scholarly journals Integrated system for automatic detection of representative video frames in wireless capsule endoscopy using adaptive sliding window singular value decomposition

2020 ◽  
Vol 14 (1) ◽  
pp. 147-153 ◽  
Author(s):  
Abbas Biniaz ◽  
Fatemeh Abdolali ◽  
Reza Aghaeizadeh Zoroofi
Keyword(s):  
Singular Value Decomposition ◽  
Capsule Endoscopy ◽  
Sliding Window ◽  
Automatic Detection ◽  
Wireless Capsule Endoscopy ◽  
Singular Value ◽  
Integrated System ◽  
Video Frames ◽  
Value Decomposition ◽  
Wireless Capsule

scholarly journals Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Vahid Faghih Dinevari ◽  
Ghader Karimian Khosroshahi ◽  
Mina Zolfy Lighvan
Keyword(s):  
Singular Value Decomposition ◽  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
Singular Value ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Data Set ◽  
Value Decomposition ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

Automatic detection of informative frames from wireless capsule endoscopy images

2010 ◽  
Vol 14 (3) ◽  
pp. 449-470 ◽  
Author(s):  
M.K. Bashar ◽  
T. Kitasaka ◽  
Y. Suenaga ◽  
Y. Mekada ◽  
K. Mori
Keyword(s):  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Wireless Capsule Endoscopy ◽  
Wireless Capsule

scholarly journals Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning

2019 ◽  
Vol 47 (1) ◽  
pp. 52-63 ◽  
Author(s):  
Pedro M. Vieira ◽  
Nuno R Freitas ◽  
João Valente ◽  
Ismael F. Vaz ◽  
Carla Rolanda ◽  
...  
Keyword(s):  
Small Bowel ◽  
Ensemble Learning ◽  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Wireless Capsule Endoscopy ◽  
Small Bowel Tumors ◽  
Wireless Capsule

An Integrated System for Wireless Capsule Endoscopy in a Liquid-Distended Stomach

2014 ◽  
Vol 61 (3) ◽  
pp. 794-804 ◽  
Author(s):  
Iris De Falco ◽  
Giuseppe Tortora ◽  
Paolo Dario ◽  
Arianna Menciassi
Keyword(s):  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Integrated System ◽  
Wireless Capsule ◽  
Distended Stomach

scholarly journals A Novel Blind and Robust Video Watermarking Technique in Fast Motion Frames Based on SVD and MR-SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Imen Nouioua ◽  
Nouredine Amardjia ◽  
Sarra Belilita
Keyword(s):  
Singular Value Decomposition ◽  
Video Quality ◽  
Singular Value ◽  
Video Watermarking ◽  
Digital Video Watermarking ◽  
Video Frames ◽  
Fast Motion ◽  
Good Transparency ◽  
Value Decomposition

In this work, a novel and efficient digital video watermarking technique based on the Singular Value Decomposition performed in the Multiresolution Singular Value Decomposition domain is proposed. While most of the existing watermarking schemes embed the watermark in all the video frames, which is time-consuming and also affects the perceptibly of the video quality, the proposed method chooses only the fast motion frames in each shot to host the watermark. In doing so, the number of frames to be processed is consequently reduced and a better quality of the watermarked video is also ensured since the human visual system cannot notice the variations in fast moving regions. The watermark information is embedded by Quantization Index Modulation which is a blind watermarking algorithm. The experimental results demonstrate that the proposed method can achieve a very good transparency, while being robust against various kinds of attacks such as filtering, noising, compression, and frame collusion. Compared with several methods found in the literature, the proposed method gives a better robustness.

Observability Analysis of SINS/GPS during In-Motion Alignment Using Singular Value Decomposition

2012 ◽  
Vol 433-440 ◽  
pp. 5918-5923 ◽  
Author(s):  
Yao Li ◽  
Yong Li ◽  
Chris Rizos ◽  
Xiao Su Xu
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Integrated System ◽  
Accelerated Motion ◽  
Observability Analysis ◽  
Attitude Error ◽  
Axis Rotation ◽  
Value Decomposition ◽  
Svd Method ◽  
Degree Of Observability

The in-motion alignment of a SINS/GPS integrated system uses the GPS velocity and position as observations, hence the SINS position and velocity errors are instantaneously observable. However the attitude error is not directly observable, it is important to find a way to improve the observability of attitude error because the degree-of-observability of the attitude error affects the accuracy of alignment. In this paper, the singular value decomposition (SVD) method is used to analyze the degree-of-observability of SINS/GPS during in-motion alignment. Simulations involving nine typical maneuvers indicate that both the yaw-axis rotation and the accelerated motion can improve the degree-of-observability of attitude error. Of the nine types of maneuvers, the triaxial rotation with acceleration is the most effective maneuver for improving the degree-of-observability of a SINS/GPS integrated system.

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