scholarly journals A New Simple Procedure for Extracting Coastline from SAR Image Based on Low Pass Filter and Edge Detection Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 175
Author(s):  
Ni Nyoman Pujianiki ◽  
I Nyoman Sudi Parwata ◽  
Takahiro Osawa
Keyword(s):  
Simple Procedure ◽  
Detection Algorithm ◽  
Sar Image ◽  
Dual Polarization ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Edge Detection Algorithm ◽  
Low Pass ◽  
Pixel Value ◽  
Single Polarization

This study proposes a new simple procedure for extracting coastline from Synthetic Aperture Radar (SAR) images by utilizing a low-pass filter and edge detection algorithm. The low-pass filter is used to improve the histogram of the pixel value of the SAR image. It provides better distribution of pixel value and makes it easy to separate between sea and land surfaces. This study provides the processing steps using open-source software, i.e., SNAP SAR processor and QGIS application. This procedure has been tested using dual polarization Sentinel-1 (10x10 meters resolution) and single polarization ALOS-2 (3x3 meters resolution) dataset. The results show that using Sentinel-1 with dual polarization (VH) provides a better result than single polarization (VV). In the ALOS-2 case, only single polarization (HH) is available. However, even using only HH polarization, ALOS-2 provides a good result. In terms of resolution, ALOS-2 provides a better coastline than Sentinel-1 data due to ALOS-2 has better resolution. This procedure is expected to be helpful to detect coastline changes and for coastal area management.

A quantum edge detection algorithm for quantum multi-wavelength images

2021 ◽  
pp. 2150017
Author(s):  
Engin Şahin ◽  
İhsan Yilmaz
Keyword(s):  
Edge Detection ◽  
Noise Reduction ◽  
Circuit Complexity ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Low Pass ◽  
Multi Wavelength

Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.

Based on the CO2 Gas Shielded Welding Molten Pool Image Edge Detection Algorithm

2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao
Keyword(s):  
Edge Detection ◽  
Molten Pool ◽  
High Speed ◽  
Vision System ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Defect Recognition ◽  
Pool Image

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.

scholarly journals Window Memoization In Software As Applied To Image Processing Algorithms

2021 ◽  
Author(s):  
Tahir Jaffer
Keyword(s):  
Image Processing ◽  
Lower Bound ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Image Processing Algorithm ◽  
Pass Filter ◽  
Pixel Intensity ◽  
Low Pass Filter ◽  
Low Pass ◽  
Speed Up

A new local image processing algorithm, the Tahir algorithm, is an adaptation to the standard low-pass filter. Its design is for images that have the spectrum of pixel intensity concentrated at the lower end of the intensity spectrum. Window memoization is a specialization of memoization. Memoization is a technique to reduce computational redundancy by skipping redundant calculations and storing results in memory. An adaptation for window memozation is developed based on improved symbol generation and a new eviction policy. On implementation, the mean lower-bound speed-up achieved was between 0.32 (slowdown of approximately 3) and 3.70 with a peak of 4.86. Lower-bound speed-up is established by accounting for the time to create and delete the cache. Window memoization was applied to: the convolution technique, Trajkovic corner detection algorithm and the Tahir algorithm. Window memoization can be evaluated by calculating both the speed-up achieved and the error introduced to the output image.

scholarly journals Window Memoization In Software As Applied To Image Processing Algorithms

2021 ◽  
Author(s):  
Tahir Jaffer
Keyword(s):  
Image Processing ◽  
Lower Bound ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Image Processing Algorithm ◽  
Pass Filter ◽  
Pixel Intensity ◽  
Low Pass Filter ◽  
Low Pass ◽  
Speed Up

A new local image processing algorithm, the Tahir algorithm, is an adaptation to the standard low-pass filter. Its design is for images that have the spectrum of pixel intensity concentrated at the lower end of the intensity spectrum. Window memoization is a specialization of memoization. Memoization is a technique to reduce computational redundancy by skipping redundant calculations and storing results in memory. An adaptation for window memozation is developed based on improved symbol generation and a new eviction policy. On implementation, the mean lower-bound speed-up achieved was between 0.32 (slowdown of approximately 3) and 3.70 with a peak of 4.86. Lower-bound speed-up is established by accounting for the time to create and delete the cache. Window memoization was applied to: the convolution technique, Trajkovic corner detection algorithm and the Tahir algorithm. Window memoization can be evaluated by calculating both the speed-up achieved and the error introduced to the output image.

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

ANALYSIS AND IMPROVEMENT TECHNIQUES FOR THE TRANSFER FUNCTION OF A PLANAR LOW - PASS FILTER

2016 ◽  
Vol 15 (12) ◽  
pp. 2579-2586
Author(s):  
Adina Racasan ◽  
Calin Munteanu ◽  
Vasile Topa ◽  
Claudia Pacurar ◽  
Claudia Hebedean
Keyword(s):  
Transfer Function ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass
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