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1

Determination of reactivated regions and faults in the Iraq Southern Desert with the new edge technique, Inverse Tilt Angle of Second-gradients (ITAS)

Al-Bahadily Hayder Adnan, Al-Rahim Ali Maki, Smith Richard Stuart

Acta Geophysica

|

2024

|

Vol. 72, no. 3

1675--1692

EN

A well-known and essential task in magnetic data interpretation is the structural mapping of basement terranes. Useful tools for analyzing the deep basement are edge-detection filters that normalize the first-/second-order gradients of the magnetic field in three spatial directions. Different edge-detection filters can reveal different structures; for example, filters that normally involve higher-order gradients may detect small-scale magnetic sources. This study aims to define magnetic bodies and interpret intra-basement structures from aeromagnetic data. For this purpose, we utilize three commonly used edge-detection filters that show edges as peaks, which can sometimes be quite broad. In addition, we suggest a new high-resolution filter that shows edges as a sharp crossover. The filters have been tested on the Bishop magnetic model and real airborne magnetic data from the Iraq Southern Desert (SD). Comparing the products of the proposed crossover filter with the three common filters shows similar high-resolution products, but the new filter can often highlight both shallow and deep subtle basement fabric. Our study of the SD magnetic dataset defined older reactivated regions, structural elements, and the trends of intrabasement fractures associated with more recent seismic events.

2

EDGE detection technique based on bilateral filtering and iterative threshold selection algorithm and transfer learning for traffic sign recognition

Parse Milind, Pramod Dhanya

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2023

|

z. 119

199--222

EN

The traffic sign identification and recognition system (TSIRS) is an essential component for autonomous vehicles to succeed. The TSIRS helps to collect and provide helpful information for autonomous driving systems. The information may include limits on speed, directions for driving, signs to stop or lower the speed, and many more essential things for safe driving. Recently, incidents have been reported regarding autonomous vehicle crashes due to traffic sign identification and recognition system failures. The TSIRS fails to recognize the traffic signs in challenging conditions such as skewed signboards, scratches on traffic symbols, discontinuous or damaged traffic symbols, etc. These challenging conditions are presented for various reasons, such as accidents, storms, artificial damage, etc. Such traffic signs contain an ample amount of noise, because of which traffic sign identification and recognition become a challenging task for automated TSIRS systems. The proposed method in this paper addresses these challenges. The sign edge is a helpful feature for the recognition of traffic signs. A novel traffic sign edge detection algorithm is introduced based on bilateral filtering with adaptive thresholding and varying aperture size that effectively detects the edges from such noisy images. The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. The performance of the proposed method is evaluated and compared with existing edge detection methods. The results show that the proposed algorithm achieves optimal Mean Square Error (MSE) and Root Mean Square Error (RMSE) error rates and has a better Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) ratio than the traditional edge detection algorithms. Furthermore, the precision rate, recall rate, and F1 scores are evaluated for the CNN models. With the German Traffic Sign Benchmark database (GTSRB), the proposed algorithm and Inception V3 CNN model gives promising results when it receives the edge-detected images for training and testing.

3

Research on Railway Track Edge Detection Based on BM3D and Zernike Moments

Wang Nan, Huo Tao, Zhang Tianming

Archives of Transport

|

2023

|

Vol. 68, iss. 4

7--20

EN

With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.

4

A new method of balanced edge detection based on curvature for gravity data

Dandan Jiang, Qi Zhang, Hairong Zhang

Acta Geophysica

|

2023

|

Vol. 71, no. 4

1705--1715

EN

The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments.

5

A Comparative Study of Various Edge Detection Techniques for Underwater Images

Awalludin Ezmahamrul Afreen, Arsad Tengku Noorfarahana T., Yussof Wan Nural Jawahir Hj Wan, Bachok Zainudin, Hitam Muhammad Suzuri

Journal of Telecommunications and Information Technology

|

2022

|

nr 1

23--33

EN

Nowadays, underwater image identification is a challenging task for many researchers focusing on various ap plications, such as tracking fish species, monitoring coral reef species, and counting marine species. Because underwater im ages frequently suffer from distortion and light attenuation, pre-processing steps are required in order to enhance their quality. In this paper, we used multiple edge detection techniques to determine the edges of the underwater images. The pictures were pre-processed with the use of specific techniques, such as enhancement processing, Wiener filtering, median filtering and thresholding. Coral reef pictures were used as a dataset of underwater images to test the efficiency of each edge detection method used in the experiment. All coral reef image datasets were captured using an underwater GoPro camera. The performance of each edge detection technique was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). The lowest MSE value and the highest PSNR value represent the best quality of underwater images. The results of the experiment showed that the Canny edge detection technique outperformed other approaches used in the course of the project.

