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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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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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...
Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in co...
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Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep Learning Approach (DLA) in medical imageprocessing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.
Fine-grained image analysis is an enduring and fundamental problem in computer vision and pattern recognition. It underpins a diverse set of real-world applications. Tracking visual targets in fine-grained images is a...
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Fine-grained image analysis is an enduring and fundamental problem in computer vision and pattern recognition. It underpins a diverse set of real-world applications. