Design of 3D Image Feature Point Detection System Based on Artificial Intelligence

2021 ◽  
pp. 313-323
Author(s):  
He Peng
Keyword(s):  
Artificial Intelligence ◽  
Detection System ◽  
Feature Point ◽  
Image Feature ◽  
3D Image ◽  
Point Detection

Equi-affine differential invariants for invariant feature point detection

2019 ◽  
Vol 31 (2) ◽  
pp. 277-296
Author(s):  
STANLEY L. TUZNIK ◽  
PETER J. OLVER ◽  
ALLEN TANNENBAUM
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Differential Invariants ◽  
Feature Points ◽  
Affine Invariant ◽  
Moving Frames ◽  
Feature Detectors ◽  
Point Detection ◽  
Point Detector ◽  
Method Of Moving Frames

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

Image feature point detection method based on the pixels of high-resolution sensors

2014 ◽  
Author(s):  
Xingchun Liu ◽  
Zhe Wang ◽  
Zhipeng Hu ◽  
Jiancheng Zhang
Keyword(s):  
High Resolution ◽  
Detection Method ◽  
Feature Point ◽  
Image Feature ◽  
Point Detection

35-LB: Reliability of an Artificial Intelligence (AI) Diabetic Retinopathy (DR) Detection System

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 35-LB
Author(s):  
ELI IPP ◽  
DAVID R. LILJENQUIST
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Detection System

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Unsupervised multi-spectral image feature point matching under different lighting scenes

2021 ◽  
Author(s):  
Junchong Huang ◽  
Wei Tian ◽  
Yongkun Wen ◽  
Zhan Chen ◽  
Yuyao Huang
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Spectral Image ◽  
Point Matching ◽  
Feature Point Matching
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