Perform Edge Detection Research Articles

Direct Noise-Resistant Edge Detection with Edge-Sensitive Single-Pixel Imaging Modulation

The majority of edge detection methods are applied after the capture of object photos. Thus, edge detection quality suffers when disturbances occur during imaging. This work proposes an effective edge detection technique for single-pixel imaging (SI). A sequence of edge-sensitive single-pixel imaging (ESI) and single-round edge-sensitive single-pixel imaging (SESI) modulation patterns is specially designed to extract the edges of unknown objects directly without the need for any previous images. The modulation patterns are formed by convolving the SI basis patterns with a second-order differential operator. Compared with existing published edge detection methods, experimental results revealed that the proposed SESI increased the signal-to-noise ratio by at least 228%, thereby reducing the edge detection time by at least half. The edge detection performance of the SESI scheme was also demonstrated on moving objects, with SESI detecting clear edges even when the target was in motion. Moreover, unlike traditional methods, ESI and SESI are immune to light interference and can detect clear edges of objects even if the objects are corrupted by severe interference from laser or light-emitting diode light sources, whereas traditional methods exhibit substantial noise contamination. Consequently, ESI and SESI can lay the groundwork for fast and robust edge detection operations without imaging.

SAR Edge Detection Using Weighted Directional Bhattacharyya Coefficients

In this letter, an edge detector using weighted directional Bhattacharyya coefficients (WDBCs) is proposed to improve the edge detection performance on real synthetic aperture radar (SAR) images. The Bhattacharyya coefficient (BC) is a similarity measure that considers the grayscale distribution of pixels around edges and gives a better description of textured and nonuniform regions than the ratio of averages (ROAs). However, in addition to the grayscale distribution, the spatial distribution of pixels around the edges also has a significant impact on the edge strength. To consider both grayscale and spatial distribution, the weighted histograms are computed by Gaussian-Gamma-shaped (GGS) bi-windows to obtain WDBCs in eight directions. Then the edge strength map (ESM) and edge direction map (EDM) are defined by WDBCs. The edge detector can be obtained after non-maximum suppression and hysteresis thresholding. Experimental results on real SAR images compared with five existing detectors show the effectiveness of the proposed detector.

Multi-criteria decision making under uncertainty for Personality-driven Modern design

This paper combines intelligent deconstruction technology with intelligent structure technology to conduct modern clothing design research and build an intelligent modern clothing system in order to enhance the impact of modern clothing design. Additionally, this work conducts simulation experiments on the edge detection operator and image filtering approach based on Matlab program. This paper eventually filters the image using the median filter method, performs edge detection using the Canny operator, and creates an intelligent modern clothing system after examining and contrasting the experimental results. The modern clothing design system based on intelligent deconstruction technology suggested in this paper can effectively improve the effect of modern clothing design and meet the needs of various types of people, according to the experimental research findings.

Single-pixel edge imaging with gradient Radon spectrum

Edge detection has been a fundamental component in many image segmentation and object detection systems. Recently, many researchers have implemented direct edge detection with the single-pixel imaging system. Compared with traditional edge detection methods, it does not need the procedures of acquiring the object image and then performing edge detection on the image. However, many previous works need to shift the primary illumination patterns at least twice to get the gradient information in multiple directions, which leads to the surge of the measurements. In this paper, we propose a novel single-pixel edge imaging scheme by obtaining the gradient Radon spectrum. Although the proposed scheme also needs to shift the primary illumination patterns once to obtain the gradient information of the object, compared with the Radon single-pixel imaging, no additional measurements are required. In addition, with the gradient Radon spectrum, this scheme can also directly obtain the positions of the straight edges which can be used for image correction, target positioning, and so on. The experimental and numerical results indicate that a high-quality edge image of the object can be restored by the proposed scheme, and the positions of the straight edges can be effectively located with the gradient Radon spectrum.

Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA

During 3D rendering by GPU, jagged patterns will appear at the edge of the image due to the lack of sampling points. To increase the continuity of the image, we use an anti-aliasing technique to process these jagged patterns. In this paper, we review the current anti-aliasing techniques and propose a multi-parallel anti-aliasing algorithm based on Morphological Anti-Aliasing (MLAA). Through an experiment and a comparison of the results, we find that our algorithm achieves better anti-aliasing performance at the edge of the image than Multiple Sampling Anti-Aliasing (MSAA) and MLAA by setting more color gradients. Moreover, our algorithm consumes much less time than MLAA in FPGA implementation by performing edge detection of the image, classification of the aliasing patterns, acquisition of the blend weight coefficients, and calculation of the anti-aliased color in three channels of the image simultaneously. In addition, our algorithm also consumes much less memory than MLAA in FPGA implementation by scaling and optimizing the area texture used by MLAA. A comparison of related works indicates that it is more favorable for the proposed algorithm to perform the anti-aliasing of the image than MSAA and MLAA.

Detection of microcracks in silicon solar cells using Otsu-Canny edge detection algorithm

The necessity for photovoltaic (PV) arrays has expanded due to the rising demand for solar electrical energy. The demand for solar cells has grown as they are a key component of the PV array. Recent years have seen a rise in solar cell production as a result of this demand. The quality of a silicon panel affects its lifespan and effectiveness in the production of solar power. The picture edge-detection method is regularly employed to identify silicon solar panel flaws. On the other hand, defect identification is impacted by the panel's grid shadow. The most modern defect detection technique uses artificial neural networks, and pre-training the model requires a large number of potential samples from picture data sets. This paper offers the innovative Otsu-Canny operator method as a fault discovery strategy to address this problem. Rapid and accurate identification of microcracks enhances the effectiveness of fault detection. The Otsu algorithm is used in this method to improve edge detection performance for microcracks by modifying the canny operator with a double threshold and eliminating the impact of grids on detection. The process was finished by performing noise removal with morphological operation, picture segmentation, and image binarization. The experimental findings demonstrate a significant improvement in purity and integrity of the image processing algorithm used in this research. The Otsu-Canny edge detection technique is more accurate than other conventional edge detection algorithms for detecting microcracks, with a 92.83 % detection rate.

Speckle Noise Suppression Based on Empirical Mode Decomposition and Improved Anisotropic Diffusion Equation

Existing methods to eliminate the laser speckle noise in quantitative phase imaging always suffer from the loss of detailed phase information and the resolution reduction in the reproduced image. To overcome these problems, this paper proposes a speckle noise suppression method based on empirical mode decomposition. Our proposed method requires only one image without additional equipment and avoids the complicated process of searching the optimal processing parameters. In this method, we use empirical mode decomposition to highlight the high frequency information of the interference image and use the Canny operator to perform edge detection, so the diffusion denoising process is guided by high-precision detection results to achieve better results. To validate the performance of our proposed method, the phase maps processed by our proposed method are compared with the phase maps processed by the improved anisotropic diffusion equation method with edge detection, the mean filter method and the median filter method. The experimental results show that the method proposed in this paper not only has a better denoising effect but also preserves more details and achieves higher phase reconstruction accuracy.

Edge detection in single multimode fiber imaging based on deep learning.

We propose a new edge detection scheme based on deep learning in single multimode fiber imaging. In this scheme, we creatively design a novel neural network, whose input is a one-dimensional light intensity sequence, and the output is the edge detection result of the target. Different from the traditional scheme, we can directly obtain the edge information of unknown objects by using this neural network without rebuilding the image. Simulation and experimental results show that, compared with the traditional method, this method can get better edge details, especially in the case of low sampling rates. It can increase the structural similarity index of edge detection imaging from 0.38 to 0.62 at the sampling rate of 0.6%. At the same time, the robustness of the method to fiber bending is also proved. This scheme improves the edge detection performance of endoscopic images and provides a promising way for the practical application of multimode fiber endoscopy.

