Lightning Image Segmentation Based on Contour Detection and Curvature Analysis

Many cues have been proposed for contour detection or image segmentation. These include low-level image gradients to high-level information such as the identity of the objects in the scene or 3D depth understanding. While state-of-the-art approaches have been incorporating more cues, the relative importance of the cues is unclear. In this paper, we examine the relative importance of low-, mid- and high-level cues to gain a better understanding of their role in detecting object contours in an image. To accomplish this task, we conduct numerous human studies and compare their performance to several popular segmentation and contour detection machine approaches. Our findings suggest that the current state-of-the-art contour detection algorithms perform as well as humans using low-level cues. We also find evidence that the recognition of objects, but not occlusion information, leads to improved human performance. Moreover, when objects are recognized by humans, their contour detection performance increases over current machine algorithms. Finally, mid-level cues appear to offer a larger performance boost than high-level cues such as recognition.

This work focuses on joint image denoising and contour detection. On the one hand, contour detection on clean images (without noise) has been extensively studied from both variational formulation and deep learning perspectives. On the other hand, although the task of jointly denoising and contour detection has been largely considered in image processing literature using variational formulations, it has not been handled from the model-based neural network perspective. In this work, we propose an unfolded discrete Mumford-Shah procedure that enables us to bridge the gap between standard variational procedures designed to perform the combined denoising/edge detection task and black-box neural network designed for edge detection purpose.

Image contour detection is fundamental to many image analysis applications, including image segmentation, object recognition and classification. However, highly accurate image contour detection algorithms are also very computationally intensive, which limits their applicability, even for offline batch processing. In this work, we examine efficient parallel algorithms for performing image contour detection, with particular attention paid to local image analysis as well as the generalized eigensolver used in Normalized Cuts. Combining these algorithms into a contour detector, along with careful implementation on highly parallel, commodity processors from Nvidia, our contour detector provides uncompromised contour accuracy, with an F-metric of 0.70 on the Berkeley Segmentation Dataset. Runtime is reduced from 4 minutes to 1.8 seconds. The efficiency gains we realize enable high-quality image contour detection on much larger images than previously practical, and the algorithms we propose are applicable to several image segmentation approaches. Efficient, scalable, yet highly accurate image contour detection will facilitate increased performance in many computer vision applications.

In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved objects to accurately locate their center. Thus, contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In this paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm’s performance is better than both traditional machine learning methods, such as Support Vector Machine, Extreme Learning Machine and even deep Convolutional Neural Network. The extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, and thus can be applied to contour detection for Cassini ISS images.

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

At present, in medical diagnosis, facial acne vulgaris is a common disease, which mainly occurs in adolescents and has a large population base. For different degrees of facial acne, it needs to be classified and treated to relieve the pressure on medical resources. Normally, dermatologists use models that artificially determine the severity of acne vulgaris on the face. However, the outstanding problem is that it is a waste of manpower, and it may not be possible to quickly and reasonably grade acne treatment of different degrees. In this article, in view of the above problems, image recognition is used to judge the severity of facial acne vulgaris at different levels, so that perform hierarchical diagnosis and treatment. Therefore, this paper designs a graded diagnosis and treatment system for facial acne vulgaris based on image recognition. The system includes a camera acquisition system, an image processing system, an image recognition system, and related detection algorithms. In the processing process, the collected facial acne vulgaris images are mainly subjected to color processing, image segmentation processing and contour detection, and combined with Harris corner detection algorithm to detect acne. In order to determine the severity of acne and the site of the disease to a certain extent, and provide a reference for graded diagnosis and treatment.

Bubble detection is a challenging problem in automatic process control in the power and energy industry, medical and pharmaceutical industry and many other fields. Computer vision methods applications for bubble detection and measurement is the principal step of robust bubbles monitoring systems development. In various applications the input image may include a diverse and image background, especially in different environments. This paper presents a new and effective bubble detection approach. The main steps of this proposed approach are as follows: image preprocessing, background subtraction, and contour detection. The graph cut algorithm is used for image segmentation. The Haar wavelet transform is applied to collect bubble component points. The developed approach is evaluated based on the real data set.

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice.

