Image Edge Detection Techniques Research Articles

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

Linear Diophantine Fuzzy Sets: Image Edge Detection Techniques based on Similarity Measures

In the digital imaging process, fuzzy logic provides many advantages, including uncertainty management, adaptability to variations, noise tolerance, and adaptive classification. One of the techniques of digital image processing is the edge detection. The edge detection process is an essential tool to segment the foreground objects from the image background. So, it facilitates subsequent analysis and comprehension of the image’s underlying structural properties. This process can be moved on with the notion of fuzzy sets and their generalizations. The concept of Linear Diophantine fuzzy sets is a generalization of fuzzy sets where reference parameters correspond to membership and non-membership grades. This study aims to apply linear Diophantine fuzzy sets (LDFSs) to edge detection of images. The novelty of this paper is twofold. The first one is that we conduct a comprehensive evaluation to ascertain the similarity values using the linear Diophantine fuzzy similarity measure by leveraging the gray normalized membership values associated with fundamental edge detection techniques. The other is to modify the image pixels into the LDFSs and then filter the images by using the presented similarity measure operators given in the LDFS environment.

Coin Sum Counter from an Image

Abstract: The purpose of our paper is to recognize the coins which exist in the picture taken by the following user, which will be of Indian National Rupees. Almost every sacred place or some tourist place present in India has a charity/donation box. So the partition or segmentation of such coins requires a lot of people or manpower and therefore for that purpose the whole process must be automated so that it can be more efficient as well as reduce time consumption. We will organize and classify them based on the following classes specified by the user while making the model and then calculate the accumulated or total sum of the INR coins. Features of newer Indian coins are also considered. The proposed approach is to use the image classification model to classify the INR coins from the image and use Edge Detection, Gaussian Blur, and other techniques to correctly identify the edges of the coins as well as to find out the denominations of the coins. The implementation of this classification is written in Python and the dataset is taken from Kaggle.

Image Edge Detection Algorithm Based on Fuzzy Radial Basis Neural Network

Digital image processing technology is widely used in production and life, and digital images play a pivotal role in the ever-changing technological development. Noise can affect the expression of image information. The edge is the reflection of the main structure and contour of the image, and it is also the direct interpretation of image understanding and the basis for further segmentation and recognition. Therefore, suppressing noise and improving the accuracy of edge detection are important aspects of image processing. To address these issues, this paper presents a new detection algorithm combined with information fusion based on the existing image edge detection techniques, and the algorithm is studied from two aspects of fuzzy radial basis fusion discrimination, in terms of preprocessing algorithm, comparing the denoising effect of mean and median filters with different template sizes on paper images with added noise, and selecting the improved median filter denoising, comparing different operator edge detection. The effect of image edge detection contour is finally selected as the3∗3Sobel operator for edge detection; the binarized image edge detection contour information is found as the minimum outer rectangle and labeled, and then, the original paper image is scanned line by line to segment the target image edge region. The image edge detection algorithm based on fuzzy radial basis fuser can not only speed up the image preprocessing, meet the real-time detection, and reduce the amount of data processed by the upper computer but also can accurately identify five image edge problems including folds and cracks, which has good application prospects.

Twin-image suppression in digital in-line holography based on wave-front filtering

Digital holography is an imaging process able to recreate three-dimensional representations of objects from recording pattern interference among distinct waves. The in-line configuration setup is a variant considered the simplest physical implementation, providing a feasible manner for acquisition and the same time higher resolution for free-living microscopy imaging using a single illumination system. However, the well-known twin-image problem is bounded to the technique, since there is no separation among reference and objects beams in this configuration. As a result, computational numerical diffraction routines present the twin-image effect intrinsically, imposing several difficulties in terms of post-processing requirements. In this context, this paper aims to present a numerical approach able to provide consistent suppression of twin-image problem for in-line holography, during the numerical diffraction procedure for phase retrieval, combining image subtraction and edge detection techniques. The proposed solution was implemented in Python language, and metrics defined to assess it were both qualitative and quantitative, based on edge detection and some image comparison metrics. The obtained results of the proposed approach present a significant reduction in the twin-image artifacts in the reconstructions of both experimental and simulated holograms, considering a spherical reference wave, regardless of the shapes and sizes of objects.

RHN: A Residual Holistic Neural Network for Edge Detection

Edge detection plays a very important role in many image processing and computer vision applications. Use of deep convolutional neural networks (DCNNs) has significantly advanced the performance of image edge detection techniques. Existing DCNN techniques, which make use of residual learning, exhibit a good edge detection performance at the expense of an extremely high computational complexity. There are a few VGG16-based DCNN techniques for edge detection that have been proposed with relatively much lower complexity. In this paper, by using the mechanism of residual learning, a new VGG16-based DCNN technique for edge detection is proposed with a view to provide a performance superior to that provided by other such networks while still preserving their low complexity. The proposed network is experimented on different datasets and is shown to outperform all the other VGG16-based techniques designed to solve the problem of edge detection.

