Multilevel nonlinear filters for edge detection and noise suppression

1994 ◽  
Vol 42 (2) ◽  
pp. 249-258 ◽  
Author(s):  
Humor Hwang ◽  
R.A. Haddad
Keyword(s):  
Edge Detection ◽  
Noise Suppression ◽  
Nonlinear Filters

Application of nonlinear filters to edge detection

1992 ◽  
Author(s):  
Charles G. Boncelet, Jr. ◽  
Russell C. Hardie
Keyword(s):  
Edge Detection ◽  
Nonlinear Filters

scholarly journals Peningkatan Performa Prediksi Daerah Potensi Penangkapan Ikan Dengan Metode Threshold Adaptif

2020 ◽  
Vol 4 (1) ◽  
pp. 48-64
Author(s):  
Ridla Kumara Hadi ◽  
Rudy Hartanto ◽  
Silmi Fauziati
Keyword(s):  
Edge Detection ◽  
Noise Suppression ◽  
Single Image ◽  
Thermal Front ◽  
Image Edge Detection ◽  
Image Edge

Metode yang digunakan untuk penentuan thermal fronts adalah algoritme Single Image Edge Detection dengan threshold statis 0,5 yang didapatkan dari penelitian terdahulu. Kekurangan dari metode threshold statis adalah tingginya bias akurasi hasil deteksi dikarenakan lebih banyaknya hasil deteksi negatif tervalidasi dibandingkan deteksi front murni yang tervalidasi. Penelitian yang diusulkan bertujuan untuk meningkatkan performa metode deteksi daerah potensi ikan. Peningkatan performa deteksi thermal front dapat dilakukan dengan mencari nilai threshold optimal yang sesuai untuk masing-masing citra. Threshold adaptif didapatkan dari hasil analisis histogram pada setiap citra greyscale yang diproses. Untuk mendapatkan nilai threshold optimal dipilih Algoritme Otsu dengan pertimbangan proses cepat dan ketepatan hasil menengah. Penyesuaian metode dibutuhkan karena sifat dasar data SST yang dikonversi menjadi raster. Modifikasi metode Otsu dilakukan pada perhitungan nilai threshold optimal dengan rentang intensitas greyscale 1-254. Pemurnian front menggunakan pendekatan Geodesic Buffering dengan jarak maksimal 10 kilometer untuk mengatasi pergeseran front akibat noise suppression. Penelitian telah dilakukan dan menghasilkan metode deteksi daerah potensi ikan dengan performa recall yang lebih tinggi 25,42% dibandingkan metode threshold statis. Nilai recall lebih tinggi membuktikan bahwa metode yang diusulkan mampu menghasilkan lebih banyak hasil deteksi front murni yang lokasinya tervalidasi dengan data aktual penangkapan ikan.

scholarly journals On High-precision Subpixel-based Drilling Technique for Suture Needles with Thread

2014 ◽  
Vol 8 (1) ◽  
pp. 607-612
Author(s):  
Tiebo Sun ◽  
Hong Li
Keyword(s):  
Edge Detection ◽  
High Precision ◽  
Noise Suppression ◽  
Hole Drilling ◽  
Drilling Process ◽  
Structural Elements ◽  
Spatial Moments ◽  
Automated Production ◽  
Fitting Method ◽  
Drilling Method

In order to improve the automation of end-hole drilling process in the production of suture needles with thread, a high-precision subpixel-based drilling method is proposed. According to the edge detection principle in mathematical morphology, combined with the characteristics of the magnified images of the ends of suture needles to be drilled, the morphological edge detection operators with variable structural elements are constructed to achieve noise suppression and fully extract the detailed information of edges of images of needle end holes to be drilled. Then, the subdivision method of spatial moments is adopted to realize the subpixel positioning of pixel-level edges. Finally, least squares fitting method is used to achieve the high-precision positioning of center of needle end hole to be drilled. The experimental results of the 0.5 mm needle samples show that the drilling method proposed in this study has a concentricity error no more than ± 0.2 μm and an average drilling time of 0.65S. Moreover, the method also boasts good real-time performance and stability and meets the automated production needs of drilling process of suture needles with thread.

