Evidence map›Paper›PMID 39056863›Full record

ReviewBiomimetics (Basel, Switzerland)2024

Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey.

Yibin Tian, Ming Wen, Dajiang Lu, Xiaopin Zhong, Zongze Wu

Abstract readReview
In one paragraph

Review in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yibin TianCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0009-0008-4590-8318
Ming WenCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Dajiang LuCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Xiaopin ZhongCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-8761-9396
Zongze WuCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.

Funding

Equipment Development Project of Shenzhen University 868/03010315National Natural Science Foundation of China 62327808Shenzhen Fundamental Research Fund JCYJ20230808105212023Shenzhen University 2035 Program 00000224
6 · The paper itself

Abstract

The concept of Image Phase Congruency (IPC) is deeply rooted in the way the human visual system interprets and processes spatial frequency information. It plays an important role in visual perception, influencing our capacity to identify objects, recognize textures, and decipher spatial relationships in our environments. IPC is robust to changes in lighting, contrast, and other variables that might modify the amplitude of light waves yet leave their relative phase unchanged. This characteristic is vital for perceptual tasks as it ensures the consistent detection of features regardless of fluctuations in illumination or other environmental factors. It can also impact cognitive and emotional responses; cohesive phase information across elements fosters a perception of unity or harmony, while inconsistencies can engender a sense of discord or tension. In this survey, we begin by examining the evidence from biological vision studies suggesting that IPC is employed by the human perceptual system. We proceed to outline the typical mathematical representation and different computational approaches to IPC. We then summarize the extensive applications of IPC in computer vision, including denoise, image quality assessment, feature detection and description, image segmentation, image registration, image fusion, and object detection, among other uses, and illustrate its advantages with a number of examples. Finally, we discuss the current challenges associated with the practical applications of IPC and potential avenues for enhancement.

Indexed as

deep neural networkfeature detectionFourier analysishuman visual systemimage fusionimage qualityimage registrationmonogenic filterobject detection and trackingphase congruencywavelet transform

Identifiers

PMID39056863
PMCPMC11274423

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.