Edges Of Objects Research Articles

An edge detail enhancement strategy based on Fourier single-pixel imaging

In this study, we propose an edge detection scheme based on Fourier single-pixel imaging, which extracts edges directly from the Fourier spectrum of the target object. The numerical simulations and experimental results show that the edges extracted by the proposed scheme have a better signal-to-noise ratio (SNR) than those extracted by the phase-shift sinusoidal pattern scheme. Furthermore, we investigate the effect of edge extraction in the case of undersampling and propose a plug-and-play edge detection algorithm to enhance the image. Our work combines Fourier single-pixel imaging with edge detection to extract the edges of a target object without imaging, providing a new idea for edge detection techniques.

SiamC Transformer: Siamese coupling swin transformer Multi-Scale semantic segmentation network for vegetation extraction under shadow conditions

The accuracy of fractional vegetation cover (FVC) estimation has important significance in high-precision agricultural and ecological environment assessments. However, the presence ofshadowsunder natural light conditions can cause problems such as poor vegetation distinguishability and blurred boundaries during vegetation segmentation. This leads to missing or over-segmented vegetation inshadedareas, thus reducing the accuracy of FVC. Polarizationinformation can reflect the texture structure features, edgecharacteristics and surface state information, providing important supplementary information to light intensity information. To address the issue of shadows interfering with vegetation segmentation, a Siamese coupling swin transformer (SiamC Transformer) is proposed in this study. In this network, a dual-flow structure is used to simultaneously retrieve the feature information of vegetation degree of linear polarization (DoLP) images and light intensity images simultaneously to obtain multi-dimensional global semantic information of multi-scale vegetation. In the feature fusion stage, two fusion modules are proposed: an adaptive fusion module (AFM) and an adaptive fusion plus module (AFPM), which can fuse shallow spatial information, texture information and deep semantic information. The AFM and AFPM enable the network to better achieve object localization, region activation and edge sharpening, while improving the segmentation accuracy of small objects. The experimental results show that the mean intersection over union (mIoU) of this network is as high as 97.46% on vegetation datasets consisting of light intensity and polarization images, which is better than that of other algorithms. This network has higher accuracy and adaptability for shadow scenes and improves the accuracy of FVC calculation under shadow conditions.

Extraction of Actual Faults with Adjusting the Pixel Width Parameter to Remove Undesired Noise Pixels in the Image (RPW)

Image Denoising, Deblurring, and Enhancement techniques are most commonly used on the images to reduce or completely remove the noise. The noisy images are not capable of perfectly using the solution of the desired problems such as object edge detection, object segmentation, and object classification. Because the edges of these objects have much bluer or more noise pixels for clearly stable detection. The main motivation of this study is to solve the problem of detection and elimination of the undesired noisy pixels on the encountered images. These noisy pixels are needed to be removed from the obtained images which are the results of throughout determined image processing steps. The main reason for this study motivation come out that to find the actual inscription fault has some noisy (undesired) pixels when extracting the true difference between the two images has been subtracted from each other. The subtracted image results are used in the inscription inspection process that controls the accuracy of the inscription quality. In the inscription inspection process, subtracted image results are used to determine the accuracy of the inscription quality. These subtracted images are formed by subtraction from each other of reference images and sample images. In these subtracted images if truly exist inscriptions faults or sometimes that could be occurred undesired noisy pixels at the same time in the subtraction process. This study focused on detecting and eliminating the undesired noisy pixels in order to reach actual inscription faults in the images. Thus, the remove pixels as width algorithm (RPW) has been developed and applied to these specified images.

Multispectral in-line hologram reconstruction with aberration compensation applied to Gram-stained bacteria microscopy

In multispectral digital in-line holographic microscopy (DIHM), aberrations of the optical system affect the repeatability of the reconstruction of transmittance, phase and morphology of the objects of interest. Here we address this issue first by model fitting calibration using transparent beads inserted in the sample. This step estimates the aberrations of the optical system as a function of the lateral position in the field of view and at each wavelength. Second, we use a regularized inverse problem approach (IPA) to reconstruct the transmittance and phase of objects of interest. Our method accounts for shift-variant chromatic and geometrical aberrations in the forward model. The multi-wavelength holograms are jointly reconstructed by favouring the colocalization of the object edges. The method is applied to the case of bacteria imaging in Gram-stained blood smears. It shows our methodology evaluates aberrations with good repeatability. This improves the repeatability of the reconstructions and delivers more contrasted spectral signatures in transmittance and phase, which could benefit applications of microscopy, such as the analysis and classification of stained bacteria.