Edge detection using nonlinear structure tensor

Abstract In order to improve the performance of edge detection for noisy images, a new edge detection method based on nonlinear structure tensor is proposed. First, the tensor product of noisy images is calculated. The tensor product is diffused according to the image gradient, which depends on the tensor product itself. Finally, the eigenvalues and eigenvectors of the diffusion tensor product are calculated, and the edges of the image are detected according to the eigenvalues. The method is compared with other methods. The experimental results show that the average number of edge points detected by method 1, method 2, method 3, and this method are 513.7, 530.0, 509.0, and 719.3, respectively. The average detection time of method 1, method 2, method 3, and this method were 65.3, 54.9, 57.3, and 33.6 s, respectively. When the number of edge detection is the largest, the average detection time of this method is significantly smaller than that of the three comparison methods. Therefore, this method is more suitable for edge detection of noisy images, and the performance of this method is better than that of the comparison method and can improve the performance of edge detection of noisy images.

Fixational eye movements enable robust edge detection.

Human vision relies on mechanisms that respond to luminance edges in space and time. Most edge models use orientation-selective mechanisms on multiple spatial scales and operate on static inputs assuming that edge processing occurs within a single fixational instance. Recent studies, however, demonstrate functionally relevant temporal modulations of the sensory input due to fixational eye movements. Here we propose a spatiotemporal model of human edge detection that combines elements of spatial and active vision. The model augments a spatial vision model by temporal filtering and shifts the input images over time, mimicking an active sampling scheme via fixational eye movements. The first model test was White's illusion, a lightness effect that has been shown to depend on edges. The model reproduced the spatial-frequency-specific interference with the edges by superimposing narrowband noise (1–5 cpd), similar to the psychophysical interference observed in White's effect. Second, we compare the model's edge detection performance in natural images in the presence and absence of Gaussian white noise with human-labeled contours for the same (noise-free) images. Notably, the model detects edges robustly against noise in both test cases without relying on orientation-selective processes. Eliminating model components, we demonstrate the relevance of multiscale spatiotemporal filtering and scale-specific normalization for edge detection. The proposed model facilitates efficient edge detection in (artificial) vision systems and challenges the notion that orientation-selective mechanisms are required for edge detection.

Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan

Utilizing an edge detection algorithm in an image will produce the edges of the image object. The aim is to mark the part that becomes the image's detail and correct the point of blurring of vision that occurs due to errors or the effects of the image acquisition process. This study aims to see the ability of the combination of Sobel and Canny edge detection algorithms (SoCan) to detect the inverted image. The image dataset used is the image of the Laysan Albatross, which consists of 10 original images and ten images that have been inverted based on the standard image dataset. The Laysan albatross is a large species of seabird found in the North Pacific. 99.7% of the total population is found in the Northwest Hawaiian Islands. The research dataset was obtained from the Caltech Vision Lab website http://www.vision.caltech.edu/datasets/cub_200_2011/ with dimensions of 500 x 271 pixels. Based on the analysis of 10 experiments carried out, the combination of the Sobel and Canny algorithm (SoCan) is not good at performing edge detection because it only has an average accuracy of 47.79% with an average accuracy error rate of 52.21%. Thus, in this case, the combination of the Sobel and Canny algorithms (SoCan) is not able to identify the Inversion Image

Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

AbstractA method based on the baseline model of the visual characteristics of images (BMVCI) is proposed to detect cracks in concrete structures. BMVCI refers to the model, which consists of images of the noncrack areas of a concrete structure with cracks or images of the noncrack state of a concrete structure. Compared with the performance of edge detection (ED) methods for detecting cracks in concrete structures, this baseline model expands the quasi‐distance between the edges of cracks and the image background; thus, the crack detection accuracy is effectively improved. Additionally, the discriminative threshold of cracks is quantitatively determined with BMVCI, which avoids the influence of artificial interference when determining the abovementioned threshold used for ED methods. Meanwhile, compared with the methods based on artificial intelligence, such as deep learning (DL), the calculating efficiency of the proposed method is higher because the proposed method converts the high‐dimensional image data into low‐dimensional digital features for training. With the same small size set of training samples, the accuracy of the crack detection of the proposed method is higher than that of the methods based on the framework of DL. In this study, Gaussian convolution is applied to generate the visual characteristics of images, and then a kernel principal component analysis‐based method is implemented to establish the BMVCI. The basic idea of novelty detection is applied to detect cracks in concrete structures. Finally, an experiment on concrete structures is designed and applied to demonstrate the effectiveness of the proposed method.