6

An Enhanced Approach for Image Edge Detection Using Histogram Equalization (BBHE) and Bacterial Foraging Optimization (BFO)

Kumar Praveen, Jindal Tanvi, Raj Balwinder

International Journal of Electronics and Telecommunications

|

2022

|

Vol. 68. No. 4

875--880

EN

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures.

7

Image segmentation method of mine pass soil and ore based on the fusion of the confidence edge detection algorithm and mean shift algorithm

Jin Feng, Zhan Kai, Chen Shengjie, Huang Shuwei, Zhang Yuansheng

Gospodarka Surowcami Mineralnymi

|

2021

|

T. 37, z. 4

133--152

EN

In the execution of edge detection algorithms and clustering algorithms to segment image containing ore and soil, ore images with very similar textural features cannot be segmented effectively when the two algorithms are used alone. This paper proposes a novel image segmentation method based on the fusion of a confidence edge detection algorithm and a mean shift algorithm, which integrates image color, texture and spatial features. On the basis of the initial segmentation results obtained by the mean shift segmentation algorithm, the edge information of the image is extracted by using the edge detection algorithm based on the confidence degree, and the edge detection results are applied to the initial segmentation region results to optimize and merge the ore or pile belonging to the same region. The experimental results show that this method can successfully overcome the shortcomings of the respective algorithm and has a better segmentation results for the ore, which effectively solves the problem of over segmentation.

PL

W procesie algorytmu wykrywania krawędzi ufności i algorytmu grupowania do segmentacji obrazu zawierającego rudę i glebę, obraz rudy o bardzo podobnych cechach tekstury nie może być skutecznie segmentowany, gdy oba algorytmy są używane osobno. W pracy zaproponowano nowatorską metodę segmentacji obrazu opartą na połączeniu algorytmu wykrywania krawędzi ufności i algorytmu zmiany średniej, który integruje kolor, teksturę i cechy przestrzenne obrazu. Na podstawie wstępnych wyników segmentacji uzyskanych przez algorytm segmentacji zmiany średniej informacja o krawędziach oryginalnego obrazu jest wyodrębniana za pomocą algorytmu wykrywania krawędzi opartego na stopniu ufności, a otrzymane wyniki są stosowane do początkowych wyników segmentacji obszaru w celu optymalizacji i scalenia rudy lub gleby należących do tego samego obszaru. Wyniki eksperymentalne pokazują, że metoda ta może skutecznie przezwyciężyć wady odpowiedniego algorytmu i daje lepsze wyniki segmentacji dla rudy, co dobrze rozwiązuje problem nadmiernej segmentacji.

8

Multi-line signal change detection for image segmentation with application in the ceramic tile industry

Sušac Filip, Matić Tomislav, Aleksi Ivan, Keser Tomislav

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2021

|

Vol. 69, nr 3

art. no. e137121

EN

In the ceramic industry, quality control is performed using visual inspection in three different product stages: green, biscuit, and the final ceramic tile. To develop a real-time computer visual inspection system, the necessary step is successful tile segmentation from its background. In this paper, a new statistical multi-line signal change detection (MLSCD) segmentation method based on signal change detection (SCD) method is presented. Through experimental results on seven different ceramic tile image sets, MLSCD performance is analyzed and compared with the SCD method. Finally, recommended parameters are proposed for optimal performance of the MLSCD method.

9

Stress feld estimation by the geoid undulations of the Samos Kuşadası Bay and implications for seismogenic behavior

Oruç Bülent, Balkan Emir

Acta Geophysica

|

2021

|

Vol. 69, no. 3

1137--1149

EN

The strong earthquake with magnitude 6.9 occurred ofshore at the northernmost edge of the Samos Island and was strongly felt in the north Aegean islands and İzmir metropolitan city. In this study, the effective elastic thicknesses of the lithosphere and seismogenic layer thickness were correlated with each other in order to understand the nature of the earthquakes. We determined that the upper and lower depth limits of seismogenic layer are in a range of 5–15 km, meaning that only the upper crust is mostly involved in earthquakes in the study area. The fact that seismogenic layer and effective elastic thicknesses are close to each other indicates that the earthquake potential may be within the seismogenic layer. Following that, we estimate the stress feld from the geoid undulations as a proxy of gravity potential energy in order to analyze the amplitude and orientation of the stress vectors and seismogenic behavior implications. The discrete wavelet transform has been carried out to decompose the isostatic residual gravity anomalies into horizontal, vertical and diagonal detail coefcients. The results delineated edges of gravity anomalies that reveal some previously unknown features.

