Edge Detection In Digital Images Research Articles

An Asynchronous Spiking Neural Membrane System for Edge Detection.

Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection.

Retracted: Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

Retracted: Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

Cobble edge detection algorithm based on digital image processing

Cobble size distribution (CSD) on the surface of a cobble bed is an important part of the river system, with important implications for river ecology and geomorphology. A digital image edge detection algorithm based on markers and detection lines is proposed to realize automatic statistics gathering of CSD. By making multiple detection lines outward from the markers, the cobble edge detection process of 2D images is transformed into a 1D space for analysis and processing. To correct the error detection points, an edge correction algorithm based on mutation point detection is designed. Simultaneously, a cobble edge reconstruction algorithm is proposed to restore covered cobble edges. The results of a verification experiment and comparative analysis indicate that the relative error of D50, which was estimated by the algorithm in our study, is 3% in the laboratory and 8% in the field. The proposed method is reliable for the estimation of CSD with grain size range of 5 to 50 cm. In conclusion, the proposed algorithm is suitable for river geomorphology research and physical habitat studies of cobble floodplains using an unmanned aerial vehicle for remote sensing photography.

Retracted] Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

With the rapid development of the national economy, the film industry has risen rapidly under these conditions, and the development of film occupies a more important position in this process, which has led to the development of investment in the film field, an internationally recognized cultural space. This paper studies the commercial investment in the film field based on the edge detection of the digital image of the visual sensor. The purpose of the research is to point out the direction of commercial investment in the film industry by studying the effect of the edge detection of the digital image of the visual sensor on the application of the film object. The image edge detection algorithm based on wavelet transform and morphology and the application of these image edge detection algorithms such as wavelet theory analyze the shortcomings of movie, sound effects, and pictures and make a comparison before and after the improvement. Audiences, as the direct enjoyers of the cultural product of the film, their opinions, and evaluations of the film, largely determine the quality of the work. On the contrary, a good work can attract the public’s attention and bring a large impact. The data acquisition in this process is mainly based on the questionnaire survey of the audience. The experimental results show that before applying digital image edge detection to transform the clip, 7 people thought that the clip needed to be further improved to improve the quality of the film itself, and only 1 person affirmed the film; after the modification, 9 people proposed the film praise; in addition, 15 people audience watched the film processed by the edge detection method and gave high evaluations to the three aspects of sound effects, special effects, and editing, especially the editing part, with a score of 8.9.

A novel digital fuzzy system for image edge detection based on wrap-gate carbon nanotube transistors

This study presents a novel digital fuzzy approach for image edge detection based on carbon nanotube field effect transistor (CNTFET). The proposed design employs a simple fuzzy rule for digital image edge detection. To investigate the functionality and performance of the proposed fuzzy approach, our results are compared with the MATLAB edge detection operators such as Sobel, Canny, and Prewitt. Based on the simulation results, we demonstrate that the proposed fuzzy approach can be implemented through nanoscale logic gates and provide a reasonable accuracy. The core element of the proposed fuzzy system, implemented with CNTFETs, occupies 0.22 µm2 layout area and consumes 535 nW power consumption. Our results also emphasize the benefits of the Gate-All-Around (GAA) CNTFETs as a potential candidate for nanoscale digital fuzzy image processing applications.

A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal

Fuzzy sets provide a framework for incorporating human knowledge as an efficient unsupervised machine learning tool for problem solving. The approach discussed in this paper introduces a generalized transfer learning scheme using rule based fuzzy logic for edge detection in digital images. The spatial domain statistical properties of the image are explored as training data set and expressed in fuzzy format to obtain a decision function for optimal edge detection along with reduction of impulse noise. During fuzzy inference process, a specific linguistic value in input fuzzy set is selected in order to obtain an optimal range of second order difference which discriminates the edge pixels from the non-edge pixels. The proposed fuzzy rule based optimal edge pixel detection method in the presence of random valued impulse noise tends to sufficiently extract the edge pixels with out boosting the noisy pixels. The effectiveness of the proposed fuzzy rule based edge detection scheme is verified by testing it on various standard test images and comparing with existing edge detection techniques at different noise densities.

Analisis Kompleksitas Algoritma Filter IRR Shen-Castan untuk Deteksi Tepi pada Citra Digital

This study aims to measurethe level of complexity of a Shen-Castan IIR filter algorithm for edge detection in digital images based on time complexity and space complexity. This analysis is very helpful when going to do an algorithm optimization and data structure that will be used in a system. Time complexity states how long an algorithm runs at runtime based on the input given. While space complexity states how much space in memory an algorithm needs when operating. The calculation of time complexity and space complexity is denoted by Big-O notation. From the results of time complexity calculations obtained if the Shen-Castan IIR filter algorithm has the complexity of the O (n) algorithm and requires space complexity O (n) where the algorithm will need at least N units of space in memory and depends on the length of the array.

