Relevance of useful visual words in object retrieval

2013 ◽  
Author(s):  
Siyuan Qi ◽  
Yupin Luo
Keyword(s):  
Object Retrieval ◽  
Visual Words

scholarly journals Unsupervised auxiliary visual words discovery for large-scale image object retrieval

CVPR 2011 ◽  
2011 ◽  
Author(s):  
Yin-Hsi Kuo ◽  
Hsuan-Tien Lin ◽  
Wen-Huang Cheng ◽  
Yi-Hsuan Yang ◽  
Winston H. Hsu
Keyword(s):  
Large Scale ◽  
Object Retrieval ◽  
Visual Words ◽  
Image Object

3D object retrieval via range image queries in a bag-of-visual-words context

2013 ◽  
Vol 29 (12) ◽  
pp. 1351-1361 ◽  
Author(s):  
Konstantinos Sfikas ◽  
Theoharis Theoharis ◽  
Ioannis Pratikakis
Keyword(s):  
Range Image ◽  
3D Object Retrieval ◽  
Bag Of Visual Words ◽  
Object Retrieval ◽  
3D Object ◽  
Visual Words

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

Explainable Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images

2020 ◽  
Author(s):  
Athanasios Kallipolitis ◽  
Alexandros Stratigos ◽  
Alexios Zarras ◽  
Ilias Maglogiannis
Keyword(s):  
Skin Cancer ◽  
Visual Words ◽  
Confocal Images ◽  
Fully Connected

Fast and Resilient Manipulation Planning for Object Retrieval in Cluttered and Confined Environments

2021 ◽  
pp. 1-14
Author(s):  
Changjoo Nam ◽  
Sang Hun Cheong ◽  
Jinhwi Lee ◽  
Dong Hwan Kim ◽  
ChangHwan Kim
Keyword(s):  
Object Retrieval ◽  
Manipulation Planning
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