Improvement in design and training of feature pyramid network for contour refinement

• A simplified feature pyramid architecture, called Edge Pyramid Network, applied for the first time to edge detection. • A recursive fine-tuning strategy introducing a new weighted negative loss to detect accurate and thin edges. • State-of-the-art results on the benchmark datasets for semantic and category-agnostic edge detection. Semantic edge detection is a challenging problem which aims to produce multiple edge maps corresponding to different object categories. The design and the training of the related network are the two most powerful levers to optimize its accuracy. To this end, we first refine existing semantic edge detection architectures with low and high level features inside our Edge Pyramid Network, which already outperforms state-of-the-art networks on popular benchmark datasets. Then, we apply an ingenious fine-tuning strategy, which incrementally improves edge detection accuracy through a few alternated training cycles, using various loss functions to perform thick and thin edge detection. We observe that applying separately any of these refinements outperforms such networks as SEAL and STEAL for category-aware edge detection. More remarkable is the fact that these two refinements provide accumulated benefits on the edge detection performance (+0.85% and +1.78% on MF-scores compared to STEAL on SBD and Cityscapes datasets respectively). This improvement is visible in the edge maps in which EPN architecture results in smoother edges and the fine-tuning strategy is responsible for thinner edges. Furthermore, we show that EPN architecture achieves competitive performance (ODS F-measure of 0.828) against the state-of-the-art category-agnostic edge detection networks on the BSDS500 dataset.

A proposal of edge detection in images with multiplicative noise using the Ant Colony System algorithm

Multiplicative noise is one of the most aggressive types of noise present in various types of images: Synthetic Aperture Radar, Ultrasound images, Ultrasonic Imaging, among others. Edges detectors such as Canny or Sobel are not very efficient for processing an image with multiplicative noise, they also require filtering algorithms, a preprocessing, which is why bio-inspired algorithms are an alternative for processing images with the presence of multiplicative noise, due to its efficiency in finding an approximate solution. This article proposes a method for the edges detection in images with multiplicative noise using the Ant Colony System algorithm. For which we must adapt the Ant Colony System algorithm to detect contours, this we define the calculation of a global pheromone matrix between several edge detection equations, gradient, and the coefficient of variation, these equations are compared for their edge detection performance using a visual inspection and a performance function. The results of the experiments show a correct implementation of the algorithm proposed to the images with multiplicative noise even for high noise levels.

Beyond the Edge: Markerless Pose Estimation of Speech Articulators from Ultrasound and Camera Images Using DeepLabCut.

Automatic feature extraction from images of speech articulators is currently achieved by detecting edges. Here, we investigate the use of pose estimation deep neural nets with transfer learning to perform markerless estimation of speech articulator keypoints using only a few hundred hand-labelled images as training input. Midsagittal ultrasound images of the tongue, jaw, and hyoid and camera images of the lips were hand-labelled with keypoints, trained using DeepLabCut and evaluated on unseen speakers and systems. Tongue surface contours interpolated from estimated and hand-labelled keypoints produced an average mean sum of distances (MSD) of 0.93, s.d. 0.46 mm, compared with 0.96, s.d. 0.39 mm, for two human labellers, and 2.3, s.d. 1.5 mm, for the best performing edge detection algorithm. A pilot set of simultaneous electromagnetic articulography (EMA) and ultrasound recordings demonstrated partial correlation among three physical sensor positions and the corresponding estimated keypoints and requires further investigation. The accuracy of the estimating lip aperture from a camera video was high, with a mean MSD of 0.70, s.d. 0.56 mm compared with 0.57, s.d. 0.48 mm for two human labellers. DeepLabCut was found to be a fast, accurate and fully automatic method of providing unique kinematic data for tongue, hyoid, jaw, and lips.

