scholarly journals Hyperanalytic Wavelet-Based Robust Edge Detection

2021 ◽  
Vol 13 (15) ◽  
pp. 2888
Author(s):  
Alexandru Isar ◽  
Corina Nafornita ◽  
Georgiana Magu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Speckle Noise ◽  
Transform Domain ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gradient Based ◽  
New Type ◽  
Edge Detection Method

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

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.

An Edge Detection Method Based on Adjacent Dispersion

2016 ◽  
Vol 30 (10) ◽  
pp. 1655026
Author(s):  
Qindong Sun ◽  
Yimin Qiao ◽  
Hua Wu ◽  
Jiamin Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Detection Method ◽  
Superior Performance ◽  
Gray Level ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gray Level Image ◽  
Novel Method ◽  
Edge Detection Method

Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.

scholarly journals Analisis Perbandingan Kinerja Metode Superpixel dan Gradien berbasis Edge Detector pada Pendeteksian Objek Bergerak

2020 ◽  
Vol 8 (2) ◽  
pp. 362
Author(s):  
MUHAMMAD KHAERUL NAIM MURSALIM ◽  
IHSAN VERDIAN
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Edge Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Edge Detector ◽  
Segmentation Process ◽  
Gradient Based ◽  
Edge Detection Method

ABSTRAKSalah satu bagian dalam algoritma pemrosesan citra adalah proses segmentasi yang menjadi tahap pra-pemrosesan untuk ekstraksi fitur objek. Superpixel menjadi salah satu solusi pada proses segmentasi dengan mendefenisikan kumpulan piksel yang mempunyai kesamaan karekterisitik sehingga membawa banyak informasi mengenai fitur objek. Adapun tantangan yang dihadapi dalam mendeteksi objek bergerak adalah ketidakmampuan untuk memisahkan objek bergerak dari background objek. Sehingga, pada citra yang dideteksi akan dikelilingi oleh wilayah yang terdapat derau. Pada penelitian ini, diusulkan metode superpixel berbasis deteksi tepi untuk mendeteksi objek bergerak. Selanjutnya, kinerja metode superpixel diuji dengan membandingkan dengan metode deteksi tepi yang berbasis gradient. Hasilnya menunjukkan bahwa metode yang diusulkan mampu meminimalisir derau lebih baik dan hasil perhitungan MSE, RMSE, dan PSNR hanya berbeda 0.06% dan 0.1% dari metode Sobel dan Prewitt.Kata kunci: Deteksi tepi, Objek bergerak, Proses Segmentasi, Superpixel ABSTRACTOne part of the image processing is the segmentation which becomes the preprocessing stage for feature extraction. Superpixel becomes solutions in the segmentation process by defining a collection of pixels that have the same characteristics ang bringing the information about the object's features. The challenge faced in detecting moving objects is the inability to separate moving objects from the object's background. Thus, the detected image will be surrounded by an area with noise. In this study, a superpixel-based edge detection method is proposed to detect moving objects. Furthermore, the performance of the superpixel method is tested by comparing it to the gradient-based edge detection method. The results show that the proposed method is able to minimize noise better and the results of MSE, RMSE, and PSNR calculations differ only 0.06% and 0.1% from the Sobel and Prewitt methods.Keywords: Edge detection, Moving objects, Segmentation, Superpixels

Edge Detection of Color Images Based on Improved Morphological Gradient Operators

2014 ◽  
Vol 511-512 ◽  
pp. 550-553 ◽  
Author(s):  
Jian Yong Liang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Color Image ◽  
Color Images ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Gradient Based ◽  
Image Edge ◽  
Edge Detection Method ◽  
Traditional Approaches

Edge detection is an old and hot topic in image processing, pattern recognition and computer vision. Numerous edge detection approaches have been proposed to gray images. It is difficult to extend these approaches to color image edge detection. A novel edge detection method based on mathematical morphology for color images is proposed in this paper. The proposed approach firstly compute vector gradient based on morphological gradient operators, and then compute the optimal gradient according to structure elements with different size. Finally, we use a threshold to binary the gradient images and then obtain the edge images. Experimental results show that the proposed approach has advantages of suppressing noise and preserving edge details and it is not sensitive to noise pixel. The finally edge images via the proposed method have high PSNR and NC compared with the traditional approaches.

