Automic Edge Detection in Noisy Fringe Patterns: A Practical Approach

2012 ◽  
Author(s):  
Mani Maran Ratnam
Keyword(s):  
Edge Detection ◽  
Threshold Value ◽  
Practical Approach ◽  
Fringe Projection ◽  
Edge Detector ◽  
Noise Levels ◽  
Detection Techniques ◽  
Real Objects ◽  
Fringe Patterns ◽  
Binary Edge

Corak-corak pinggir biasanya dikaji dengan menggunakan tingkap yang ditakrifkan awal kerana kesukaran mencari sempadannya. Hal ini berlaku kerana pinggir-pinggir tersebut biasanya mempunyai keamatan minimum yang serupa dengan keamatan latar belakang dan adalah sukar untuk menentukan di mana sesuatu pinggir bermula dan berakhir. Kehadiran pinggir-pinggir boleh menyebabkan ralat dalam pengesanan sempadan dengan menggunakan kaedah-kaedah pengesanan sempadan yang sedia ada. Kertas kerja ini membentangkan suatu pendekatan praktik ke arah pengesanan automatik sempadan corak pinggir berhingar dengan paras hingar yang berbeza. Teknik tersebut menggunakan operasi-operasi morfologi pengembangan dan penghakisan serta pengesan sempadan binari kejiranan mudah. Nilai ambang yang diperlukan dalam operasi binarisasi ditentukan daripada hubungkait linear antara nilai ambang dengan sisihan piawai hingar Gaussian daripada kajian simulasi. Teknik tersebut kemudian dilaksanakan dengan jayanya pada corak-corak pinggir pada beberapa objek sebenar yang didapati melalui kaedah pengunjuran pinggir. Beberapa kelemahan kaedah tersebut dibincangkan. Kata kunci: Pengesanan sempadan, corak pinggir Fringe patterns are usually analyzed using a predefined window due to the difficulty associated with finding their edges. This is because, the fringes usually have minimum intensities similar to that of the background and it is difficult to determine where a fringe starts or ends. The presence of the fringes can therefore lead to errors in detecting the edges of the object when using existing edge detection techniques. This paper presents a practical approach towards the automatic detection of the edges of a noisy fringe pattern having different noise levels. The technique uses the dilation and erosion morphological operators combined with a simple neighborhood binary edge detector. The threshold required for the binarization operation is determined from a linear relationship between the threshold value and the standard deviation of Gaussian noise from a simulation study. The proposed technique has been successfully applied to fringe patterns on real objects obtained using the fringe projection method. Some of the limitations of the technique are discussed. Key words: Edge detection; fringe patterns

scholarly journals Comparison of edge detection techniques for M7 subtype Leukemic cell in terms of noise filters and threshold value

2017 ◽  
Vol 162 ◽  
pp. 01074
Author(s):  
Afifah Salmi Abdul Salam ◽  
Mohd. Nazrin Md. Isa ◽  
Muhammad Imran Ahmad ◽  
Rizalafande Che Ismail
Keyword(s):  
Edge Detection ◽  
Leukemic Cell ◽  
Threshold Value ◽  
Detection Techniques ◽  
Noise Filters

scholarly journals M7 subtype leukemic cell edge detection techniques with threshold value comparison and noise filters

2019 ◽  
Vol 13 (3) ◽  
pp. 1294
Author(s):  
A.S.A. Salam ◽  
M.N.M. Isa ◽  
M.I. Ahmad
Keyword(s):  
Edge Detection ◽  
Leukemic Cell ◽  
Threshold Value ◽  
Acute Myelocytic Leukemia ◽  
Threshold Values ◽  
Detection Techniques ◽  
Myelocytic Leukemia ◽  
Edge Detectors ◽  
Canny Edge ◽  
Noise Filters

