A Sea–Sky Line Detection Method Based on the RANSAC Algorithm in the Background of Infrared Sea–Land–Sky Images

2021 ◽  
Author(s):  
Hongfei Song ◽  
Hongkai Ren ◽  
Yansong Song ◽  
Shuai Chang ◽  
Zhennan Zhao
Keyword(s):  
Detection Method ◽  
Line Detection ◽  
Ransac Algorithm

A Real-Time Sea-Sky-Line Detection Method under Complicated Sea-Sky Background

2012 ◽  
Vol 182-183 ◽  
pp. 1826-1831 ◽  
Author(s):  
Yang Gui ◽  
Xiao Hu Zhang ◽  
Yang Shang ◽  
Kun Peng Wang
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Computational Cost ◽  
Input Image ◽  
Line Detection ◽  
Image Position ◽  
Interior Points ◽  
Line Fitting ◽  
Ransac Algorithm ◽  
Correlation Matching

A real-time sea-sky-line detection method under complicated sea-sky background is presented. Firstly, a black-white template is constructed and used for fast correlation matching in several searching regions which are predefined in input image, position of maximal correlation coefficient in each predefined region is hunt out, and coordinates of several candidate sea-sky-line points are made certain according to the position. Then, RANSAC algorithm is applied to preserve interior points which are really on the sea-sky-line and eliminate exterior points which are not. Finally, line parameters of the sea-sky-line can be gained by applying least squares line fitting for all interior points. The pixels of several regions in the image instead of the whole image need to be considered, so computational cost can be reduced dramatically. The experimental results show that the proposed method can detect out sea-sky-line under complicated sea-sky background effectively and has many advantages such as strong robustness and speedy calculation.

Real-Time Line Detection Method for Camera Images of the Rescue Robot

2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Line Detection ◽  
Time Line ◽  
Rescue Robot

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

Vehicles Categorization in Complex Background

2014 ◽  
Vol 998-999 ◽  
pp. 708-711
Author(s):  
Ying Zhuo Xiang ◽  
Dong Mei Yang ◽  
Ji Kun Yan
Keyword(s):  
Detection Method ◽  
Line Length ◽  
Line Detection ◽  
View Point ◽  
Training Set ◽  
Large Variability ◽  
Linear Classifier ◽  
Complex Background ◽  
Novel Approach ◽  
Dimension Characteristic

This paper presents a novel approach to categorize multi-view vehicles in complex background using only two dimension characteristic vectors instead of high dimension vectors. Vehicles have large variability of models and the view-point makes the appearance change dramatically. Significant characteristics should be chosen as the evidence to categorize. In this paper, we categorize the vehicles into two categories – cars and lorries. Line detection method is used and calculating the average line length and the number of parallel lines as the two characteristics. A linear classifier is trained using 30 different view cars and lorries as the training set and an 10 additional different cars and lorries as the testing set.

scholarly journals EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

2021 ◽  
Vol 11 (4) ◽  
pp. 22-27
Author(s):  
Paweł Kowalski ◽  
Piotr Tojza
Keyword(s):  
Detection Method ◽  
Computational Cost ◽  
New Method ◽  
Line Detection ◽  
Curve Detection ◽  
Real Time Image ◽  
Convolution Filter ◽  
Straight Lines ◽  
Time Image ◽  
Local Detection

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚.  Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

Chord-line detection method of optical performance for medical rigid endoscopes

2014 ◽  
Vol 22 (3) ◽  
pp. 576-581
Author(s):  
杨冬 YANG Dong ◽  
侯纯伟 HOU Chun-wei ◽  
孙志辉 SUN Zhi-hui ◽  
姬军 JI Jun ◽  
李咏雪 LI Yong-xue
Keyword(s):  
Detection Method ◽  
Line Detection ◽  
Optical Performance
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