Vehicle Tracking and Counting through Image Processing is an efficient system for real-time vehicle detection, classification, and counting using the powerful combination of YOLO and OpenCV. The System uses the application of machine learning in the domain of image processing for vehicle tracking and counting. OpenCV in this system is used to process the video feed or images and provide real-time visualization of the vehicles for detection. The system uses advanced computer vision techniques such as contour detection, image segmentation, and feature extraction to accurately locate and recognize individual vehicles in a scene. The detected vehicles are classified into various categories, including cars, trucks, motorcycles, and bicycles (Two & four wheelers) using the trained YOLO model. In addition to detection and classification the count of vehicles is also noted down. The primary objective of this system is to track the vehicles, distinguish and count them. This system can be used in – Traffic Signal Management and Parking Lot Monitoring. Keywords: - YOLO, OpenCV, Python, CVAT

In the real world, the biological visual system is more efficient than the machine visual system in analyzing visual information. Physiology theories show that this efficiency owes to the multi-layer neural network in human visual system, in which every layer accomplishes different tasks and is related with other layers. The low-level stages of the human visual system, especially the retina, can provide certain scale information for the high-level stages of visual system through using the non-classical receptive field (nCRF) mechanism. This mechanism that the nCRF size can be adjusted automatically by ganglion cell (GC) can achieve a multi-scale image analysis. The results, reflecting the distribution of the image information, can be shared by several algorithms or processes solving different visual tasks, such as contour detection and image segmentation. A model of multi-scale image analysis based on GC has been proposed in this paper, which retains the key information and reduces the redundancy information for the further stages of the visual system. Experimental results on N-cut and contour detection show that this multi-scale image analysis model provides distinctive improvement for these image processing tasks.

This work is dedicated to joint image restoration and contour detection considering the Ambrosio-Tortorelli functional. Two proximal alternating minimization schemes with convergence guarantees are provided, PALM-AT and SL-PAM-AT, as well as closed-form expressions of the involved proximity operators. A thorough numerical study is conducted in order to evaluate the performance of both numerical schemes as well as comparisons to state-of-the-art Mumford-Shah strategies.

Myocardial ischemia induced by stress may produce abnormalities of left ventricular (LV) wall motion. The location of wall motion abnormalities may be used to estimate the location and severity of coronary arterial obstructions. The American Society of Echocardiography has recommended the use of a 16-segment model of LV for assessment of wall motion abnormalities. Evaluation of motion of each segment of LV can be used to localize wall motion abnormalities (anterior, inferior, and lateral). Wall motion abnormalities can then be assigned to the distribution of each coronary artery. The normal response of the LV to stress is a uniform increase of regional wall motion, thickening and a reduction in end-systolic LV cavity volume, with minimal changes in diastolic cavity volume. Thus, echocardiographic comparison of LV performance before and after stress can be used to detect reversible myocardial ischemia. Computer-assisted detection of ischemia is based on a quantitative analysis of LV wall thickness at end-diastole and end-systole. This paper presents an approach to computer-assisted detection of ischemia utilizing proper boundary identification, segmentation and comparative analysis of wall segments. The proper boundary identification includes an image pre-processing stage, contour detection and segmental analysis. The algorithm used in this study combines the detection of endocardial and epicardial boundaries, and the computation of the area of segments of LV wall. The technique is a modified form of two-phase relaxation active contour detection. The analysis of the LV wall segments before and after stress includes the computation of the area of a segment at end-diastole and end-systole. These areas provide the key values for monitoring LV wall motion.

Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition. The mainstream works to address this problem focus on designing engineered gradient features. In this work, we show that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs). While rather than using the networks as a blackbox feature extractor, we customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters. A new loss function, named positive-sharing loss, in which each subclass shares the loss for the whole positive class, is proposed to learn the parameters. Compared to the sofmax loss function, the proposed one, introduces an extra regularizer to emphasizes the losses for the positive and negative classes, which facilitates to explore more discriminative features. Our experimental results demonstrate that learned deep features can achieve top performance on Berkeley Segmentation Dataset and Benchmark (BSDS500) and obtain competitive cross dataset generalization result on the NYUD dataset.

Describes a method of image segmentation for image coding based on contour detection and region-growing procedures. Contour detection allows one to find the most evident frontiers of homogeneous regions. The region-growing procedure serves to close contours and to obtain more precise segmentation. The original idea of the method is the choice of growing centres which are placed on the skeleton of non-closed regions and used in split-and-merge procedure of region growing. The method allows one to obtain staircase closure of original contours which is easy to code. >

Does a lightning channel show a fractal?