Image processing‐based Athletes' injury prevention mechanism

AbstractThis study proposes a compressed domain video action recognition to prevent athlete injuries. The main idea is to use the motion vector and residual of the compressed video to construct new spatiotemporal features while extracting compressed code stream information. The new spatiotemporal features have the spatiotemporal relationship between motion vectors and residuals and the characteristic of precise object edges. Through verification on mainstream action recognition datasets (HMDB‐51, UCF‐101), the proposed method has a more negligible computational overhead than action recognition based on the traditional pixel domain and higher recognition accuracy than action recognition based on the video compression domain. Experiments have shown that the new spatiotemporal features based on the compressed domain have advantages such as solid spatiotemporal relationships and high information density, which can make action recognition results with high accuracy, detect abnormalities or patterns associated with injuries, and analyze an athlete's movements and provide personalized training recommendations.

IMPLEMENTASI FILTER SKETSA CITRA DIGITAL MELALUI OPERASI KONVOLUSI DENGAN MATRIK KERNEL LAPLACE OF GAUSS

Sketch filter is used usually as a preliminary process of the images due to further processing such as in pattern recognition of a certain object by deep learning method. Several types of sketch filter can be used to detect the edge of object. In this paper the implementation of convolution operation by the Laplace of Gauss kernel matrix is explored to be used as a sketch filter to detect the edge of object.

Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots

To further improve the real-time performance and accuracy of power transmission line maintenance, this paper primarily focuses on the preliminary line recognition and extraction method based on thermal image processing of infrared images collected by line-following robots for thermal fault detection. Firstly, filtering and noise reduction techniques along with enhanced image processing are applied to preprocess the collected infrared images. This effectively addresses the noise and interference from background objects, which can affect the extraction of overheated areas on the lines, while also reducing the computational memory required for subsequent image processing. Subsequently, an improved Canny edge detection algorithm is employed to extract the edges of foreground objects in the images. Additionally, a region-growing algorithm combined with the geometric features of the lines is employed to filter out unwanted thermal sources, enabling the accurate segmentation and extraction of power transmission lines. This forms a solid foundation for subsequent detection and identification of abnormal hotspots on the extracted lines, and holds significance for the inspection and maintenance of existing thermal faults and potential hotspots in power transmission lines.

Wideband optical edge detection based on dielectric metasurface

Optical image edge detection manifests itself as the advantages of fast speed, low energy consumption, parallel operation, and large information capacity, playing a crucial role in object and image recognition in the realms of biology, medicine, and artificial intelligence. In this paper, a dielectric metasurface that can achieve object edge detection in the full visible band is proposed. By combining two optical differential methods referring to spatial spectrum-dependent transmission function and 4f system-based spatial spectrum phase shift, a tetratomic macropixel metasurface that enables polarization-correlated interference based on dual geometric phase modulation is designed, which can achieve object edge detection under 473, 530, and 633 nm wavelengths, and even under white light conditions. This coherent filtering metasurface operator provides a flexible approach for achieving wideband two-dimensional edge detection.