10

Canny edge detection based real-time intelligent parking management system

Trivedi Janak, Devi Mandalapu Sarada, Dhara Dave

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2020

|

z. 106

197--208

EN

Real-time traffic monitoring and parking are very important aspects for a better social and economic system. Python-based Intelligent Parking Management System (IPMS) module using a USB camera and a canny edge detection method was developed. The current situation of real-time parking slot was simultaneously checked, both online and via a mobile application, with a message of Parking “Available” or “Not available” for 10 parking slots. In addition, at the time entering in parking module, gate open and at the time of exit parking module, the gate closes automatically using servomotor and sensors. Results are displayed in figures with the proposed method flow chart.

11

Review of edge detection algorithms for application in miniature dimension measurement modules

Budzyn Grzegorz, Rzepka Janusz

Journal of Machine Engineering

|

2020

|

Vol. 20, No. 4

74--85

EN

Continuous improvement of semiconductor chip technology allows more and more complex functions to be performed by an increasing number of modules with limited space and power. The deep miniaturisation and cost reduction of such modules has a positive impact on their application areas. They can be used, for example, inside machine tools for dimensional inspection of workpieces or deformation of the tool tip. Modules of this type must contain as many standard components as possible and, in particular, a processing unit responsible, among other things, for processing the optical sensor signal. For this reason, this study reviews and compares algorithms for object edge detection useful in embedded systems based on standard single chips, cores based on Cortex-M4F. The complexity of processing and the quality of algorithm output data was analysed. The results of experimental studies are presented. It was found that the required processing times significantly reduce the use of single chip embedded systems only for edge detection on small images.

12

Detecting visual objects by edge crawling

Grycuk Rafał, Wojciechowski Adam, Wei Wei, Siwocha Agnieszka

Journal of Artificial Intelligence and Soft Computing Research

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2020

|

Vol. 10, No. 3

223--237

EN

Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

13

A new approach to detection of changes in multidimensional patterns

Gałkowski Tomasz, Krzyżak Adam, Filutowicz Zbigniew

Journal of Artificial Intelligence and Soft Computing Research

|

2020

|

Vol. 10, No. 2

125--136

EN

Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.

14

The applications of edge-detection image filtering in medical imaging and diagnosis

Jankowicz Bogdan

Geomatics, Landmanagement and Landscape

|

2020

|

no. 2

25--39

EN

This article presents examples of the use of image filtering for various types of diagnostic medical imaging in order to improve their interpretative value, and thus to improve the diagnostic reliability of that imaging. As research and visual tests have shown, in many cases, the use of digital image filtering makes it possible to significantly improve not only image quality, but also their readability or clarity, thus contributing to a more accurate and precise reading and interpretation of information contained in the images. The author proposed specific filters that largely meet the assumed conditions and constitute a supplement, and sometimes introduce a possible new application in addition to those already known in subject literature. A visual assessment was also made of the degree of diagnostic usefulness of images after filtration compared to the source images. The most commonly used filters are those that not only help to improve the overall appearance and quality of the image, but also, on the one hand, help to extract or highlight certain information, or to reduce noise, on the other hand. Thanks to these solutions, it is possible to smoothen or sharpen some structures within the images, which impacts their readability and quality. Thus, image filtering has become a very desirable and useful tool in many fields of science, technology, as well as art and medicine. The subject matter of image transformation is here applied to the latter discipline.

PL

W niniejszym artykule przedstawiono przykłady zastosowania filtracji dla różnych rodzajów medycznej diagnostyki obrazowej, celem polepszenia ich wartości interpretacyjnej, a tym samym – podniesienia wiarygodności diagnostycznej obrazu. Jak wykazały badania i testy wizualne - w wielu przypadkach, stosowanie filtracji obrazów cyfrowych umożliwia znaczne poprawienie nie tylko ich jakości, a co za tym idzie czytelności czy klarowności, przyczyniając się do bardziej trafnego i precyzyjnego odczytywania i interpretowania informacji znajdujących się na obrazach. Autor zaproponował określone filtry, które w znacznym stopniu spełniają założone warunki i stanowią uzupełnienie, a niekiedy są nową propozycją w stosunku do znanych z literatury. Dokonano również wizualnej oceny stopnia przydatności diagnostycznej obrazów po filtracji w porównaniu z obrazami źródłowymi. Najczęściej używanymi filtrami są te, które nie tylko pomagają poprawić ogólny wygląd i jakość zdjęcia, ale wyodrębniają bądź uwypuklają pewne informacje – z jednej strony lub redukują szumy – z drugiej. Dzięki nim można dokonać wygładzenia bądź wyostrzenia niektórych struktur na obrazie, co wpływa na ich czytelność i jakość . Tak więc filtracja stała się narzędziem bardzo pożądanym i przydatnym w wielu dziedzinach nauki, techniki , również sztuki i medycyny, której to dziedziny dotyczy przedstawiona tematyka transformacji obrazu.

