A step edge detector based on bilinear transformation

2021 ◽  
pp. 1-12
Author(s):  
Koorosh Dabighi ◽  
Akbar Nazari ◽  
Saeid Saryazdi
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Quantitative Comparison ◽  
Discrete Version ◽  
Edge Detector ◽  
Bilinear Transformation ◽  
Proposed Model ◽  
Edge Detectors ◽  
Canny Edge ◽  
Error Metric

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.

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.

scholarly journals Morphological Edge Detection Algorithms on the Noisy Car Image Database

2020 ◽  
Vol 6 (3) ◽  
pp. 328
Author(s):  
Nasa Zata Dina
Keyword(s):  
Law Enforcement ◽  
Early Detection ◽  
Edge Detection ◽  
Mathematical Morphology ◽  
Morphological Operation ◽  
Edge Detector ◽  
Traffic Law ◽  
Canny Edge ◽  
Traffic Law Enforcement ◽  
Two Populations

Development of edge detector using mathematical morphology can provide remarkably more precise edge detection. This system mainly focuses on edge detection of cars. This paper uses car database for the early detection in automatic traffic law enforcement. The methodology for accurate edge detector includes grayscale image conversion, median filter, element structures formation, mathematical morphology, synthetic weighted and segmentation using Otsu method. The morphological operation that was used in this study is basic operation such as dilation and erosion. The use of morphological operation was to find the limits and the image terminals by selecting different size and extensions converted to images of grey levels and the results were compared to determine the most suitable and better methods. In this study, 371 car databases and 100 objects from the publicly available Berkeley and Greek car databases were used as data. This methodology is proved highly accurate 0.7-1.58% higher than Canny edge detector. The accuracy of the results from Mathematical Morphlogy Edge Detection were 83.23%; 83.27%; 82.66%; 79.78% while the results from Canny Edge Detection were 81.90%; 82.08%; 81.08%; 79.71%. In comparison with the results from Canny edge detector. It shows that mathematical morphology renders better overall performance. It was also tested with salt and pepper noises and still shows better results. Z-test was used for comparing the means of two populations while F-test was used to test if two population variances are equal. Both tests were done because in this study two populations were used as main datasets. The effectiveness and robustness make this mathematical morphology method a suitable tool to be integrated into complete pre-screening systems for the early detection in automatic traffic law enforcement.

scholarly journals Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 165 ◽  
Author(s):  
Krishnamurthy V. Vemuru
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Image Classification ◽  
Spiking Neural Network ◽  
Edge Detector ◽  
Time Step ◽  
Biologically Inspired ◽  
Total Exposure Time ◽  
Canny Edge ◽  
Low Exposure

We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 < t < T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.

scholarly journals The Edge Detectors Suitable for Retinal OCT Image Segmentation

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Su Luo ◽  
Jing Yang ◽  
Qian Gao ◽  
Sheng Zhou ◽  
Chang’an A. Zhan
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Thickness Measurement ◽  
Retinal Layer ◽  
Detection Algorithms ◽  
Edge Detectors ◽  
Canny Edge ◽  
Segmentation Task ◽  
Effective Use ◽  
Oct Image

Retinal layer thickness measurement offers important information for reliable diagnosis of retinal diseases and for the evaluation of disease development and medical treatment responses. This task critically depends on the accurate edge detection of the retinal layers in OCT images. Here, we intended to search for the most suitable edge detectors for the retinal OCT image segmentation task. The three most promising edge detection algorithms were identified in the related literature: Canny edge detector, the two-pass method, and the EdgeFlow technique. The quantitative evaluation results show that the two-pass method outperforms consistently the Canny detector and the EdgeFlow technique in delineating the retinal layer boundaries in the OCT images. In addition, the mean localization deviation metrics show that the two-pass method caused the smallest edge shifting problem. These findings suggest that the two-pass method is the best among the three algorithms for detecting retinal layer boundaries. The overall better performance of Canny and two-pass methods over EdgeFlow technique implies that the OCT images contain more intensity gradient information than texture changes along the retinal layer boundaries. The results will guide our future efforts in the quantitative analysis of retinal OCT images for the effective use of OCT technologies in the field of ophthalmology.

A Novel Edge Detection Algorithm Based on Texture Feature Coding

2015 ◽  
Vol 24 (2) ◽  
pp. 235-248 ◽  
Author(s):  
Abdulkadir Sengur ◽  
Yanhui Guo ◽  
Mehmet Ustundag ◽  
Ömer Faruk Alcin
Keyword(s):  
Edge Detection ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Input Image ◽  
Edge Detector ◽  
Edge Image ◽  
Hardware System ◽  
Analysis Scheme ◽  
Edge Detectors ◽  
Field Programmable

AbstractA new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector.

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.

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