A Novel Approach towards Automatic Contour Identification of Jaw Cysts from Digital Panoramic Radiographs to improvise the Treatment planning

Panoramic dental x-ray, a two-dimensional dental x-ray that captures the entire mouth in a single image, is used for the initial screening of various dental anomalies. One such is Jaw bone cyst, which, if not identified earlier, may lead to complications which in turn may lead to disfigurement and loss of function. Hence processing of radiographic images plays a vital role in identifying and locating the cystic region and extracting related features to assist clinical experts in further analysis. Objective: To develop an application of active contour model, known as Geodesic Active Contour, to generate Panoramic Dental X-Ray, a single 2 D X-ray image of the entire mouth highlighting the dental specifications. Methods: The process involves the image conversion from the OPG image into grayscale, Contrast adjustment using intensity level slicing, edge smoothing, segmentation, and cyst segmentation by Morphological Geodesic Active Contour to obtain the results. Hence processing of radiographic images plays a vital role in identifying and locating the cystic region. It is crucial in extracting related features to assist clinical experts in further analysis. Conclusion: When efficient and accurate diagnostic methods exist, the treatment and cure become easy and concrete. Based on the morphological snake and level sets, it aims at identifying the boundary by minimizing the energy. Results: Using the structural similarity index, an accuracy of 97.6% is obtained. Advances in Knowledge: This process is advantageous as it is simpler, faster, and does not suffer from instability problems. Morphological methods improve their functional gradient descent by improving stability and speed. The hysteresis algorithm exhibits better edge detection performance, a significant reduction in computational time and scalability.

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.

Investigation the Efficacy of Fuzzy Logic Implementation at Image-Guided Radiotherapy.

At image-guided radiotherapy, technique, different imaging, and monitoring systems are utilized for (i) organs border detection and tumor delineation during the treatment planning process and (ii) patient setup and tumor localization at pretreatment step and (iii) real-time tumor motion tracking for dynamic thorax tumors during the treatment. In this study, the effect of fuzzy logic is quantitatively investigated at different steps of image-guided radiotherapy. Fuzzy logic-based models and algorithms have been implemented at three steps, and the obtained results are compared with commonly available strategies. Required data are (i) real patients treated with Synchrony Cyberknife system at Georgetown University Hospital for real-time tumor motion prediction, (ii) computed tomography images taken from real patients for geometrical setup, and also (iii) tomography images of an anthropomorphic phantom for tumor delineation process. In real-time tumor tracking, the targeting error averages of the fuzzy correlation model in comparison with the Cyberknife modeler are 4.57 mm and 8.97 mm, respectively, for a given patient that shows remarkable error reduction. In the case of patient geometrical setup, the fuzzy logic-based algorithm has better influence in comparing with the artificial neural network, while the setup error averages is reduced from 1.47 to 0.4432 mm using the fuzzy logic-based method, for a given patient.Finally, the obtained results show that the fuzzy logic based image processing algorithm exhibits much better performance for edge detection compared to four conventional operators. This study is an effort to show that fuzzy logic based algorithms are also highly applicable at image-guided radiotherapy as one of the important treatment modalities for tumor delineation, patient setup error reduction, and intrafractional motion error compensation due to their inherent properties.

Medical Image Enhancement Based on Laplace Transform, Sobel Operator and Histogram Equalization

Medical image enhancement is one of the most widely used medical image processing techniques in medical domain. Its purpose is to improve the visual effect of the image and facilitate the analysis and understanding of the image by human or machine. The Laplace transform and the Sobel gradient operator are two common ways of performing edge detection, image sharpening and enabling the image to be enhanced. However, each has limitations when used in isolation. The Laplace operator has a good edge detection effect, but it will make the image noise expand; the Sobel operator has a certain ability to smooth the noise, but the edges of the image obtained after processing are rougher. This paper therefore proposes a method based on both Laplace transform and Sobel operator, and histogram equalization of the transformed image is processed to enhance the image. Using a combination of both filtering methods avoids the disadvantage. This method was found to be effective in improving the quality of lung images and skeletal images through several experiments.

Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.