section: Experimantal Resultsmentioning

confidence: 77%

Section: Literature Reviewmentioning

confidence: 99%

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…The paper [4] presents a novel feature point matching approach based on distinct wavelength phase congruency and log-Gabor filters for infrared and visible images, that exhibits superior performance compared to other state-of-the-art approaches.…”

Section: Special Issue Contentsmentioning

confidence: 99%

Special Issue on Intelligent Systems in Sensor Networks and Internet of Things

Choi

D’Angelo

Palmieri

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…It has good local illumination and contrast invariance. The two-dimensional phase congruency transformation function [32] is…”

Section: B Improvement Of Sssf Descriptormentioning

confidence: 99%

Registration of UAV Images Using Improved Structural Shape Similarity Based on Mathematical Morphology and Phase Congruency

Jin

Hao

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Image registration is a basic step for remote sensing image processing, such as classification, change detection, and so on. Registration of unmanned aerial vehicle (UAV) images is a challenging task because of illumination change, obvious rotation, viewpoint change, and similar textures. The change in viewing angle between adjacent images causes significant difficulties in image matching, especially for residential areas and high-rise buildings. Therefore, the feature points on the buildings are removed by morphological processing when detecting feature points in this study. Considering the similarity of terrain structural properties between adjacent UAV images, the scene shape similarity feature (SSSF) descriptor is utilized to capture the structural similarity. However, the SSSF descriptor is influenced by the illumination changes of UAV images. The phase congruency algorithm is adopted to replace the Canny operator to detect structural features when calculating the SSSF descriptors because of its robustness for illumination change. Then, the normalized correlation coefficient of SSSF descriptors is used as the similarity metric to detect tie points. Finally, the point pairs with large residuals are eliminated by random sample consensus algorithm and the tie points are detected by iteratively removing the mismatched points. The proposed method was compared to some recent related methods using correct matching ratio, root-mean-square error, and time. Five experiments including habitation, bare land, mixed terrain, farmland, and buildings are carried out to evaluate the effectiveness of the proposed method. Registration results demonstrate that the proposed method is robust for illumination and viewpoint change of UAV images and usually generates more accurate registration results than some popular registration methods.

show abstract

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Cited by 25 publications

References 67 publications

Towards better performance: phase congruency based face recognition

Towards better performance: phase congruency based face recognition

Special Issue on Intelligent Systems in Sensor Networks and Internet of Things

Registration of UAV Images Using Improved Structural Shape Similarity Based on Mathematical Morphology and Phase Congruency

Contact Info

Product

Resources

About