Comparative Performance Analysis of various Digital Image Edge Detection Techniques against Salt and Pepper Noise

The edge detection is a digital image processing technique to find the boundaries or edges of an image or object through the brightness discontinuity. There are many operators to get boundaries or edges but we need more effective and accurate methods. This paper will provide a comparison of Conventional Sobel, Prewitt, Roberts, Canny and Log Operators Edge Detector Techniques against Salt and Pepper noise With regard to Mean Square Error (MSE), Root Mean Square Error (RMSE), signal to noise ratio (SNR) , peak signal to noise ratio (PSNR) , mean-absolute error (MAE) and Bit error, etc.

Experimental investigation on the dynamic behaviour of metal foam: From yield to densification

Large size specimen should be adopted in testing the cellular material with large cell dimension, whereas, experimental investigation on the dynamic densification of large size cellular materials is still a big challenge. In this paper, a new experimental frame has been presented, which could carry out dynamic tests of low strength cellular materials from yield to densification (80% strain). Functionally, this method could be named as a ‘direct impact Hopkinson bar test’ where the measurement of stress is realized by a two points strain gage measurements technique and the strain is measured by high speed camera and digital image edge detection. The principle to maintain a relative constant impact speed has been discussed as well. With this method, dynamic experiments on a closed-cell metal foam have been carried out, the stress-strain curves from elastic zone to densification zone have been obtained. Results revealed that strain hardening behavior was observed in higher density foam while the lower one exhibited an ideal response of cellular foam: a long plateau before densification. It is also found that the higher density foam has stronger strain hardening.

Optimizing Robinson Operator with Ant Colony Optimization As a Digital Image Edge Detection Method

Edge detection serves to identify the boundaries of an object against a background of mutual overlap. One of the classic method for edge detection is operator Robinson. Operator Robinson produces a thin, not assertive and grey line edge. To overcome these deficiencies, the proposed improvements to edge detection method with the approach graph with Ant Colony Optimization algorithm. The repairs may be performed are thicken the edge and connect the edges cut off. Edge detection research aims to do optimization of operator Robinson with Ant Colony Optimization then compare the output and generated the inferred extent of Ant Colony Optimization can improve result of edge detection that has not been optimized and improve the accuracy of the results of Robinson edge detection. The parameters used in performance measurement of edge detection are morphology of the resulting edge line, MSE and PSNR. The result showed that Robinson and Ant Colony Optimization method produces images with a more assertive and thick edge. Ant Colony Optimization method is able to be used as a method for optimizing operator Robinson by improving the image result of Robinson detection average 16.77 % than classic Robinson result.

An Edge Detection Method Based on Adjacent Dispersion

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.

A Review of Classic Edge Detectors

In this paper some of the classic alternatives for edge detection in digital images are studied. The main idea behind edge detection is to find where abrupt changes in the intensity of an image have occurred. The first family of algorithms reviewed in this work uses the first derivative to find the changes of intensity, such as Sobel, Prewitt and Roberts. In the second reviewed family it is used second derivative, for example in algorithms like Marr-Hildreth and Haralick. Results obtained from a qualitative point of view (perceptual) and from a quantitative point of view (number of operations, execution time) are compared, considering different ways to convolve an image with a kernel (step required in some of the algorithms).

ALGORITHMS FOR EDGE DETECTION BY USING FUZZY LOGIC TECHNIQUE

The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. This paper introduces such operators on hand of computer vision application. novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel Robert and Canny operators. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.

Implementation of Hog Edge Detection Algorithm Onfpga's

In recent years, HOG (Histogram of Oriented Gradients) algorithm has get popularity. Researchers tend to use HOG algorithm for recognizing objects in images. HOG algorithm is used object recognition with very high success rate. Hardware reinforcement is very important studying on large size and complex images to perform image processing techniques. In this study, it is aimed to implement HOG algorithm on FPGA (Field Effect Gate Array) structure for edge detection in digital images. Hence, HOG algorithm runs on FPGA to recognize different geometrical figures in the images. Objects’ vertical and horizontal edges have sharpened using edge detection algorithms to calculate magnitude and angles of the local gradients. The performance of proposed system is observed with very high success rate.