15

Salt and pepper noise reduction and edge detection algorithm based on neutrosophic logic

Arulpandy P., Trinita Pricilla M.

Computer Science

|

2020

|

T. 21 (2)

193--209

EN

Noise reduction of images is a challenging task in image processing. Salt and pepper noise is one kind of noise that affects a gray-scale image significantly.Generally, the median filter is used to reduce salt and pepper noise; it gives optimum results while compared to other image filters. Median filter works only up to a certain level of noise intensity. Here we proposed a neighborhoodbased image filter called nbd-filter, it works perfectly for gray image regardless of noise intensity. It reduces salt and pepper noise significantly at any noise level and produces a noise-free image. Further, we proposed an edge detection algorithm based on the neutrosophic set, it detects edges efficiently for images corrupted by noise and noise-free images. Neutrosophic set (NS) is a powerful tool to deal with indeterminacy. Since most of the real-life images consists of indeterminate regions, Neutrosophy is a perfect tool for edge detection. In this paper, the neutrosophic set is applied to the image domain and a novel edge detection technique is proposed.

16

Detection of line objects by means of Gabor wavelets and Hough transform

Marmol U., Borowiec N.

Archives of Civil Engineering

|

2020

|

Vol. 66, nr 3

339--363

PL

Celem pracy jest zaproponowanie metody wykrywania krawędzi o ściśle określonym kierunku przebiegu na danych obrazowych i laserowych. Tradycyjne filtry wykrywają krawędzie we wszystkich kierunkach (np. filtr Canny), ewentualnie w trzech wybranych – horyzontalnym, wertykalnym lub diagonalnym (np. filtr Roberts). Często przedmiotem analiz są tylko określone obiekty liniowe jak linie energetyczne, tory, czy rurociągi. Mają one zazwyczaj ściśle określone kierunki przebiegu. Klasyczne filtry wykrywają oczywiście te informacje, ale także dużą ilość danych nadmiarowych, które utrudniają dalsze analizy. Problemem postawionym w pracy jest znalezienie takiego rozwiązania, które pozwoliłoby na wyznaczenie krawędzi tylko i wyłącznie o ściśle określonym kierunku, odpowiadających za przebieg konkretnych obiektów takich jak tory kolejowe, rurociągi czy linie energetyczne. Problem badawczy skupiał się w pierwszym etapie na określeniu przybliżonej lokalizacji wyłącznie analizowanych obiektów, a w kolejnym kroku na poprawnej i dokładnej ich detekcji. Pierwszy etap został przeprowadzony z wykorzystaniem filtrów Gabora, drugi - z użyciem transformaty Hougha. Testy zostały wykonane zarówno dla danych laserowych jak i danych obrazowych w postaci ortofotomapy. W obydwu przypadkach uzyskano dobre rezultaty dla obydwóch etapów: przybliżonej lokalizacji i precyzyjnej detekcji.

EN

This article presents a method for detecting linear objects with a defined direction based on image and lidar data. It was decided to use Gabor waves for this purpose. The Gabor wavelet is a sinusoid modulated by the Gauss function. The orientation angle of the sinusoid means that the waveform can only operate in strictly defined directions. It should, therefore, provide an appropriate solution to the problem posed by the publication. The research problem focused in the first stage on determining the approximate location of only the analysed objects, and in the next step on correct and accurate detection. The first stage was carried out using Gabor filters, the second - using the Hough transform. The tests were performed for both laser data and image data. In both cases, good results were obtained for both stages: approximate location and precise detection.

