scholarly journals Achieving Turbidity Robustness on Underwater Images Local Feature Detection

2015 ◽  
Author(s):  
Felipe Codevilla ◽  
Joel De O. Gaya ◽  
Nelson Duarte Filho ◽  
Silvia S. C. Costa Botelho
Keyword(s):  
Feature Detection ◽  
Local Feature

scholarly journals α-shapes for local feature detection

2016 ◽  
Vol 50 ◽  
pp. 56-73 ◽  
Author(s):  
Christos Varytimidis ◽  
Konstantinos Rapantzikos ◽  
Yannis Avrithis ◽  
Stefanos Kollias
Keyword(s):  
Feature Detection ◽  
Local Feature

scholarly journals An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Luan Xidao ◽  
Xie Yuxiang ◽  
Zhang Lili ◽  
Zhang Xin ◽  
Li Chen ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Feature Detection ◽  
Feature Vector ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Local Feature ◽  
Image Similarity ◽  
Sift Feature ◽  
Similarity Detection ◽  
Overcomplete Dictionary

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.

scholarly journals Dithering-based Sampling and Weighted α-shapes for Local Feature Detection

2015 ◽  
Vol 7 (0) ◽  
pp. 189-200 ◽  
Author(s):  
Christos Varytimidis ◽  
Konstantinos Rapantzikos ◽  
Yannis Avrithis ◽  
Stefanos Kollias
Keyword(s):  
Feature Detection ◽  
Local Feature

scholarly journals Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery

2020 ◽  
Vol 10 (12) ◽  
pp. 4078 ◽  
Author(s):  
Francesca Manni ◽  
Fons van der Sommen ◽  
Svitlana Zinger ◽  
Caifeng Shan ◽  
Ronald Holthuizen ◽  
...  
Keyword(s):  
Reference Frame ◽  
Hyperspectral Imaging ◽  
Spinal Surgery ◽  
Feature Detection ◽  
Surgical Navigation ◽  
Pedicle Screws ◽  
Local Feature ◽  
Detection Methods ◽  
Average Error ◽  
Electromagnetic Spectrum

In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.

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