scholarly journals FPGA based Edge Detection using Sobel Filter

2021 ◽  
Vol 9 (VII) ◽  
pp. 145-147
Author(s):  
Gabbar Jadhav
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Noise Suppression ◽  
Satellite Image ◽  
Differentiation Operator ◽  
Considerable Extent ◽  
Minimal Amount ◽  
Gradient Vector ◽  
Sobel Operator ◽  
Correct Form

In image processing, Sobel operator is utilised especially inside algorithms of edge-detection. It is a discreet differentiation operator which calculates the gradient approximation of the function picture intensity. The outcome of the Sobel operation at each location of the image is either the appropriate gradient vector or the vector standard. The Sobel operator relies on the image being converted into horizontal and vertical with a tiny, separable and integrated valued filter. This means that the computation is quite inexpensive. PAN Poanta satellite image was used for this work using Java, Core Java in GDAL package. As compared to in built Sobel operator, the image generated for this work is very fine and sharp as a result of noise suppression to a considerable extent. Inorder to do edge detection efficiently with minimal amount of false results, a correct form of Sobel filter ( I’=√(I*X)²+(I*Y)2 ) was used instead of the approximation(I’=I*X+I*Y) for the sake of computation.

Nondestructive Testing of Blockboard Using Combination of Wavelet Transform and Gray-Scale Mathematical Morphology

2011 ◽  
Vol 55-57 ◽  
pp. 1206-1210
Author(s):  
Nian Xue Yang ◽  
Da Wei Qi
Keyword(s):  
Wavelet Transform ◽  
Nondestructive Testing ◽  
Edge Detection ◽  
Mathematical Morphology ◽  
Noise Suppression ◽  
Testing System ◽  
Gray Scale ◽  
X Ray ◽  
Testing Method ◽  
Nondestructive Testing Method

This paper presents a scheme for blockboard nondestructive detection. X-ray nondestructive testing system has been used to obtain blockboard x-ray images. In order to recognize defects of blockboard effectively through x-ray images, an image processing method based on wavelet transform and gray-scale mathematical morphology is proposed. Firstly, image noises are reduced by wavelet soft threshold de-noising, and then a new modified mathematical morphology edge detection operator is used to detect edges of the de-noised image. The experiment results show that the method performs well in noise-suppression and edge detection. The proposed nondestructive testing method of blockboard is practical and easy to implement.

An Edge Detection Algorithm for Image Corrupted by Impulse Noise

2014 ◽  
Vol 678 ◽  
pp. 143-146
Author(s):  
De Hai Shen ◽  
Xu E ◽  
Long Chang Zhang
Keyword(s):  
Edge Detection ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Detection Efficiency ◽  
Median Filter ◽  
Vertical Direction ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Noise Point ◽  
Gradient Calculation

In order to improve the edge detection efficiency, and decrease the impulse noise impact on the edge detection, a new edge detection algorithm for impulse noise image is proposed. The algorithm combined the median filter idea, and used cross convolution template to calculate image gradients of horizontal and vertical direction. First, detected the point that will attend gradient calculation by threshold, for the noise point, its value will be replaced by median of those points associated with it in detection window. For non-noise point, we directly calculate gradient it initial pixel values. Experiments show that, the new algorithm has strong suppressing noise ability for images corrupted by impulse noise, the extracted image edge is also fine, the outline is clear, the algorithm is better than the traditional Sobel algorithm and the wavelet transform algorithm in noise suppression performance and the detection efficiency, it has strong practicability.

scholarly journals An Advanced Neural Network based Method for Noise Removal and Edge Detection

2013 ◽  
Vol 7 (2) ◽  
pp. 1084-1089
Author(s):  
Baljit Kaur ◽  
Vijay Dhir
Keyword(s):  
Edge Detection ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Noise Removal ◽  
Processing Application ◽  
Noisy Images ◽  
Processing Step ◽  
Exact Difference ◽  
The Comparative Study

Edge detection is an important pre-processing step for any image processing application, object recognition and emotion detection. Edge detection is very helpful in case of noise free images. But in case of noisy images it is a challenging task.Noisy images are corrupted images. Their parameters are difficult to analyze and detect. In this research work different filters are used for the filtration of the image and to analyze that what exact difference it makes when it comes to detect t he edge of the image. It includes the comparative study of various image denoising filters. These Filters are then applied withBFO Algorithm and they are compared with one another which help to calculate the parameters of noisy images. The comparison parameters which have been taken into contrast are Peak Signal to Noise Ratio, Mean Square Error and Noise Suppression Rate.

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