The aim of this paper is to study and identify various threshold values for two prevalently used edge detection techniques, which are Sobel and Canny. The purpose is to determine which value gives an accurate result for identifying a leukemic cell. Moreover, evaluating suitability of edge detectors is also essential as feature extraction of cell depends greatly on image segmentation (edge detection). Firstly, an image of M7 subtype of Acute Myelocytic Leukemia (AML) is selected due to its diagnosing which were found lacking. Next, apply noise filters for the best of image quality. Thus by comparing image with no filter, median and average filters, useful information can be acquired. Each edge detectors is fixed with threshold value of 0-0.5 but for Cann edge detection the value can increase until 0.9. From the research, it is found that Canny edge with no filter and a threshold value of 0.7 gives a clearer image with less noise reduction.

scholarly journals EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

2019 ◽  
Vol 17 (3) ◽  
pp. 333 ◽  
Author(s):  
Milan Pavlović ◽  
Vlastimir Nikolić ◽  
Miloš Simonović ◽  
Vladimir Mitrović ◽  
Ivan Ćirić
Keyword(s):  
Genetic Algorithm ◽  
Edge Detection ◽  
Threshold Value ◽  
High Threshold ◽  
Threshold Values ◽  
Detection Error ◽  
Edge Detector ◽  
Rail Track ◽  
Canny Edge

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector.

scholarly journals Single-shot detection of 8 unique monochrome fringe patterns representing 4 distinct directions via multispectral fringe projection profilometry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Parsa Omidi ◽  
Mohamadreza Najiminaini ◽  
Mamadou Diop ◽  
Jeffrey J. L. Carson
Keyword(s):  
Spatial Resolution ◽  
Fringe Pattern ◽  
Three Dimensional ◽  
Fringe Projection ◽  
Video Frame ◽  
Single Shot ◽  
Shot Detection ◽  
Fringe Patterns ◽  
Fringe Projection Profilometry ◽  
Multispectral Camera

AbstractSpatial resolution in three-dimensional fringe projection profilometry is determined in large part by the number and spacing of fringes projected onto an object. Due to the intensity-based nature of fringe projection profilometry, fringe patterns must be generated in succession, which is time-consuming. As a result, the surface features of highly dynamic objects are difficult to measure. Here, we introduce multispectral fringe projection profilometry, a novel method that utilizes multispectral illumination to project a multispectral fringe pattern onto an object combined with a multispectral camera to detect the deformation of the fringe patterns due to the object. The multispectral camera enables the detection of 8 unique monochrome fringe patterns representing 4 distinct directions in a single snapshot. Furthermore, for each direction, the camera detects two π-phase shifted fringe patterns. Each pair of fringe patterns can be differenced to generate a differential fringe pattern that corrects for illumination offsets and mitigates the effects of glare from highly reflective surfaces. The new multispectral method solves many practical problems related to conventional fringe projection profilometry and doubles the effective spatial resolution. The method is suitable for high-quality fast 3D profilometry at video frame rates.

scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

scholarly journals SURVEY ON DISTRIBUTED CANNY EDGE DETECTOR WITH FPGA

2015 ◽  
Vol 3 (3) ◽  
pp. 51-61
Author(s):  
Poonam S. Deokar ◽  
Anagha P. Khedkar
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Large Scale ◽  
Attractive Alternative ◽  
Edge Detector ◽  
Canny Edge Detection ◽  
Canny Edge Detector ◽  
Canny Edge ◽  
Canny Algorithm ◽  
Real Time Applications

The Edge can be defined as discontinuities in image intensity from one pixel to another. Modem image processing applications demonstrate an increasing demand for computational power and memories space. Typically, edge detection algorithms are implemented using software. With advances in Very Large Scale Integration (VLSI) technology, their hardware implementation has become an attractive alternative, especially for real-time applications. The Canny algorithm computes the higher and lower thresholds for edge detection based on the entire image statistics, which prevents the processing of blocks independent of each other. Direct implementation of the canny algorithm has high latency and cannot be employed in real-time applications. To overcome these, an adaptive threshold selection algorithm may be used, which computes the high and low threshold for each block based on the type of block and the local distribution of pixel gradients in the block. Distributed Canny Edge Detection using FPGA reduces the latency significantly; also this allows the canny edge detector to be pipelined very easily. The canny edge detection technique is discussed in this paper.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

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