The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Dee...
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ISBN:
(纸本)9798350373301;9798350373295
The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Deep Learning (DL) architectures to draw out key details from images, localizing this retrieved data to mitigate the impact of haze. The work explores using DL methods, particularly contrasting the regression and classification models of Convolutional Neural Networks (CNN), to remove haze from foggy images. This work sets the stage for further developments in imageprocessing, particularly in conditions with poor visibility. It opens opportunities for improving image quality in various applications, such as autonomous driving and outdoor robotics, where clarity of vision is crucial. The final stage of the proposed model involves three specific pre-processing methods: contextual regularization, air light estimation and boundary constraint for optimal results. The next stage sets out to determine the best DL model for producing clear images from de-hazed ones.
It is important to miniaturize robot systems while maintaining advantages such as high responsiveness and functionality for human-machine interactions and for achieving integration with other robotic systems such as d...
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It is important to miniaturize robot systems while maintaining advantages such as high responsiveness and functionality for human-machine interactions and for achieving integration with other robotic systems such as drones. In this research, we focused on the miniaturization of a high-speed visual feedback system, and developed a "portable saccade mirror," which is a system that can realize active target tracking using 1000 Hz image capturing, processing, and feedback actuation with only 3 ms latency in a hand-held device. By using a three-dimensionally-stacked vision chip, the proposed system achieved high speed, low latency, low power consumption and compact size, and therefore, can be considered as a good example of a miniaturized high-speed visual feedback system. In this study, we evaluated the performance of the proposed system in comparison with the conventional optical gaze controller, and demonstrated some applications, such as tracking field scope and panorama target scanning.
Welding defects are a crucial problem in the manufacturing industry. However, the industry faces enormous losses for these defects. Conditional monitoring and quality control can reduce this loss. In Industry 4.0, art...
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Underwater optics in all-aquatic environments is vital for environmental management, biogeochemistry, phytoplankton ecology, benthic processes, global change, etc. Many optical techniques of observational systems for ...
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Underwater optics in all-aquatic environments is vital for environmental management, biogeochemistry, phytoplankton ecology, benthic processes, global change, etc. Many optical techniques of observational systems for underwater sensing, imaging, and applications have been developed. For the demands of compact, miniaturized, portable, lightweight, and low-energy consumption, a novel underwater binocular depth-sensing and imaging meta-optic device is developed and reported here. A GaN binocular meta-lens is specifically designed and fabricated to demonstrate underwater stereo vision and depth sensing. The diameter of each meta-lens is 2.6 mm, and the measured distance between the two meta-lens centers is 4.04 mm. The advantage of our binocular meta-lens is no need of distortion correction or camera calibration, which is necessary for traditional two camera stereo vision systems. Based on the experimental results, we developed the generalized depth calculation formula for all-size binocular vision systems. With deep-learning support, this stereo vision system can realize the fast underwater object's depth and image computation for real-time processing capability. Our artificial intelligent imaging results show that depth measurement accuracy is down to 50 mu m. Besides the aberration-free advantage of flat meta-optic components, the intrinsic superhydrophobicity properties of our nanostructured GaN meta-lens enable an antiadhesion, stain-resistant, and self-cleaning novel underwater imaging device. This stereo vision binocular meta-lens will significantly benefit underwater micro/nanorobots, autonomous submarines, machinevision in the ocean, marine ecological surveys, etc.
Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision appli...
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In today's world, machine learning, artificial intelligence, IoT, deep learning and several other techniques have become the need of the moment. One such division of artificial intelligence is computer vision. The...
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Median filter is one of the predominant filters that are used to suppress impulse noise. Its simplicity and ability to maintain edges has led to an extensive application in the domain of imageprocessing and computer ...
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Median filter is one of the predominant filters that are used to suppress impulse noise. Its simplicity and ability to maintain edges has led to an extensive application in the domain of imageprocessing and computer vision. However, challenges such as moderate to high running time of the standard median filter algorithm and relatively poorer performance when the image is highly corrupted with impulse noise, have led to the design of several variations of the algorithm. One set of variation of the algorithm concentrates on generating quality outputs, while the other set focuses on reducing running time. Among the set targeting the reduction of the running time of the median filter is the DP approximated median filter. However, DP performs poorly when images are corrupted with moderate to high levels of noise. This paper therefore proposes an Improved Approximation Median Filtering Algorithms (IAMFA-I & IAMFA-ii) based on DP to generate a better output. The introduction of Mid-Value-Decision-Median in DP reduces the chances of selecting corrupted pixel for denoised image. Experimental results indicate that the IAMFA-ii has better running time and equivalent output compared with DP, while IAMFA-I generates better output and has equivalent running time when compared with DP. (C) 2020 Production and hosting by Elsevier B.V. on behalf of King Saud University.
RGB-D cameras provide both depth (D) and colour (RGB) data as the output simultaneously in real-time. The depth data provided by the camera typically contains imperfections, such as holes and noise. Improving the qual...
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We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compe...
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We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e., on an event-by-event basis. ii) Besides purely visual transformations in 2D, the framework can readily use additional information, e.g., by augmenting the events with depth, to estimate the parameters of motion models in higher dimensional spaces. iii) The optimisation complexity only depends on the number of events. We achieve this by modelling the event alignment according to candidate parameters and minimising the resultant dispersion, which is computed by a family of suitable entropy-based measures. Data whitening is also proposed as a simple and effective pre-processing step to make the framework's accuracy performance more robust, as well as other event-based motion-compensation methods. The framework is evaluated on several challenging motion estimation problems, including 6-DOF transformation, rotational motion, and optical flow estimation, achieving state-of-the-art performance.
A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar ima...
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