17

Mapping shoreline change using machine learning: a case study from the eastern Indian coast

Kumar Lalit, Afzal Mohammad Saud, Afzal Mohammad Mashhood

Acta Geophysica

|

2020

|

Vol. 68, no. 4

1127--1143

EN

The continuous shift of shoreline boundaries due to natural or anthropogenic events has created the necessity to monitor the shoreline boundaries regularly. This study investigates the perspective of implementing artifcial intelligence techniques to model and predict the realignment in shoreline along the eastern Indian coast of Orissa (now called Odisha). The modeling consists of analyzing the satellite images and corresponding reanalysis data of the coastline. The satellite images (Landsat imagery) of the Orissa coastline were analyzed using edge detection flters, mainly Sobel and Canny. Sobel and canny flters use edge detection techniques to extract essential information from satellite images. Edge detection reduces the volume of data and flters out worthless information while securing signifcant structural features of satellite images. The image diferencing technique is used to determine the shoreline shift from GIS images (Landsat imagery). The shoreline shift dataset obtained from the GIS image is used together with the metrological dataset extracted from Modern-Era Retrospective analysis for Research and Applications, Version 2, and tide and wave parameter obtained from the European Centre for Medium-Range Weather Forecast for the period 1985–2015, as input parameter in machine learning (ML) algorithms to predict the shoreline shift. Artifcial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) algorithm are used as a ML model in the present study. The ML model contains weights that are multiplied with relevant inputs/features to obtain a better prediction. The analysis shows wind speed and wave height are the most prominent features in shoreline shift prediction. The model’s performance was compared, and the observed result suggests that the ANN model outperforms the KNN and SVM model with an accuracy of 86.2%.

18

Creating see-around scenes using panorama stitching

Alferidah Saja, Alkhaldi Nora

Annals of Computer Science and Information Systems

|

2019

|

Vol. 18

293-–301

EN

Image stitching refers to the process of combining multiple images of the same scene to produce a single high-resolution image, known as panorama stitching. The aim of this paper is to produce a high-quality stitched panorama image with less computation time. This is achieved by proposing four combinations of algorithms. First combination includes FAST corner detector, Brute Force K-Nearest Neighbor (KNN) and Random Sample Consensus (RANSAC). Second combination includes FAST, Brute Force (KNN) and Progressive Sample Consensus (PROSAC). Third combination includes ORB, Brute Force (KNN) and RANSAC. Fourth combination contains ORB, Brute Force (KNN) and PROSAC. Next, each combination involves a calculation of Transformation Matrix. The results demonstrated that the fourth combination produced a panoramic image with the highest performance and better quality compared to other combinations. The processing time is reduced by 67% for the third combination and by 68% for the fourth combination compared to stat-of-the-art.

19

Implementacja w FPGA algorytmu detekcji krawędzi obrazu w czasie rzeczywistym

Kowalski P., Smyk R.

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

|

2018

|

Nr 61

49--52

PL

W artykule przedstawiono projekt architektury oraz implementację układową toru przetwarzania wstępnego obrazu z modułem detekcji krawędzi. Układ został zaimplementowany w FPGA Intel Cyclone. Zrealizowany moduł wykorzystuje pięć wybranych algorytmów wykrywania krawędzi, w tym Robertsa, Sobela i Prewitt.

EN

The paper presents FPGA implementation details of the hardware image processing block with the edge detection module. In the implemented video processing module we use five selected edge detection algorithms, including Roberts, Sobel and Prewitt. The structure was synthesized and packed using hardware design platform built around the Intel Cyclone V FPGA. The number of logic elements used in each implementation was compared. We also estimated the execution time and maximum possible frame rate in VGA (640x480) and FullHD (1920x1080) video stream.

20

Fast edge detection approach based on global optimization convex model and Split Bregman algorithm

Jing Y., Liu J., Liu Z., Cao H.

Diagnostyka

|

2018

|

Vol. 19, No. 2

23--29

EN

Active contour model is a typical and effective closed edge detection algorithm, which has been widely applied in remote sensing image processing. Since the variety of the image data source, the complexity of the application background and the limitations of edge detection, the robustness and universality of active contour model are greatly reduced in the practical application of edge extraction. This study presented a fast edge detection approach based on global optimization convex model and Split Bregman algorithm. Firstly, the proposed approach defined a generalized convex function variational model which incorporated the RSF model’s principle and Chan’s global optimization idea and could get the global optimal solution. Secondly, a fast numerical minimization scheme based on split Bregman iterative algorithm is employed for overcoming drawbacks of noise and others. Finally, the curve evolves to the target boundaries quickly and accurately. The approach was applied in real special sea ice SAR images and synthetic images with noise, fuzzy boundaries and intensity inhomogeneity, and the experiment results showed that the proposed approach had a better performance than the edge detection methods based on the GMAC model and RSF model. The validity and robustness of the proposed approach were also verified.