Automated pipeline reconstruction using deep learning & instance segmentation

BIM is a powerful tool for the construction industry as well as for various other industries, so that its use has increased massively in recent years. Laser scanners are usually used for the measurement, which, in addition to the high acquisition costs, also cause problems on reflective surfaces. The use of photogrammetric techniques for BIM in industrial plants, on the other hand, is less widespread and less automated. CAD software (for point cloud evaluation) contains at best automated reconstruction algorithms for pipes. Fittings, flanges or elbows require a manual reconstruction. We present a method for automated processing of photogrammetric images for modeling pipelines in industrial plants. For this purpose we use instance segmentation and reconstruct the components of the pipeline directly based on the edges of the segmented objects in the images. Hardware costs can be kept low by using photogrammetry instead of laser scanning. Besides the autmatic extraction and reconstruction of pipes, we have also implemented this for elbows and flanges. For object recognition, we fine-tuned different instance segmentation models using our own training data, while also testing various data augmentation techniques. The average precision varies depending on the object type. The best results were achieved with Mask R–CNN. Here, the average precision was about 40%. The results of the automated reconstruction were examined with regard to the accuracy on a test object in the laboratory. The deviations from the reference geometry were in the range of a few millimeters and were comparable to manual reconstruction. In addition, further tests were carried out with images from a plant. Provided that the objects were correctly and completely recognized, a satisfactory reconstruction is possible with the help of our method.

Image segmentation and feature extraction method for lung lesion detection in computed tomography images

Lung cancer is a form of cancer that causes uncontrollable cell growth in the lungs. Patients with lung cancer frequently miss a treatment, face higher health care costs, and get the worst outcomes. The detection of the existence of lung cancer can be performed in a variety of ways, such as computed tomography (CT), magnetic resonance imaging (MRI), and radiography. Many researchers have developed ways of automating lung cancer diagnosis using image processing techniques because of the noise and low image quality between the cancer cells, the lung, and the background. This study develops an image processing technique that uses image segmentation algorithms to segment lung nodules in computed tomography images using feature extraction. In the initial phase, it is essential to establish a rigorous image processing framework with the following sequential steps: (i) object edge identification and (ii) lesion boundary recognition. The architecture includes image processing techniques, thresholding, and morphological detections (erosion and dilation). Lesions can have various sizes and shapes, both regular and irregular. The new method has been applied to find the lesions using their roundness size. In addition to learning purely from CT scans, the previously studied lesion characteristics are also integrated. Data was collected from the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Penang. The manual segmentation was used image segmented in the MATLAB software function to remove the background of the images. The perimeter evaluates such as accuracy, recall, and F-score. Based on the analysis the performance of lung lesion segmentation of accuracy is 99.95, recall at 45.76%, and the F-score is 60.67%. For lung lesion detection, the results shows it consist of 3-5 slices with the value of roundness. Besides, lesion detection also have continuity for the roundness value. The experiment results found clear support for the next step of this research for classifications of lesions.

GLFFNet: A Global and Local Features Fusion Network with Biencoder for Remote Sensing Image Segmentation

In recent years, semantic segmentation of high-resolution remote sensing images has been gradually applied to many important scenes. However, with the rapid development of remote sensing data acquisition technology, the existing image data processing methods are facing major challenges. Especially in the accuracy of extraction and the integrity of the edges of objects, there are often problems such as small objects being assimilated by large objects. In order to solve the above problems, based on the excellent performance of Transformer, convolution and its variants, and feature pyramids in the field of deep learning image segmentation, we designed two encoders with excellent performance to extract global high-order interactive features and low-order local feature information. These encoders are then used as the backbone to construct a global and local feature fusion network with a dual encoder (GLFFNet) to effectively complete the segmentation of remote sensing images. Furthermore, a new auxiliary training module is proposed that uses the semantic attention layer to process the extracted feature maps separately, adjust the losses, and more specifically optimize each encoder of the backbone, thus optimizing the training process of the entire network. A large number of experiments show that our model achieves 87.96% mIoU on the Potsdam dataset and 80.42% mIoU on the GID dataset, and it has superior performance compared with some state-of-the-art methods on semantic segmentation tasks in the field of remote sensing.

