Detection of sky regions is one of the most crucial challenges in imageprocessing and computer visionapplications, including scene parsing, picture retrieval, weather forecasting, and robot navigation. However, it i...
详细信息
Detection of sky regions is one of the most crucial challenges in imageprocessing and computer visionapplications, including scene parsing, picture retrieval, weather forecasting, and robot navigation. However, it is challenging to detect sky regions under certain circumstances, particularly in gloomy and overcast conditions. This study aims to summarize sky region detection approaches, challenges, and applications together. Additionally, classical, and deep learning-based approaches have been delineated. An extensive literature review has been conducted to achieve the objectives of the study. It has emerged that various machine and deep learning approaches have been proposed. Unfortunately, most of the approaches lose efficacy when encountering overcasting or lighting conditions, as most of the approaches are trained on an ideal dataset. Moreover, a taxonomy of sky region detection challenges has been proposed, categorizing the identified challenges into edge-based, color-based, texture-based, deep learning-based methods, etc. The challenging datasets that are being utilized for robust sky detection methods have been presented.
Underwater imageprocessing has received tremendous attention in the past few years. The reason for increased research in this area is that the process of taking images underwater is very difficult. images obtained un...
详细信息
Underwater imageprocessing has received tremendous attention in the past few years. The reason for increased research in this area is that the process of taking images underwater is very difficult. images obtained underwater frequently suffer from quality deterioration issues such as poor contrast, blurring features, colour variations, non-uniform lighting, the presence of dust particles, noise at the bottom of the sea, different properties of the water medium, and so on. The improvement of underwater images is a critical problem in imageprocessing and computer vision for a variety of practical applications. To address this problem, we need to find some other methods to increase the quality of the image while capturing it underwater. But capturing the image in normal circumstances as well as underwater is the same, so once we get an image, some mechanism to increase the quality of the captured image will also be required. A complete and in-depth study of relevant accomplishments and developments, particularly the survey of underwater image methods and datasets, which are a critical issue in underwater imageprocessing and intelligent application, is still lacking. In this paper, we first provide a review of more than 85 articles on the most recent advancements in underwater image restoration methods, underwater image enhancement methods, and underwater image enhancement using deep learning and machine learning methods, along with the techniques, data sets, and evaluation criteria. To provide a thorough grasp of underwater image restoration, enhancement, and enhancement using deep learning and machine learning, we explore the strengths and limits of existing techniques. Additionally, we offer thorough, unbiased reviews and evaluations of the representative methodologies for five distinct types of underwater situations, which vary their usefulness in various underwater circumstances. Two main evaluations, subjective image quality evaluation and objective image quali
Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly d...
详细信息
ISBN:
(纸本)9798350350494;9798350350500
Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly detection technique called Dual-Domain Autoencoders (DDA) is proposed to address these challenges. First, Nonnegative Matrix Factorization (NMF) is applied to decompose the hyperspectral data into anomaly and background components. Refinement of the designation is then done using intersection masking. Next, a spectral autoencoder is trained on identified background signature pixels and used to reconstruct the image. The reconstruction error highlights spectral anomalies. Furthermore, a spatial autoencoder is trained on principal component patches from likely background areas. Fused reconstruction error from the spectral and spatial autoencoders is finally used to give enhanced anomaly detection. Experiments demonstrate higher AUC for DDA over individual autoencoders and benchmark methods. The integration of matrix factorization and dual-domain, fused autoencoders thus provides superior anomaly identification. Spatial modeling further constrains the background, enabling accurate flagging of unusual local hyperspectral patterns. This study provides the effectiveness of employing autoencoders trained on intelligently sampled hyperspectral pixel signatures and spatial features for improved spectral-spatial anomaly detection.
3D vision technology-based research is suggested to model indoor environmental sensory design because machinevision-based models struggle with object removal. The study involves constructing three-dimensional spatial...
详细信息
Computer vision has many applications in smart cities. In smart cities, applying recent vision-based technologies have been explored by researchers in civil structures. The research motivation for this review paper li...
详细信息
Computer vision has many applications in smart cities. In smart cities, applying recent vision-based technologies have been explored by researchers in civil structures. The research motivation for this review paper lies in the wide range of applications computer vision offers in smart cities. With a specific focus on civil structures, researchers have been exploring the utilization of recent vision-based technologies. The main objective of this paper is to investigate vision-based systems for crack detection in civil structures. The paper accomplishes this by reviewing current vision-based technologies, including imageprocessing, machine learning, laser, and ultrasonic approaches, providing descriptions for each technique. Additionally, a comparative analysis of these approaches is presented. Furthermore, the paper addresses the challenges associated with fracture detection in civil construction and offers potential solutions to these problems, taking into account the research problems that have been addressed throughout the study.
This journal presents original research that describes novel pattern analysis techniques as well as industrial and medical applications. It details new technology and methods for pattern recognition and analysis in ap...