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.

Comparative Study of Artificial Intelligence Techniques for Image Classification

There are various applications of image processing in the field of engineering, agriculture, graphic design, commerce, historical research and architecture. This paper studies and compares most of the research works done in the field of image processing and machine learning for the purpose of image classification based on the features extracted from the image through different feature extraction techniques. The machine learning techniques studied in this paper are Convolution Neural Network (CNN), Support Vector Machine (SVM) and Fuzzy logic. The paper studies and compares these methods for their implementation in classification of digital images. Color based segmentation models are used to segment the specific features from image and categories them into different classes. First image preprocessing is done on the image to reduce the noise from the image. Then image segmentation and edge detection techniques are used to identify the objects in the image and extract the features through which the image can be labeled with a specific class.

Image edge detection method based on anisotropic diffusion and total variation models

In this study, a novel image edge detection technique based on the combination of total variation (TV) and anisotropic diffusion (PM) models is presented. In the proposed technique, the authors first use the gradient magnitude to eliminate the noise, then utilise the adaptive weight function to detect the edges of the image. The adaptive weight function has a high ability to adapt and change according to the areas information (edges or flats areas). More specifically, TV filter is applied on the areas which suffer from double and false edges, whereas, anisotropic diffusion filter is applied on the areas which suffer from weak and discontinuous edges. Applying TV filter on the double edges areas will allow one to remove most of the false edges, and thus to obtain much sharper edges. While, applying anisotropic diffusion filter on the discontinuous edges areas will lead to obtaining robust and continuous edges. Consequently, less false edges besides high localisation accuracy were obtained. Experimental results demonstrate the superiority of the new approach in terms of removing the false edges and improving the localisation accuracy of the edges. As objective quantitative performance measures, the peak signal-to-noise ratio (PSNR) and Pratt's figure of merit (FOM) were used.

Car model reconstruction from images through character line recognition

PurposeThis paper aims to automatically derive a 2D parametric model of the main characteristic lines of a car from images, blueprints or hand-made sketches of its side view. Then this model can be used for the further computer-aided design manipulation starting from images of the side view of a car.Design/methodology/approachThe method combines different image edge detection techniques and edge removal processes with optimization techniques according to local and global constraints specific of the single curves to automatically construct a precise parametric model of the main character lines of a car from images. First, process the car image to compute the most important curves and then warp a car template model to match its feature points and curves with the ones detected in the image.FindingsThe paper provides method to construct parametric model from an image using maximum cover ratio to the edge points obtained by state-of-the-art edge detection algorithms. A feature points’ organization mechanism produces quadric curves to express feature curves of a product.Research limitations/implicationsThe robustness of the presented method depends on the completeness of edge detection results and the accuracy of some key points’ registration result, so if the image is not good, the result cannot be trusted. Only side-view is considered in this paper. Additional limits in the process regard the side view verification: pictures of the front or rear view can be wrongly classified as lateral ones when they contain round lights.Practical implicationsThis program enables designers to convert the image to geometric parametric model directly.Originality/valueThe method is applicable to shaded pictures, sketches and blue prints of the side view of a car. It can process a database of car images in a batch mode or a specific picture on user demand. The method classifies the cars to different categories: SUV/Wagon/Hatchback, sedan, city and coupe. The authors obtain good results for every category.

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.</p>

Mechanical Part Surface Defect Detection using Crack Extraction Approach

inspection constitutes an important part of quality control in manufacturing industry. The detection of defects on mechanical part surfaces is an important quality control step in the manufacturing of machine products. In this paper, we have introduced a new approach to detect surface defects with varied size, shape in mechanical parts through the use of image processing techniques. First, we apply image edge detection techniques for extracting the edges in an image by identifying pixels where intensity variation is high. Then, for extracting actual defects we reduce gray scale edge information to binary defect information using thresholding. A threshold process will generate a certain amount of noise. So, this noise will removed by a noise filtering technique using the connected component's eccentricity property. Then, based on the highlighted edges, the defect itself should become identifiable by filling the gap between two corresponding edges by comparing gray scale values. The Experimental results show that the proposed method is suitable for extracting the various defects of varying shapes and size in images. KeywordsDetection, Crack Extraction, Edge Detection, Thresholding, Connected Component Property, Gray Scale values

True orthophoto generation using multi-view aerial images

Abstract. Usually aerial imagery are acquired through perspective projection which results in relief displacement in the map. Therefore, orthophotos from large scale aerial images over urban area often suffer from double mappings caused by sudden elevation changes in the scene. In the generation of true orthophotos, one aims at removing these perspective displacements and occlusions. Hence, knowledge of the surface shape is needed, as it has great impacts both on the orthorectification and occlusion identification. Mostly, the relief displacements are caused by buildings. In this paper, we present a method for the generation of true orthophotos from overlapping aerial images of known orientation, especially in built-up areas. Apart from the aerial images, the method does not depend on additional data sources. In particular, we focus on the derivation of building roof outline model using image segmentation and edge detection techniques under the guide of the classification result of point cloud from image matching . Experiment results on real data show the feasibility and the performance of the method.

