Hydro-acoustic Survey and Edge-detection method in Investigation of a Passenger Vessel Accident in East Java, Indonesia

2020 ◽  
Vol 9 (1) ◽  
pp. 59-68
Author(s):  
Hendra Kurnia Febriawan ◽  
Yudo Haryadi ◽  
Aleik Nurwahyudy
Keyword(s):  
Edge Detection ◽  
Risk Mitigation ◽  
Detection Methods ◽  
Accident Investigation ◽  
Side Scan Sonar ◽  
In Situ Investigation ◽  
Acoustic Technologies ◽  
Edge Detection Method ◽  
Ocean Environment ◽  
Passenger Vessel

As an archipelagic country, the shipping sector in Indonesia becomes crucial in delivering goods inter-island, and due to increasing transportation demands. However, that industry encounters some challenges of the ocean environment that could lead to vessel accidents. An investigation into the accident is crucial since this is related to the properties, environment, and life disadvantages. The wrecks of sinking vessels also could harm the environment, providing an obstacle to the sea passage hence increasing the risk of a shipping operation. A proper and comprehensive investigation needs to be carried out to identify the factors that contribute to the accident, so then risk mitigation can be taken to prevent re-occurrence. In the case of missing foundered or sunken vessels, an underwater examination is a must, so the investigator understands the real condition of the vessel. Although diver and underwater robotic surveys are still prevalent in the investigation, these techniques have limitations due to visibility and location. By contrast, those limitations can be addressed using hydro-acoustic technologies, which are capable of providing high-resolution underwater images and digital elevation model (DEM) bathymetry. Thus, the use of these technologies is promising in-vessel accident investigation, both in-situ investigation, and post-processing analysis. This paper describes an examination of the use of side-scan sonar and multibeam echosounder in-vessel accident investigation. The use of slope feature and edge-detection technique are also investigated concerning the investigation. Results indicate that those acoustic systems can contribute to the inquiry effectively by portraying some underwater objects as the accident suspects. Besides, slope and edge detection methods also produce expectant outcomes to support underwater object detection and investigation.

Using Local Edge Pattern Descriptors for Edge Detection

2017 ◽  
Vol 32 (03) ◽  
pp. 1850006 ◽  
Author(s):  
Yu Wang ◽  
Na Zhang ◽  
Huaixin Yan ◽  
Min Zuo ◽  
Cuiling Liu
Keyword(s):  
Edge Detection ◽  
Vital Role ◽  
Detection Methods ◽  
Weighted Fusion ◽  
Edge Strength ◽  
Detection Approach ◽  
Segmentation Pattern ◽  
Edge Detection Method ◽  
Local Edge ◽  
Edge Pattern

Edge detection is an active and critical topic in the field of image processing, and plays a vital role for some important applications such as image segmentation, pattern classification, object tracking, etc. In this paper, an edge detection approach is proposed using local edge pattern descriptor which possesses multiscale and multiresolution property, and is named varied local edge pattern (VLEP) descriptor. This method contains the following steps: firstly, Gaussian filter is used to smooth the original image. Secondly, the edge strength values, which are used to calculate the edge gradient values and can be obtained by one or more groups of VLEPs. Then, weighted fusion idea is considered when multiple groups of VLEP descriptors are used. Finally, the appropriate threshold is set to perform binarization processing on the gradient version of the image. Experimental results show that the proposed edge detection method achieved better performance than other state-of-the-art edge detection methods.

Finger Vein ROI Extraction Based on Robust Edge Detection and Flexible Sliding Window

2017 ◽  
Vol 32 (04) ◽  
pp. 1856002 ◽  
Author(s):  
Mingwen Wang ◽  
Dongming Tang ◽  
Zhangyou Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Finger Joint ◽  
Reference Line ◽  
Detection Methods ◽  
Joint Region ◽  
Sliding Windows ◽  
Finger Vein ◽  
Roi Extraction ◽  
Edge Detection Method

An accurate region of interest extraction (ROI) plays an important role for both finger vein recognition systems and finger vein-based cryptography systems. In order to localize the rectangle ROI accurately, the edges of the finger and a line in the finger joint region should be detected accurately as a reference position. Because most of the existing finger edge detection methods do not work well, a robust finger edge detection method is proposed in this paper. An inner line of the finger is first detected to divide the finger vein image by two parts, after that two edge detection templates and a series of technologies such as interpolation, fit, etc. are used to detect and fix the wrong edges of the finger. Furthermore, considering that the shapes of the brighter finger joint region are irregular, multiple sliding windows including rectangle, disk, diamond and ellipse are generated, respectively to detect the reference line of the finger joint. Finally, a contour similarity distance-based method is introduced to evaluate the performance of various sliding windows. The experimental results show that the proposed edge detection method can 100% successfully detect the edges of the fingers in our finger vein image database. And for various detection windows, the ellipse window is more suitable for the detection of the finger joint reference line. So, the proposed ROI extraction method for finger vein images has a better overall performance compared with the other methods.

scholarly journals A novel edge detection method based on efficient gaussian binomial filter

2021 ◽  
Vol 7 (2) ◽  
pp. 211
Author(s):  
El Houssain Ait Mansour ◽  
Francois Bretaudeau
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Detection Method ◽  
Detection Methods ◽  
Gaussian Filter ◽  
Image Edge Detection ◽  
Low Pass ◽  
Image Edge ◽  
Edge Detection Method ◽  
Low Pass Filters

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

scholarly journals Comparative Analysis of Image Quality Values on Edge Detection Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Edge Detection ◽  
Computer Technology ◽  
Detection Method ◽  
Detection Methods ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.  