This journal presents original research that describes novel pattern analysis techniques as well as industrial and medical applications. It details new technology and methods for pattern recognition and analysis in applied domains, including computer vision and imageprocessing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control.
Pattern Analysis and applications (PAA) also examines the use of advanced methods, including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications.
The journal contains case-studies as well as reviews on benchmarks, evaluations of tools, and important research activities at international centers of excellence.
For all submission-related enquiries, please contact the Journal Editorial Office via “Contacts” or the Senior Editor Dragos Calitoiu at calitoiu@***.
machinevision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely imageprocessing, machine learning, and deep lea...
详细信息
machinevision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely imageprocessing, machine learning, and deep learning were investigated to detect and count infield corn kernels, immediately after harvest for combine harvester performance evaluation. A hand-held low-cost RGB camera was used to collect images with kernels of different backgrounds, based on which a 420 images dataset (200, 40, and 180 for training, validation, and testing, respectively) was generated. Three different models for kernel detection were constructed based on imageprocessing, machine learning, and deep learning. For the imaging processing method, the images were preprocessed (color thresholding, graying, and erosion), followed by Hough circle detection to identify kernels. For the machine learning (cascade detector) and deep learning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deep learning network YOLOv5 was superior to the other approaches, with a small model size (89.3 MB) and a high model average precision (78.3 %) for object detection. The detection accuracy, undetection rate and F1 value were 90.7 %, 9.3 %, and 91.1 %, respectively, and the average detection rate was 55 fps. This study demonstrates that the YOLOv5 model has the potential to be used as a real-time, reliable, and robust method for infield corn kernel detection.
This paper presents a brief analysis of modern sources of knowledge for artificial intelligence (AI) applications and discusses prospects of their development. It is shown that methods, algorithms, and technologies fo...
详细信息
This paper presents a brief analysis of modern sources of knowledge for artificial intelligence (AI) applications and discusses prospects of their development. It is shown that methods, algorithms, and technologies for knowledge extraction based on machine learning, as well as various knowledge extraction techniques that use the digital twin (DT) technology, are currently quite mature and in great demand. As for expert knowledge, it is rarely used in practice, even though it is essential in a number of critical and often unique classes of next-gen applications for which experts are the only available source of knowledge. To solve the problem of efficient access to expert knowledge, intensive research and development in the field of knowledge engineering is required, capable of elevating it to the level of knowledge science, which will be able to solve knowledge processing problems of the same scale and complexity that are currently solved in data science using big data. An analysis of potential areas of research and development in a hypothetical knowledge science is presented, and some methods, models, algorithms, and technologies for processing large volumes of raw fragments of expert knowledge to serve the next generation of intelligent applications are considered.
Contour tracing is a critical technique in image analysis and computer vision, with applications in medical imaging, big data analytics, machine learning, and robotics. We introduce a novel hardware accelerator based ...
详细信息
Contour tracing is a critical technique in image analysis and computer vision, with applications in medical imaging, big data analytics, machine learning, and robotics. We introduce a novel hardware accelerator based on the adapted and segmented (AnS) vertex following (vF) and run-data-based-following (RDBF) families of fast contour tracing algorithms implemented on the Zynq-7000 field-programmable gate array (FPGA) platform. Our algorithmic implementation utilizing a mesh-interconnected multiprocessor architecture is at least 55x faster than the existing implementations. With input-output overheads, it is up to 12.5x faster. Our hardware accelerator for contour tracing is benchmarked on mesh-interconnected hardware, all three families of contour tracing algorithms, and a random image from the imagenet database. Our implementation is, thus, faster for FPGA, application-specific integrated circuit (ASIC), graphics processing unit (GPU), and supercomputer hardware in comparison to the central processing unit (CPU)-GPU collaborative approach and offers a better solution for those systems where the input-output overheads can be minimized, such as parallel processing arrays and mesh-connected sensor networks.
Subject of study. This study investigates the influence of optical-system parameters on the error in determining the orientation and position of fiducial markers. Aim of study. This study determines the dependencies o...
详细信息
Subject of study. This study investigates the influence of optical-system parameters on the error in determining the orientation and position of fiducial markers. Aim of study. This study determines the dependencies of absolute error in position and orientation on various influencing factors. Method. The error in a machine-vision system is assessed based on fiducial markers using computer-image modeling in the Unity 3D graphics system. Main results. Over 100,000 images of AprilTag markers in different positions and orientations were synthesized and processed during the simulation. The results of this simulation yielded the dependencies of absolute position and orientation errors on the distance between the camera and marker, the rotation angle of the marker, and the focal lengths of the camera. Practical significance. The obtained results may be utilized to optimize the placement of markers on the platform, select the optimal video camera positions and lens focal lengths, and implement adjustments in the image-processing algorithm. These changes can improve measurement accuracy in systems used for developing orientation algorithms for microsatellites. (c) 2024 Optica Publishing Group
暂无评论