Image Feature Detection as Robust Model Fitting

2006 ◽  
pp. 673-682 ◽  
Author(s):  
Dengfeng Chai ◽  
Qunsheng Peng
Keyword(s):  
Feature Detection ◽  
Model Fitting ◽  
Image Feature ◽  
Robust Model

scholarly journals Performance Evaluation of Gradient Domain and Pyramid Blending Used in Image Stitching Process with ORB Binary Descriptor

2019 ◽  
Vol 9 (1) ◽  
pp. 1849-1854
Keyword(s):  
Feature Detection ◽  
Computation Time ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Image Feature ◽  
Image Stitching ◽  
Imaging Data ◽  
Multiple Images ◽  
Binary Descriptor ◽  
Blending Method

Panorama development is the basically method of integrating multiple images captured of the same scene under consideration to get high resolution image. This process is useful for combining multiple images which are overlapped to obtain larger image. Usefulness of Image stitching is found in the field related to medical imaging, data from satellites, computer vision and automatic target recognition in military applications. The goal objective of this research paper is basically for developing an high improved resolution and its quality panorama having with high accuracy and minimum computation time. Initially we compared different image feature detectors and tested SIFT, SURF, ORB to find out the rate of detection of the corrected available key points along with processing time. Later on, testing is done with some common techniques of image blending or fusion for improving the mosaicing quality process. In this experimental results, it has been found out that ORB image feature detection and description algorithm is more accurate, fastest which gives a higher performance and Pyramid blending method gives the better stitching quality. Lastly panorama is developed based on combination of ORB binary descriptor method for finding out image features and pyramid blending method.

Multimodal image feature detection with ROI-based optimization for image registration

2019 ◽  
Vol 17 (4) ◽  
pp. 1007-1013
Author(s):  
Rajesh Nandalike ◽  
H. Sarojadevi
Keyword(s):  
Image Registration ◽  
Feature Detection ◽  
Image Feature

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

2019 ◽  
Vol 12 (2) ◽  
pp. 156-164 ◽  
Author(s):  
Nick M Murray ◽  
Mathias Unberath ◽  
Gregory D Hager ◽  
Ferdinand K Hui
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Ischemic Stroke ◽  
Acute Stroke ◽  
Feature Detection ◽  
Performance Metrics ◽  
Large Vessel ◽  
Image Feature ◽  
Stroke Treatment

Background and purposeAcute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.MethodsA systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed.ResultsVariations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems).ConclusionsAI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.

A comparative study of image feature detection and matching algorithms for touchless fingerprint systems.

2016 ◽  
Vol 2016 (15) ◽  
pp. 1-9 ◽  
Author(s):  
Sos S. Agaian ◽  
Marzena (Mary Ann) Mulawka ◽  
Rahul Rajendran ◽  
Shishir P Rao ◽  
Shreyas Kamath K.M ◽  
...  
Keyword(s):  
Comparative Study ◽  
Feature Detection ◽  
Image Feature

Enabling Users to Guide the Design of Robust Model Fitting Algorithms

2007 ◽  
Author(s):  
Matthias Wimmer ◽  
Freek Stulp ◽  
Bernd Radig
Keyword(s):  
Model Fitting ◽  
Robust Model

Robust Model Fitting Using Higher Than Minimal Subset Sampling

2016 ◽  
Vol 38 (2) ◽  
pp. 350-362 ◽  
Author(s):  
Ruwan B. Tennakoon ◽  
Alireza Bab-Hadiashar ◽  
Zhenwei Cao ◽  
Reza Hoseinnezhad ◽  
David Suter
Keyword(s):  
Model Fitting ◽  
Robust Model ◽  
Minimal Subset

Designing optimal image feature detection masks: Equal area rule

1999 ◽  
Vol 35 (6) ◽  
pp. 463 ◽  
Author(s):  
E.R. Davies
Keyword(s):  
Feature Detection ◽  
Image Feature ◽  
Equal Area ◽  
Area Rule

scholarly journals Robust model fitting via convex optimization techniques

2016 ◽  
Author(s):  
Yongfang Cheng
Keyword(s):  
Convex Optimization ◽  
Model Fitting ◽  
Optimization Techniques ◽  
Robust Model

scholarly journals Optimization and Implementation of Synthetic Basis Feature Descriptor on FPGA

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 391
Author(s):  
Dah-Jye Lee ◽  
Samuel G. Fuller ◽  
Alexander S. McCown
Keyword(s):  
High Speed ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Feature Matching ◽  
Image Features ◽  
Image Feature ◽  
Superior Performance ◽  
Feature Descriptor ◽  
Binary Descriptors ◽  
Field Programmable

Feature detection, description, and matching are crucial steps for many computer vision algorithms. These steps rely on feature descriptors to match image features across sets of images. Previous work has shown that our SYnthetic BAsis (SYBA) feature descriptor can offer superior performance to other binary descriptors. This paper focused on various optimizations and hardware implementation of the newer and optimized version. The hardware implementation on a field-programmable gate array (FPGA) is a high-throughput low-latency solution which is critical for applications such as high-speed object detection and tracking, stereo vision, visual odometry, structure from motion, and optical flow. We compared our solution to other hardware designs of binary descriptors. We demonstrated that our implementation of SYBA as a feature descriptor in hardware offered superior image feature matching performance and used fewer resources than most binary feature descriptor implementations.

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