Comparison of Various Edge Detection Technique

Morphological edge detection plays a significant role in image processing and computer vision. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects also. However, edge detection is challenged and difficult to find the discontinuities in surface orientation, changes in material properties and variations in scene illuminations. In this paper, an improved co-occurrence morphology edge detection algorithm is proposed. We exploit the co-occurrence filter (a combination of bilateral filter and gray-level co-occurrence matrix) to handle with noisy images, and two novel structural elements are used for edge detection. The proposed edge detection algorithm can reasonably consider noise reduction and preserve the detailed edge information. The experimental results demonstrate the superior performance with some existing methods and exhibit better anti-noise ability.

The iris is a part of the human anatomy that can be used as a biometric identifier. Data obtained from the iris can be converted into information through iris image processing, and in order to obtain accurate iris pixel results, an edge detection operator is required that can provide detailed and good image quality effects. In this research, a comparative analysis of the Canny, Sobel, Prewitt and Roberts edge detection operators was carried out on iris images. The purpose of performing a comparative analysis of edge detection methods is to compare the detection results of each edge detection operator on iris recognition detected by each operator. The results of the comparison of edge detection methods using precision tables can be analyzed to show that the Canny edge detection operator provides better, smoother and sharper edge results in actual edge point detection, namely 0.357867, while Sobel =, 0.210212, Prewitt = 0.212452 and Roberts = 0.279196. From these results it can be concluded that the edge detection results provided by Sobel and Prewitt are less sharp and sensitive to noise, and the comparison results can vary depending on the intensity of the image and the image object being compared.

Additive manufacturing (AM), or 3D printing, has revolutionized the fabrication of complex parts, but assessing their quality remains a challenge. Quality assessment, especially for the interior part geometry, relies on post-print inspection techniques unsuitable for real-time in situ analysis. Vision-based approaches could be employed to capture images of any layer during fabrication, and then segmentation methods could be used to identify in-layer features in order to establish dimensional conformity and detect defects for in situ evaluation of the overall part quality. This research evaluated five image segmentation methods (simple thresholding, adaptive thresholding, Sobel edge detector, Canny edge detector, and watershed transform) on the same platform for their effectiveness in isolating and identifying features in 3D-printed layers under different contrast conditions for in situ quality assessment. The performance metrics used are accuracy, precision, recall, and the Jaccard index. The experimental set-up is based on an open-frame fused filament fabrication printer augmented with a vision system. The control system software for printing and imaging (acquisition and processing) was custom developed in Python running on a Raspberry Pi. Most of the segmentation methods reliably segmented the external geometry and high-contrast internal features. The simple thresholding, Canny edge detector, and watershed transform methods did not perform well with low-contrast parts and could not reliably segment internal features when the previous layer was visible. The adaptive thresholding and Sobel edge detector methods segmented high- and low-contrast features. However, the segmentation outputs were heavily affected by textural and image noise. The research identified factors affecting the performance and limitations of these segmentation methods and contributing to the broader effort of improving in situ quality assessment in AM, such as automatic dimensional analysis of internal and external features and the overall geometry.

Binocular vision ranging is becoming more and more widely used. In this paper, based on the principle of binocular camera ranging, a binocular ranging system is built with the help of Win10+VS2017+0penCV3.4.6. The calibration of the binocular camera is completed using the Zhang Zhengyou chessboard calibration method and Matlab2017b calibration toolbox. On the basis of BM matched ranging, the method of Canny edge detection, Sobel edge detection and Scharr filter edge detection edge detection followed by BM matched ranging was proposed to obtain the 3D information of the target point on the parallax map, and the difference of the effective target point to the x coordinate was the length of the measured paper. The experimental results demonstrate that the canny edge detection followed by BM matching distance measurement method yields more accurate results with a smaller error of 1.6% or less.

There are numerous applications based on edge detection have been used in the area of image analysis. The technique of edge detection is an important step towards the visual system reliability and security that delivers a better understanding in many applications like object recognition classification, photography, and many more others computer vision application such as pedestrian detection for a vehicle on the road, face detection in biometric, and video surveillance. We know that detection of edge detection is a scientific technique that is practiced to provide better image analysis and towards this purpose, lots of edge identification approach was already implemented by the researchers in the image processing era, but they do not achieve acceptable results for all types of the image that can help in the image analysis. In this research, we introduced a comparative evaluation of edge detection algorithms for instance Sobel, Canny, and Fuzzy logic-based edge detector with an Improved Fuzzy Inference (IFI) system is presented to preserve image edge for image analysis. The key contribution of this research is developing a new hybrid edge mechanism by utilizing the gradient and standard deviation based fuzzy logic approach to achieve better edge detection efficiency. To provide a better edge or non-edge region from an image the proposed IFI has its impact on quality parameters, for instance, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Entropy and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of the proposed IFI system is compared with other edge technique and we observed that the achieved results justify the proposed work in image processing.