MDSNet: a multiscale decoupled supervision network for semantic segmentation of remote sensing images

ABSTRACT Recent deep-learning successes have led to a new wave of semantic segmentation in remote sensing (RS) applications. However, most approaches rarely distinguish the role of the body and edge of RS ground objects; thus, our understanding of these semantic parts has been frustrated by the lack of detailed geometry and appearance. Here we present a multiscale decoupled supervision network for RS semantic segmentation. Our proposed framework extends a densely supervised encoder-decoder network with a feature decoupling module that can decouple semantic features with different scales into distinct body and edge components. We further conduct multiscale supervision of the original and decoupled body and edge features to enhance inner consistency and spatial boundaries in remote sensing image (RSI) ground objects, enabling new segmentation designs and semantic components that can learn to perform multiscale geometry and appearance. Our results outperform the previous algorithm and are robust to different datasets. These results demonstrate that decoupled supervision is an effective solution to semantic segmentation tasks of RS images.

Object Recognition for Millimeter Wave MIMO-SAR Images Based on High-Resolution Feature Recursive Alignment Fusion Network

There are several complex situations in recognizing concealed objects from millimeter wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) security images, such as incomplete imaging of objects, partially occluded objects, and overlapping objects, which are detrimental to the accurate recognition of concealed objects. To solve these problems, a concealed object detection method based on a high-resolution feature recursive alignment fusion network (HR-FRAFnet) is proposed. The HR-FRAFnet can segment the object area from the grayscale image with complex human background and complete the recognition. The overall architecture of the HR-FRAFnet follows the encoder-decoder framework. Specifically, in the encoder stage, a deep parallel feature extraction network (DPFEN) connects the multi-resolution feature maps in parallel and repeats multi-scale feature fusion. This approach suppresses the background noise flowing and retains more recognizable target characteristics. Then, in the decoder stage, a feature recursive alignment fusion module (FRAFM) is designed to enhance the perception of object edges. The FRAFM effectively improves the segmentation accuracy of objects whereas decreasing the computational complexity of the network. Besides, we employ a combined loss function to alleviate the foreground-background imbalance problem in MIMO-SAR images. Homemade human security screening image datasets are used for evaluation. The experimental results show that the proposed method outperforms existing semantic segmentation methods in Mean Intersection over Union (mIoU) and reduces the incidence of missed and error detection of targets.

Robust pose estimation for ship block assembly feature based on large-scale scanning

PurposeHull block assembly is a vital task in ship construction. It is necessary to obtain the actual poses of the assembly features to guide further block alignment. Traditional methods use single-point measurement, which is time-consuming and may lead to loss of key information. Thus, large-scale scanning is introduced for data acquisition, and this paper aims to provide a precise and robust method for retrieving poses based on point set registration.Design/methodology/approachThe main problem of point registration is to find the correct transformation between the model and the scene. In this paper, a vote framework based on a new point pair feature is used to calculate the transformation. First, a special edge indicator for multiplate objects is proposed to determine the edges. Subsequently, pair features with an edge description are noted for every point. Finally, a voting scheme based on agglomerative clustering is implemented to determine the optimal transformation.FindingsThe proposed method not only improves registration efficiency but also maintains high accuracy compared to several commonly used approaches. In particular, for objects composed of plates, the results of pose estimation are more promising because of the compact pair feature. The multiple ship longitudinal localization experiment validates the effectiveness in real scan applications.Originality/valueThe proposed edge description performs a better detection for the edges of multiplate objects. The pair feature incorporating the edge indicator is more discriminative than the original template, resulting in better robustness to outliers, noise and occlusions.

Automated contour extraction for light-sheet microscopy images of zebrafish embryos based on object edge detection algorithm.

Embryo contour extraction is the initial step in the quantitative analysis of embryo morphology, and it is essential for understanding the developmental process. Recent developments in light-sheet microscopy have enabled the in toto time-lapse imaging of embryos, including zebrafish. However, embryo contour extraction from images generated via light-sheet microscopy is challenging owing to the large amount of data and the variable sizes, shapes, and textures of objects. In this report, we provide a workflow for extracting the contours of zebrafish blastula and gastrula without contour labeling of an embryo. This workflow is based on the edge detection method using a change point detection approach. We assessed the performance of the edge detection method and compared it with widely used edge detection and segmentation methods. The results showed that the edge detection accuracy of the proposed method wassuperior to those of the Sobel, Laplacian of Gaussian, adaptive threshold, Multi Otsu, and k-means clustering-based methods, and the noise robustness of the proposed method was superior to those of the Multi Otsu and k-means clustering-based methods. The proposed workflow was shown to be useful for automating small-scale contour extractions of zebrafish embryos that cannot be specifically labeled owing to constraints, such as the availability of microscopic channels. This workflow may offer an option for contour extraction when deep learning-based approaches or existing non-deep learning-based methods cannot be applied.