Edge Detection Using the Bhattacharyya Distance with Adjustable Block Space

2020 ◽  
Vol 2020 (10) ◽  
pp. 133-1-133-7
Author(s):  
Jiho Yoon ◽  
Chulhee Lee
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Wiener Filter ◽  
Detection Methods ◽  
Bhattacharyya Distance ◽  
Block Space ◽  
Mean Vectors ◽  
The Mean ◽  
Edge Detection Method ◽  
Mean Vector

In this paper, we propose a new edge detection method for color images, based on the Bhattacharyya distance with adjustable block space. First, the Wiener filter was used to remove the noise as pre-processing. To calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. To detect subtle edges, we adjusted the block space. The mean vector and covariance matrix were computed from each block. Using the mean vectors and covariance matrices, we computed the Bhattacharyya distance, which was used to detect edges. By adjusting the block space, we were able to detect weak edges, which other edge detections failed to detect. Experimental results show promising results compared to some existing edge detection methods.

scholarly journals The Impact of the Implementation of Edge Detection Methods on the Accuracy of Automatic Voltage Reading

2017 ◽  
Vol 17 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Kamil Sidor ◽  
Anna Szlachta
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Detection Methods ◽  
Reading Accuracy ◽  
Vision Development ◽  
Sobel Operator ◽  
Canny Edge ◽  
Automatic Reading ◽  
Edge Detection Method ◽  
The Impact

AbstractThe article presents the impact of the edge detection method in the image analysis on the reading accuracy of the measured value. In order to ensure the automatic reading of the measured value by an analog meter, a standard webcam and the LabVIEW programme were applied. NI Vision Development tools were used. The Hough transform was used to detect the indicator. The programme output was compared during the application of several methods of edge detection. Those included: the Prewitt operator, the Roberts cross, the Sobel operator and the Canny edge detector. The image analysis was made for an analog meter indicator with the above-mentioned methods, and the results of that analysis were compared with each other and presented.

A NEW EDGE DETECTION METHOD THROUGH TEMPLATE MATCHING

1994 ◽  
Vol 08 (04) ◽  
pp. 899-917 ◽  
Author(s):  
MAO-JIUN J. WANG ◽  
SHIAU-CHYI CHANG ◽  
CHIH-MING LIU ◽  
WEN-YEN WU
Keyword(s):  
Edge Detection ◽  
Template Matching ◽  
Pattern Analysis ◽  
Detection Method ◽  
Performance Criteria ◽  
Detection Methods ◽  
Edge Detector ◽  
Edge Detectors ◽  
Edge Detection Method ◽  
Single Pixel

This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.

scholarly journals A Simple, Precise, and High-Speed Die Edge Detection Framework for the Semiconductor Industry

2021 ◽  
Author(s):  
Xiao Jian Tan ◽  
Wai Zhe Leow ◽  
Wai Loon Cheor
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Cross Correlation ◽  
Semiconductor Industry ◽  
Computation Time ◽  
Production Unit ◽  
Detection Methods ◽  
Stable Performance ◽  
Edge Detection Method ◽  
Non Destructive

Abstract This paper presents an automated high-speed die edge detection method that is meant to be implemented in non-destructive die-level defects inspection in the semiconductor industry. This paper specifically aims to achieve high accuracy (or yield) of greater than 99.95% with stable performance within a short computation time (i.e., less than 30 ms), provided the targeted unit-per-hour (UPH) is at 30,000. To demonstrate the applicability of the proposed method, the proposed method is validated using three production units (i.e., Production units A: 6000 units; Production unit B: 3500 units; Production unit C: 4000 units) and is benchmarked to two common edge detection methods that are widely implemented in the semiconductor industry nowadays (i.e., cross-correlation and normalized cross-correlation methods). The results obtained show that the proposed method is capable to perform die edge detection with promising accuracy and stable performance by achieving 100.0% yield in all three production units and outperformed the benchmarking methods. Also, the overall computation time (considering die edge detection and die rotation) of the proposed method is short, which is approximately 30 ms.

Research on the Edge Detection Method of the Rice Leaves Image Based on Phase Consistency under the Complex Environment

2012 ◽  
Vol 605-607 ◽  
pp. 2145-2148
Author(s):  
Li Bo Liu ◽  
Chun Jiang Zhao ◽  
Hua Rui Wu ◽  
Rong Hua Gao
Keyword(s):  
Edge Detection ◽  
Test Methods ◽  
Disease Classification ◽  
Detection Methods ◽  
Main Body ◽  
Rice Leaf ◽  
Single Leaf ◽  
Blade Edge ◽  
Edge Detection Method ◽  
Single Rice

In order to realize the effective segmentation of the rice leaf lesion, how to remove background interference and isolating single rice leaf from collected images is the basis of disease classification and recognition. The main body information of the images collected under the simple background of lighting box and field contains only single leaf, so we can use the normal way to detect the blade edge. However, because of the outside interference under the background of field, parameters can't be quantified and the treatment effect of using ordinary edge detection methods is bad. This paper takes advantage of the characteristics that phase consistency test methods that protect them from the image contrast and brightness change and have a strong ability to resist noise to realize complete extraction of the main blade edge in the rice leaf image under the complex background.

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