Abstract: Abstract: this study evaluates and compares the performance of two edge detection methods Sobel method and Canny method on batik image. Batik images have unique characteristics and complex patterns, making it difficult to analyze the edges. This study presents a comparison of the results using sobel and canny edge detection methods on batik kawung images both from peak signal-to-noise reatio and from mean squared error. The results showed that canny edge detection was better than sobel method. This can be seen from the results of PSNR and MSE that is 100%. This analysis is determined by considering factors such as the accuracy of edge detection, sensitivity to noise, and the ability to handle the complexity of batik drawing patterns. The results of this study provide a detailed description of the advantages and disadvantages of each method in the image of batik kawung. The conclusions that can be drawn from this study can provide valuable guidance for choosing the optimal edge detection method in image analysis of batik kawung and others. Keywords: batik kawung; canny; MSE; PSNR; sobel Abstrak: Penelitian ini mengevaluasi dan membandingkan kinerja dua metode deteksi tepi metode Sobel dan metode Canny pada citra batik. Gambar batik mempunyai ciri-ciri yang unik dan pola yang kompleks, sehingga menyulitkan analisis tepian. Penelitian ini menyajikan perbandingan hasil menggunakan metode deteksi tepi sobel dan canny pada citra batik kawung baik dari peak signal-to-noise reatio maupun dari mean squared error. Hasil penelitian ini menunjukkan bahwa deteksi tepi canny lebih baik dibandingkan dari metode sobel. Hal ini dapat dilihat dari hasil PSNR dan MSE yang dihasilkan yaitu 100%. Analisis ini ditentukan dengan mempertimbangkan faktor-faktor seperti keakuratan deteksi tepi, kepekaan terhadap noise, dan kemampuan menangani kompleksitas pola gambar batik. Hasil penelitian ini memberikan gambaran secara detail mengenai kelebihan dan kekurangan masing-masing metode pada citra batik kawung. Kesimpulan yang dapat diambil dari penelitian ini dapat memberikan panduan berharga untuk memilih metode deteksi tepi yang optimal dalam analisis citra batik kawung dan yang lainnya. Kata kunci: batik kawung; canny; MSE; PSNR; sobel

In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.

Batik is the work of the Indonesian nation which is a combination of art and technology by the ancestors of the Indonesian people. UNESCO designated batik as a Humanitarian Heritage for Masterpieces of the Oral and Intangible Heritage of Humanity, followed by a presidential decree on 2 October 2009 which was designated as Indonesia's National Batik Day. In maintaining the existence of batik in the era of technological advances, they have utilized Artificial Intelligence and Machine Learning technologies. Even though research on batik motifs has become a common topic, there are still many mistakes found during the process of identifying motifs. As is well known, Indonesia has various types of traditional batik which have very diverse colors, patterns and motifs. This is the main problem in the identification of batik motifs, where it is found that many batik motifs adopt or have similarities in either pattern or color to other batik motifs, giving rise to thin predictions when identified with other motifs. This study uses the Convolutional Neural Networks (CNN) method using Edge Detection to identify batik motifs. Using the main dataset of 1106 images divided into 4 classes, namely Kawung, Megamendung, Merak Ngibing, and Parang motifs. The research is focused on comparing the results of prediction effectiveness on the CNN model with Canny edge detection (CNN-Canny) and the CNN model with Sobel edge detection (CNN-Sobel). The training process for each model is applied the same configuration to the dataset with a ratio of 8:2. Then in the model augmentation the functions of random flip, random zoom, and random invert are applied. The results obtained from learning the machine learning model at the testing and validation stage on the CNN-Sobel model obtained an accuracy of 91.2% in the training process and 91.8% in the validation process. Meanwhile, CNN-Canny got 90.5% accuracy in the training process and 86.2% in the validation process. The results of the comparison of the performance of the two models which are mapped to the confusion matrix table on 16 data testing in the form of images of batik motifs that have never been studied by each model show that the CNN-Sobel model can work more optimally than the CNN-Canny model with an accuracy comparison percentage of 94%. compared to 76% in the process of identifying batik motifs.

