Feature-based disconnected edge segments linker

Image segmentation is an important problem in image processing that has a wide range of applications in medicine, biomedicine and other fields of science and engineering. During the non-learning-based approaches, the techniques based on the partial differential equations and calculus of variation have attracted a lot of attention and acquired many achievements. Among the variational models, the Chan-Vese variational segmentation is a well-known model to solve the image segmentation problem. The level set methods are highly accurate methods to solve this model, and they do not depend on the edges. However, the performance of these methods depends on the level set function and its initial boundary too much. In this paper, we propose automatic initial boundary generation methods based on the edge detectors: Sobel, Prewitt, Roberts and Canny. In the experiments, we prove that among the four proposed initial boundary generation methods, the method based on the Canny edge detector brings the highest performance for the segmentation method. By combining the proposed initial boundary generation method based on the Canny edge detector, we implement the Chan-Vese model to segment biomedical images. Experimental results indicate we obtain improved segmentation results and compare different edge detectors in terms of performance.

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

Introduction:High-speed and high-efficient machining is the inevitable development direction of machining technology. The tool edge preparation can improve the life, cutting performance, and surface quality of a tool and help to achieve high-speed and efficient machining. Therefore, precise modeling and detection of the micron-level contour of a tool edge are crucial for edge preparation. The aim of this study is to provide the model and detect method of the prepared tool edge radius.Methods:The mathematical model of the milling tool trajectory is established through the Matlab. The material removal model by single abrasive particle is established based on the energy conservation principle and energy absorption theory. The material removal model by multiple abrasive grains on the cutting tool edge is constructed using the statistical methods. The mathematical model of the edge radius is established through the geometrical relationship. The milling edge preparation contour detection system is setup based on the machine vision principle through LabVIEW software. Finally, the edge radius at different process parameters is determined by the mathematical model and detection system, and the results are compared with the results of the scanning electron microscopic measurement (SEM).Results:Through the Comparison and analysis of the edge radius measured by the SEM and calculated by the proposed model. The maximum error between the analytical results and SEM measurements is 11.18 μm, while the minimum error is 0.07 μm. Through the comparison and analysis of the edge radius measured by the SEM and the edge detection system. The maximum difference between the two methods is 2.71 μm, and the minimum difference is 0.31 μm. The maximum difference in percentage is 9.2%, and the minimum difference in percentage is 1.2%.Discussions:The edge preparation mechanisms of a single particle and multiple particles on the tool edge are explained. A mathematical model of the edge radius is established, which provides a basis for a deeper understanding of the edge preparation effect. Based on the machine vision principle, the prepared tool micron-level edge detection method is proposed. The histogram specification method, median filtering, multi-threshold segmentation method, and Canny edge detection operator are adopted to obtain the edge contour. The comparison result shows that the mathematical model of the edge radius is accurate, and the proposed tool edge detection method is feasible, which lays the foundation for edge preparation and realization of high-speed and high-efficient machining.

As autonomous driving technology becomes more and more popular, its safety is also attracting attention. Regarding the automatic driving of vehicles, the detection of road markings is particularly important. This paper improves the lane edge detection part of the Hough transform lane line detection method. Because the traditional Canny operator edge detection method is good for image processing, but the detection time is long, this paper replaces the Canny algorithm with the Robert operator edge detection method. The sub-edge detection method can improve the detection speed of lane line extraction. In MATLAB, by using multiple edge detection operators to perform edge detection on the same image 100 times, and taking the average of the detection time, it is found that the Robert operator takes a shorter time in the detection process than the Canny operator; Then the Robert operator and Canny operator are respectively fused into the Hough transform lane line detection. After 100 times, the same image is detected, and the running time is statistically averaged for comparison and analysis. The Robert operator is better than the Canny operator. The time taken is reduced by 0.15191 s. The simulation results show that the integration of Robert operator in Hough transform lane line detection improves the real-time performance of lane detection.

Image edge detection significantly preserves the important structural properties in an image, while reducing the amount of data and filters out useless information. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces the Canny edge detection algorithm and the implementation of the Canny edge-detection algorithm on an FPGA. Custom implementation in FPGAs, using a combination of hardware and software co-design, allows real-time processing, providing a good trade-off between performance and flexibility. The results show that FPGAs are good alternatives, which can be used to off-load these computationally-intensive and repetitive functions as co-processors and the capability of supporting canny edge detection for real-time image processing.

In this paper, we propose a novel method for automatic object segmentation from natural scene images. This method is based on saliency map, mean shift segmentation and level set method. First, a histogram based contrast method is used to generate the saliency map of the input image. Second, the input image is segmented into clusters using mean shift. Based on the saliency map, segmented clusters are classified into background and foreground clusters. After that, an initial contour for level set method is determined by applying morphological erosion on foreground clusters. Finally, the level set method is used to evolve the initial contour and find the object of interest. The experimental results have shown that our proposed method is not only able to replace manual labeling of initial contour in level set method but also able to yield more accurate segmentation results than previous segmentation approaches.

This report was centred on quantifying uncertainties associated with edge detection in image processing. The Canny edge-detection operator was used in this work due to its commonality within the image processing field. The proposed method employs a Monte Carlo approach to propagate uncertainties through the measurement model and two sampling methods were examined: simple random sampling and Latin hypercube sampling. The uncertainty-aware Canny operator method was successfully applied to several case-study images in which an uncertainty was quantified for the measurand – in this case the measurand was the area of and extracted region within the input image.