Comparison and Evaluation of Edge Detection Segmentation Techniques

Edge detection is the basic problem in the field of image processing. Various image edge detection techniques are introduced. Using various edge detection techniques different images are analyzed and compared by MATLAB7.0. In order to evaluate the effect of edge segmentation, the root mean square error is used. The experimental results show that no an edge detection technique works well for all types of images.

Research on Edge Detection Technology

Edge detection is an important field in image processing. Edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection techniques. Various image edge detection techniques are introduced. These techniques are compared by using MATLAB7.0. The qualities of these techniques are elaborated. The results show that Canny edge detection techniques is better than others.

Edge Detection in Digital Images Corrupted by Salt and Pepper Noise using Adaptive Neuro-Fuzzy Inference System (ANFIS)

This paper presents an Edge Detection technique for images corrupted by salt and pepper noise, which is based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). This proposed technique first of all filters out the noise by Noise Adaptive Fuzzy Switching Median Filter (NAFSMF) and then find the edges using proposed ANFIS based edge detector. The training pattern for edge detection is proposed to optimize the internal parameters of the ANFIS based edge detector. The edges are directly determined by the proposed ANFIS based edge detector. This proposed edge detector is then compared with popular edge detectors Sobel, Roberts, Prewitt on the basis of performance metrics PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and No. of Edges detected.

Medical Images Edge Detection Based on Mathematical Morphology

Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and reconstruction. Conventionally, edge is detected according to gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of medical images with salt-and-pepper noise. The simulation results shows that the novel mathematical morphological edge detection algorithm is more efficient for image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms. It has been observed that the proposed morphological edge detection algorithm performs better than sobel, prewitt, roberts and canny’s edge detection algorithm. In this paper the comparative analysis of various image edge detection techniques is presented using MATLAB 8.0 .

Study of Image Edge Detection Techniques

Edges characterize boundaries. Edge detection is a problem of fundamental importance in image processing. The key of edge detection for image is to detect more edge details, reduce the noise impact to the largest degree. In this paper the comparative analysis of various image edge detection techniques is presented. In order to evaluate these techniques, they are used to detect the edge of chromosome image. Firstly, the iterative thresholding algorithm and morphologic erode algorithm together are applied to enhance both the edges of the chromosomes and the contrast of the image. Then, Sobel operator technique, Roberts technique, Prewitt technique and Canny technique are used respectively to detect the edges of the chromosomes in the image.

Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision

Abstract: Edges characterize boundaries and are therefore a problem of fundamental importance in quality assessment of agricultural and food products. Since edge detection is in the forefront of computer vision system for detection of vegetables, fruits and food grains needs to quality inspection and evaluation, it is crucial to have a good understanding of edge detection algorithms. In this paper the comparative analysis of various i mage e dge d etection techniques is presented. The software is developed using MATLAB 7.6. It has been shown that the Canny’s edge detection algorithm performs better than all operators (i.e. LoG, Robert, Prewitt and Sobel ) under almost all scenarios. Keywords: edge detection, noise, computer vision, agricultural and food products DOI: 10.3965/j.issn.1934-6344.201 1 .02.0 83 -0 90 Citation: Narendra V G , Hareesh K S . Study and c omparison of various i mage e dge d etection t echniques used in quality inspection and evaluation of agricultural and food products by computer vision . Int J Agric & Biol Eng, 2011; 4(2): 83 <span style=line-height: 140%; font-family:

Striation Density for Predicting the Identifiability of Fired Bullets with Automated Inspection Systems*

Automated firearms identification systems will correlate a reference bullet with all evidence bullets without a selection procedure to exclude the bullets having insufficient bullet identifying signature. Correlations that include such bullets increase the workload and may affect the correlation accuracy. In this article, a parameter called striation density is proposed for determining and predicting bullet identifiability. After image preprocessing, edge detection and filtering techniques are used to extract the edges of striation marks, the resulting binary image distinctly shows the amount and distribution of striation marks. Then striation density is calculated for determining the quality of images. In the experiment, striation densities for six lands of 48 bullets fired from 12 gun barrels of six manufactures are calculated. Statistical results show strong relation between striation density and identification rate. It can provide firearms identification systems with a quantitative criterion to assess whether there are sufficient striae for reliable bullet identification.