WAVELET-BASED EDGE DETECTION IN DIGITAL IMAGES

2008 ◽  
Vol 08 (04) ◽  
pp. 513-533 ◽  
Author(s):  
MUHAMMAD HUSSAIN ◽  
TURGHUNJAN ABDUKIRIM ◽  
YOSHIHIRO OKADA
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Local Maxima ◽  
Wavelet Filters ◽  
Dyadic Wavelet ◽  
Edge Detectors ◽  
Frame Work ◽  
Dyadic Wavelet Transform ◽  
Edge Detection Method ◽  
Better Than

This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors.

scholarly journals A Novel Edge Detection Method Based on the Regularized Laplacian Operation

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 697
Author(s):  
Huilin Xu ◽  
Yuhui Xiao
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Regularization Parameter ◽  
Image Data ◽  
Detection Algorithm ◽  
Edge Detector ◽  
Rotation Symmetry ◽  
Edge Detection Algorithm ◽  
Choice Strategy ◽  
Edge Detection Method

In this paper, an edge detection method based on the regularized Laplacian operation is given. The Laplacian operation has been used extensively as a second-order edge detector due to its variable separability and rotation symmetry. Since the image data might contain some noises inevitably, regularization methods should be introduced to overcome the instability of Laplacian operation. By rewriting the Laplacian operation as an integral equation of the first kind, a regularization based on partial differential equation (PDE) can be used to compute the Laplacian operation approximately. We first propose a novel edge detection algorithm based on the regularized Laplacian operation. Considering the importance of the regularization parameter, an unsupervised choice strategy of the regularization parameter is introduced subsequently. Finally, the validity of the proposed edge detection algorithm is shown by some comparison experiments.

Macrozooplankton Image Edge Detection Using Wavelet Packet Decomposition

2010 ◽  
Vol 108-111 ◽  
pp. 44-49
Author(s):  
Jing Ying Zhao ◽  
Hai Guo ◽  
Xing Bin Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Wavelet Packet ◽  
Detection Methods ◽  
Automatic Identification ◽  
Wavelet Packet Decomposition ◽  
Edge Detectors ◽  
Reconstruction Image ◽  
Image Edge ◽  
Edge Detection Method

Comparing with the phytoplankton, there are few researches on zooplanktons. Now, many waterworks don’t monitor the zooplanktons in source water. There isn’t effective detection method for several common macro zooplanktons such as chironomid larvae, cyclops and so on, and little has been done in the field of the macro zooplanktons automatic identification and monitor. This paper puts for forward a macrozooplankton edge detection method based on wavelet packet decomposition and reconstruction. We erase the high frequency parts by applying wavelet packet decomposition in the original images and then detect the edge of reconstruction images using the common edge detectors such as Prewitt, Sobel, Roberts, Laplacian of Gaussion, Canny and so on. The experimental results show that the edge detection methods in the reconstruction image work better than in the original image.

scholarly journals Implementation of Xor and Edge Identification Method in Steganography

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2066-2070
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Cover Image ◽  
Secret Message ◽  
Transform Domain ◽  
Good Image Quality ◽  
Before And After ◽  
Sharp Edges ◽  
Edge Detection Method ◽  
Integer Wavelet

In this paper, a novel imagesteganography algorithm that combines the strengths of edge detection and XOR coding, to conceal a secret message either in the spatial domain or an Integer Wavelet Transform (IWT) based transform domain of the cover image is presented. Edge detection and XOR coding are used in to conceal the secret message. Edge detection enables the identification of sharp edges in the cover image and this when embedding results in good image quality. Edge detection method presented here is capable of estimating the exact edge intensities for both the cover and stegno images (before and after embedding the message), which is essential when extracting the message. The XOR coding, on the other hand, is a simple, yet effective, process that helps in reducing differences between the cover and stegno images. Experimental results are observed using XILINX ISE and demonstrated that the proposed method has achieved better imperceptibility results than other popular steganography methods.

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