One of the first steps of feature extraction is edge detection. There are various methods for edge detection such as sobel operator, log method and canny operator but with disadvantages (create noise, image smoothing and discontinuous edge detected). We present an improved method for edge detection. In this method, edge detected by morphology’s operator and their combination and with the use of various structure elements of images in satellite and remote sensing. Keywords: Satellite, Image processing, Edge detection, Multi spectral images

The object contour detection in natural scenes is an important research problem in computer vision. The difficulty is that the integrity of contour extracted is interfered with a large number of texture edges in the background seriously. In recent years, some researchers have introduced image contour detection method with biological visual features and achieved preferable results. Such as, based on the fact of visual outer region suppression, they can suppress a certain amount of texture edge while extracting the contour of the object, and obtain the proper contour edge. However, when this method is used for contour detection in some image which the texture is similar to the contour, the texture edge still cannot be removed well, and the result is unsatisfactory. In consideration of the merit and demerit of the feature suppressed method, we propose an improved algorithm for object contour detection in natural scene based on image saliency. Firstly, the initial contour is obtained by using the suppression effect of non-classical receptive field, and the saliency map is obtained by using the hyper-complex Fourier transform image saliency detection method, and then the initial contour and the saliency map are integrated into the final contour. Compared with the traditional contour detection method, the contour extraction algorithm based on image saliency can remove more background information and improve the accuracy and integrity of object contour extracted in natural scenes.

As an indispensable measuring instrument, the pointer instrument has been widely used in the fields of industrial production. While the manual instrument identification has problems of higher labor intensity, higher rate of misjudgment and lower production, we take advantage of computer vision to deal with the images of pointer instrument dials. Two algorithms are proposed for image edge detection: structured random forests edge detection and canny edge detection, Hough transform is used to recognize the straight lines of the pointer, then the automation of instrument identification can be realized. Our results show that the accuracy of the dials printed by the two methods can meet the production requirements.

Accurate segmentation of COVID‐19 CT images is crucial for reducing the severity and mortality rates associated with COVID‐19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID‐19 CT images, we introduce CDSE‐UNet: a novel UNet‐based segmentation model that integrates Canny operator edge detection and a Dual‐Path SENet Feature Fusion Block (DSBlock). This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the DSBlock, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution Block (MSCovBlock), replacing the standard convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE‐UNet’s superior performance over other leading models. Specifically, CDSE‐UNet achieved an accuracy of 0.9929, a recall of 0.9604, a DSC of 0.9063, and an IoU of 0.8286, outperforming UNet, Attention‐UNet, Trans‐Unet, Swin‐Unet, and Dense‐UNet in these metrics.

Preface This book is intended to be a self-contained introduction to analytitc foundation of a level set method for various surface evolution equations including curvature flow equa-tions. These equations are important in various fields including material sciences, image processing and differential geometry. The goal of this book is to introduce a generalized notion of solutions allowing singularities and solve the initial-value problem globally-in-time in generalized sense. Various equivalent definitions of solutions are studied. Several new results on equivalence are also presented. This book contains rather complete introduction to the theory of viscosity solutions which is a key tool for the level set method. Also a self-contained explanation is given for general surface evolution equations of the second order. Although most of results in this book is more or less known, they are scattered in several references sometimes without proof. This book presents these results in a synthetic way with full proofs. However, the references are not exhaustive at all. This book is suitable for applied researchers who would like to know the detail of the theory as well as its flavour. No familiarity of differential geometry and the theory of viscosity solutions is required. Only prerequisites are calculus, linear algebra and some familiarity of semicontinuous functions. This book is also suitable for upper level of under graduate students who are interested in the field. This book is based on my Lipschitz lectures in Bonn 1997. The author is grateful to its audience for their interest. The author is also grateful to Professor Naoyuki Ishimura, Professor Reiner Sch�tzle, Professor Katsuyuki Ishii and Professor Masaki Ohnuma for their critical remarks on earlier versions of this book. The financial support of the Japan Society for the Promotion of Science (no. 10304010, 11894003, 12874024, 13894003) is gratefully acknowledged. Finally, the author is grateful to Ms. Hisako Iwai for careful typing of the manuscripts in latex style.

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.

This paper proposes a hybrid boundary detection method for image based on a new modified level set method and a fuzzy model. It is applied to a boundary detection problem of coronary plaque. Level set method has been applied widely in image processing. It however does not work well for an intravascular ultrasound (IVUS) image because an image gradient, commonly used for calculating a speed function in the level set method, cannot detect an image boundary well. The level set method and the weighted image separability proposed by the authors in the past were applied for a coronary plaque boundary detection problem. The level set method could not however detect the plaque boundary in several regions. One problem was that the candidates of the plaque boundary detected by the weighted separability were unclear in several regions. The other problem was that the IVUS image often becomes shadowed and it contains no texture information there, which is caused by the presence of the guide wire. To overcome this problem, we propose a new modified level set, and we further propose a hybrid boundary detection method based on the new modified level set and the Takagi Sugeno (T-S) fuzzy model for detecting a coronary plaque boundary. The boundary detection accuracy of the proposed method was significantly better than those of the previous methods we proposed in the past.

In this paper we compare different edge detectors based on peak signal to noise ratio on Echocardiographic images. Edge detection is a critical element in image processing, since edges contain a major function of image information. The function of edge detection is to identify the boundaries of homogeneous regions in an image based on properties such as intensity and texture.We have taken Perwitt edge detector, Robarts edge detector, LoG edge detector, Canny edge detector, and Sobel edge detector for this comparison and study.