Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Ultrasound imaging operation capture and image analysis for speckle noise reduction and detection of shadows

Ultrasound is becoming increasingly important in medicine, both as a diagnostic tool and as a therapeutic modality. At present, experienced sonographers observe trainees as they generate hundreds of images, constantly providing them feedback and eventually deciding if they have the appropriate skills and knowledge to perform ultrasound independently. This research seeks to advance towards developing an automated system capable of assessing the motion of an ultrasound transducer and differentiate between a novice, an intermediate and an expert sonographer. The research in this thesis synchronizes the ultrasound images with three depth sensors (Microsoft Kinect) placed on the top, left and right side of the patient to ensure the visibility of the ultrasound probe. Videos obtained from the three categories of sonographers are manually labeled and compared using Studiocode Development Environment to complete the items on the medical form checklist. Next, this thesis investigates and applies well known techniques used to smooth and suppress speckle noise in ultrasound images by using quality metrics to test their performance and show the benefits each one can contribute. Finally, this thesis investigates the problem of shadow detection in ultrasound imaging and proposes to detect shadows automatically with an ultrasound confidence map using a random walks algorithm. The results show that the proposed algorithm achieves an accuracy of automatic detection of up to 85%, based on both the expert and manual segmentation

Slow-roll corrections in multi-field inflation : a separate universes approach

In view of cosmological parameters being measured to ever higher precision, theoretical predictions must also be computed to an equally high level of precision. In this work we investigate the impact on such predictions of relaxing some of the simplifying assumptions often used in these computations. In particular, we investigate the importance of slow-roll corrections in the computation of multi-field inflation observables, such as the amplitude of the scalar spectrum P-zeta, its spectral tilt n(s), the tensor-to-scalar ratio r and the non-Gaussianity parameter f(NL). To this end we use the separate universes approach and delta N formalism, which allows us to consider slow-roll corrections to the non-Gaussianity of the primordial curvature perturbation as well as corrections to its two-point statistics. In the context of the delta N expansion, we divide slow-roll corrections into two categories: those associated with calculating the correlation functions of the field perturbations on the initial flat hypersurface and those associated with determining the derivatives of the e-folding number with respect to the field values on the initial flat hypersurface. Using the results of Nakamura & Stewart '96, corrections of the first kind can be written in a compact form. Corrections of the second kind arise from using different levels of slow-roll approximation in solving for the super-horizon evolution, which in turn corresponds to using different levels of slow-roll approximation in the background equations of motion. We consider four different levels of approximation and apply the results to a few example models. The various approximations are also compared to exact numerical solutions.Peer reviewe

Combined use of GPR and Other NDTs for road pavement assessment: an overview

Roads are the main transportation system in any country and, therefore, must be maintained in good physical condition to provide a safe and seamless flow to transport people and goods. However, road pavements are subjected to various defects because of construction errors, aging, environmental conditions, changing traffic load, and poor maintenance. Regular inspections are therefore recommended to ensure serviceability and minimize maintenance costs. Ground-penetrating radar (GPR) is a non-destructive testing (NDT) technique widely used to inspect the subsurface condition of road pavements. Furthermore, the integral use of NDTs has received more attention in recent years since it provides a more comprehensive and reliable assessment of the road network. Accordingly, GPR has been integrated with complementary NDTs to extend its capabilities and to detect potential pavement surface and subsurface distresses and features. In this paper, the non-destructive methods commonly combined with GPR to monitor both flexible and rigid pavements are briefly described. In addition, published work combining GPR with other NDT methods is reviewed, emphasizing the main findings and limitations of the most practical combination methods. Further, challenges, trends, and future perspectives of the reviewed combination works are highlighted, including the use of intelligent data analysis.Xunta de Galicia | Ref. ED431F 2021/08Ministerio de Ciencia e Innovación | Ref. RYC2019–026604-

Curvilinear Structure Enhancement in Biomedical Images

Curvilinear structures can appear in many different areas and at a variety of scales. They can be axons and dendrites in the brain, blood vessels in the fundus, streets, rivers or fractures in buildings, and others. So, it is essential to study curvilinear structures in many fields such as neuroscience, biology, and cartography regarding image processing. Image processing is an important field for the help to aid in biomedical imaging especially the diagnosing the disease. Image enhancement is the early step of image analysis. In this thesis, I focus on the research, development, implementation, and validation of 2D and 3D curvilinear structure enhancement methods, recently established. The proposed methods are based on phase congruency, mathematical morphology, and tensor representation concepts. First, I have introduced a 3D contrast independent phase congruency-based enhancement approach. The obtained results demonstrate the proposed approach is robust against the contrast variations in 3D biomedical images. Second, I have proposed a new mathematical morphology-based approach called the bowler-hat transform. In this approach, I have combined the mathematical morphology with a local tensor representation of curvilinear structures in images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. Especially the proposed method is quite successful while enhancing of curvilinear structures at junctions. Finally, I have extended the bowler-hat approach to the 3D version to prove the applicability, reliability, and ability of it in 3D

A framework for local terrain deformation based on diffusion theory

Terrains have a key role in making outdoor virtual scenes believable and immersive as they form the support for every other natural element in the scene. Although important, terrains are often given limited interactivity in real-time applications. However, in nature, terrains are dynamic and interact with the rest of the environment changing shape on different levels, from tracks left by a person running on a gravel soil (micro-scale), to avalanches on the side of a mountain (macro-scale). The challenge in representing dynamic terrains correctly is that the soil that forms them is vastly heterogeneous and behaves differently depending on its composition. This heterogeneity introduces difficulties at different levels in dynamic terrains simulations, from modelling the large amount of different elements that compose the oil to simulating their dynamic behaviour. This work presents a novel framework to simulate multi-material dynamic terrains by taking into account the soil composition and its heterogeneity. In the proposed framework soil information is obtained from a material description map applied to the terrain mesh. This information is used to compute deformations in the area of interaction using a novel mathematical model based on diffusion theory. The deformations are applied to the terrain mesh in different ways depending on the distance of the area of interaction from the camera and the soil material. Deformations away from the camera are simulated by dynamically displacing normals. While deformations in a neighbourhood of the camera are represented by displacing the terrain mesh, which is locally tessellated to better fit the displacement. For gravel based soils the terrain details are added near the camera by reconstructing the meshes of the small rocks from the texture image, thus simulating both micro and macro-structure of the terrain. The outcome of the framework is a realistic interactive dynamic terrain animation in real-time