As an important part of the automation industrial system, mechanical arm robot plays an important role in service, exploration, automation engineering and other fields, and has a broad application prospect. The traditional mechanical arm and Sobel edge detection methods have some shortcomings: 1. The PID method is separated from the ontology model and only carries out simple and multiple calculations according to the code of the steering engine, which is not stable enough and will cause more time consumption; 2. In some complex environments, while Sobel edge detection is detecting the shape of objects, the separation between subject and background is not clear, which makes it impossible to obtain accurate object information. Therefore, this paper proposes a new PID method and Canny edge detection algorithm: 1. PID method combined with three-dimensional coordinate system modeling, to achieve a more stable, faster and more robust calculation formula, to complete the mechanical arm grasping function; 2. Using Canny edge detection and three-dimensional coordinate system to analyze the object image to obtain the accurate information of the target object.

Based on the shortcomings of the traditional edge detection algorithm easily lead to the brink of fracture and discontinuous, a new improved Pulse-coupled neural network (PCNN) is proposed to image segmentation and edge detection. Using Gaussian filter pre-processing, gray image is segmented into binary image. Based on this binary image, the edge detection is accomplished using improved PCNN, which can increase contrast of image and enhance the edge of image. Compared with correlative result using Canny operator and others in the existing references, simulation results show that our methods have both preferable results of gray image edge detection and fine applicability.

Edge Detection is a very essential part in digital image processing. In case of applications which require object information or feature extraction in an image, edge detection can play a vital role. Edge occurs where there is discontinuity or a sharp change in the intensity function. Now there are many operators for edge detection, but the challenge is to get better results from the existing system. This paper represents a hybrid approach which combines the Sobel and Canny edge detectors. It also uses a median filter to remove the salt and pepper noise from the image which consequently smoothen the image and edges can be detected easily. A comparison has also been shown between the hybrid approach with median filter and without median filter. The comparative study shows that using median filter shows enhanced result. As it filters the noise from the image, the accuracy of edge detection improved and achieved ideal effect.

Image steganography is a technique for obscuring secret message within image pixel matrix. It provides means for secretly communicating between two entities with the third party totally unaware of the embedded message. One of the most important methods for image steganography is edge detection technique, wherein secret data is inserted in LSBs of image edge pixels. Data embedding in edge pixels does not create notable distortion in image when compared to embedding data in non-edge pixels. Different types of edge pixel detectors are available including most renowned Canny edge detector, Sobel edge detector, Prewitt edge detector and many others. Hybrid edge detection uses a combination of different edge detectors for generating edge pixels of image. Message to be secretly communicated is inserted in the LSB of these edge pixels, and the image thus formed called stego image is sent to the concerned party. Major drawback of hybrid edge detection technique is its capacity. The number of edge pixels is very less in any image. In the last decade, many papers have focused on increasing capacity of edge detection by using different techniques. In this work, we have proposed an extremely high-capacity hybrid edge detection technique by using data compression. Also, data is encrypted and then inserted in the cover image pixels. For message integrity, SHA512 hash function is used. Subsequently, this work not only improves capacity of edge detection while maintaining the image quality but also provides security and message integrity.

Edge detection is one of the most commonly used operations in image analysis, and there are more algorithms in the literature for enhancing and detecting edges. Natural images contain both textured and untextured regions, so the cues of contour and texture are exploited simultaneously. In this paper, we present a new edge detection method for natural images using decomposition model. The main idea is to decompose image in to two image components (geometric and texture) obtained by the PDE. The edge detection is performed not on the original image but on its geometric components. Experimental results on a wide range of images are shown.

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

Feature extraction in Copy-Move Forgery Detection (CMFD) is crucial to facilitate image forgery analysis. Edge detection is one of the processes to extract specific information from Copy-Move Forgery (CMF) Images. It sensitizes the amount of information in the image and filters out useless ones while preserving the important structural properties in the image. This paper compares five edge detection methods: Robert, Sobel, Prewitt (first Derivative), Laplacian, and Canny edge detectors (second Derivatives). CMFD evaluation datasets images (MICC-F220) are tested with both methods to facilitate comparison. The edge detection operators were implemented with their respective convolution masks. Robert with a 2x2 mask, The Prewitt and Sobel with a 3x3 mask, while Laplacian and canny used adjustable masks. These masks determine the quality of the detected edges. Edges reflect a great-intensity contrast that is either darker or brighter.