Motion Context guided Edge-preserving network for video salient object detection

Video salient object detection targets at extracting the most conspicuous objects in a video sequence, which facilitate various video processing tasks, e.g., video compression, video recognition, etc. Although remarkable progress has been made for video salient object detection, most existing methods still suffer from coarse edge boundaries which may hinder their usage in real-world applications. To alleviate this problem, in this paper, we propose a Motion Context guided Edge-preserving network (MCE-Net) model for video salient object detection. MCE-Net can generate temporally consistent salient edges, which are then leveraged to refine the salient object regions completely and uniformly. The core innovation in MCE-Net is an Asymmetric Cross-Reference Module (ACRM), which is designed to exploit the cross-modal complementarity between spatial structure and motion flow, facilitating robust salient object edge extraction. To leverage the extracted edge features for salient object refinement, we fuse them with multi-level spatial–temporal embeddings in a paralleled guidance manner, generating the final saliency results. The proposed method is end-to-end trainable and the edge annotations are generated automatically from ground truth saliency maps. Experimental evaluations on five widely-used benchmarks demonstrate that our proposed method can achieve superior performance to other outstanding methods. Moreover, the experimental results indicate that our method can preserve salient objects with clear boundary structures in video sequences.

ANALYSIS OF EDGE DETECTION METHODS IN IMAGE PROCESSING

The primary goal of computer vision is to interpret the contents of an image, which can be achieved through image segmentation. This technique involves dividing an image into meaningful regions based on the intended application. By detecting and outlining the edges of objects, we can identify them within the image. Edges refer to the boundaries between objects and the background, as well as the boundaries between overlapping objects. Through image segmentation, we can separate the image from the background and extract valuable information. Edge detection is a crucial step in image segmentation, as it involves identifying and locating abrupt changes in the image. This article analyzes various edge detection techniques, such as Sobel, Prewitt, Roberts, Canny, and Laplacian Gaussian (LoG), using an esophageal image in Python.

Self-Supervised Monocular Depth Estimation Using Global and Local Mixed Multi-Scale Feature Enhancement Network for Low-Altitude UAV Remote Sensing

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple images, while UAV depth estimation can complete the task with just one image, thus having higher efficiency and lower cost. This study aims to use deep learning to estimate depth from a single UAS low-altitude remote sensing image. We propose a novel global and local mixed multi-scale feature enhancement network for monocular depth estimation in low-altitude remote sensing scenes, which exchanges information between feature maps of different scales during the forward process through convolutional operations while maintaining the maximum scale feature map. At the same time, we propose a Global Scene Attention (GSA) module in the decoder part of the depth network, which can better focus on object edges, distinguish foreground and background in the UAV field of view, and ultimately demonstrate excellent performance. Finally, we design several loss functions for the low-altitude remote sensing field to constrain the network to reach its optimal state. We conducted extensive experiments on public dataset UAVid 2020, and the results show that our method outperforms state-of-the-art methods.

Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration.

Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (Sα, Eϕ, Fβw, and MAE).

Research on improved level set image segmentation method.

Aiming at the shortcomings of the traditional level set model which only has good robustness to the weak boundary and strong noise of the original target image, this paper proposes an improved algorithm based on the no-weight initialization level set model, introducing bilateral filters and using implicit surface level sets to extract and segment the original target image object more accurately, clearly and intuitively in the evolution process. The experimental simulation results show that, compared with the traditional non-reinitialized level set model segmentation method, the improved method can more accurately extract the edge contours of the target image object, and has better edge contour extraction effect, and the original target noise reduction effect of the improved model is better than that of the model before the improvement. The original target image object edge contour takes less time to extract than the conventional non-reinitialized level set model before the improvement.