An Image Sub-Pixel Edge Detection Algorithm of Plant Roots based on Non-linear Fourth-order Interpolation Method

To improve the accuracy of digital image edge detection, an ENO nonlinear fourth-order interpolation based subpixel edge detection algorithm was proposed in this paper. A stencil was constructed through classical Canny operator, followed by processing gray images to generate gradient images. ENO nonlinear fourth-order interpolation was applied in the gradient direction of target edges, and then subpixel subdivision computation was performed to obtain subpixel accurate locations of target edges, the performance of our edge detection algorithm was compared with that of centered cubic convolution and non-centered cubic convolution based edge detection methods. Simulation results demonstrated that the ENO based interpolation detection algorithm improved the abilities of detecting image edges.

Research and Analysis of Edge-Detection of Digital Images

Edge-detection is the basic characteristic of images. Edge-detection plays an important role in computer vision and image analysis, it is the key link of image analysis and recognition. In this paper, we mainly analyze several edge-detection operators, research the processing results. The edge-detection operators mainly include Roberts operator, Prewitt operator, Sobel operator, Canny operator and LoG operator. Finally we discuss the advantage and disadvantage of the edge-detection operators.

PERBANDINGAN METODE-METODE EDGE DETECTION UNTUK PROSES SEGMENTASI CITRA DIGITAL

Edge detection (Edge detection) is executed to detect the operating margins (edges) that restricts two homogeneous image regions with different brightness levels (Pitas, 1993). A digital image edge detection is the process of looking for differences in the intensity of the states boundaries of an object (sub-images) in the entire digital image in question. Purpose of edge detection is to improve the appearance of the boundary line of an area or object in the image. The process of image edge detection is done by looking for the locations of the intensity of the pixels that discontinue with the intensity of the pixels adjacent (neighboring / neighborhood).There are several methods such as object edge detection operators are known, such as (1) Sobel, (2) Prewitt, (3) Robert, (4) Laplacian of Gaussian, (5) and Canny (6) Kiresh. Each operator has its own advantages and disadvantages in performing edge detection. By doing a combination or in the development of these operators allow to gain a better edge detection (obviously).One of the contributions of this study for science and technology is the creation of a software application that can be used as a simulation tool using edge detection operators in digital image and its various combinations in order to gain a better edge detection (obviously).Keywords: Edge detection, detection operator

Novel Adaptive Neuro-Fuzzy based Edge Detection Technique

Digital image processing is a subset of the electronic domain where the image is converted to an array of small integers, called pixels, symbolizing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Interest in digital image processing methods stems from two principals applications areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. This research problem deals with a novel adaptive neuro-fuzzy inference system (ANFIS) for edge detection in digital images. The internal parameters of the proposed ANFIS edge detector are optimized by training using by proposed training pattern. The edges are directly determined by ANFIS network

A new algorithm to extract the lines and edges through orthogonal projections

The present paper provides a generalization to the approach proposed by Frei and Chen to masks of any even dimension for line and edge detection in digital images. In the work presented in ICCS2004 we proposed the generalization to masks of odd dimension. We are completed with masks of even dimension and therefore the decomposition of Euclidean space in direct sum of the three subspaces: edge, line and uniform. We propose in this paper, first, a new algorithm based on the use of the norm projection vector as the best guarantee against the angle of projection since its use is independent of the chosen convolution masks. This will reduce the computational time of operations. Second, we designed the multiplicative factor of the standard deviation, f , allows us to modify the threshold value to decide which candidate pixel is an edge or line. Because the calculation of the average of all standard deviations implies a high computational time, we propose a new logarithmic model that acts automatically depending only on the size of the image and the size of the mask used. It is completed with the application of the designed algorithm to the synthetic and real image.

An improved method for edge detection based on interval type-2 fuzzy logic

In this paper, a method for edge detection in digital images based on the morphological gradient and fuzzy logic is described. A basic method for edge detection was improved using fuzzy logic. An advantage of the improved method is that there is no need of applying filtering to the image. The simulation results were obtained with a type-1 fuzzy inference system (T1FIS) and with an interval type-2 fuzzy inference system (IT2FIS) for improving the edge detection method. We show that the images obtained with fuzzy logic are better than the ones obtained with only the morphological gradient method. In particular the IT2FIS achieved the best results, because of the flexibility to model the uncertainty in the gradient values and the gray ranges for the edge images. In both TIFIS and IT2FIS the membership function parameters were obtained directly from the images; this allows application of the proposed method to images with different gray scales.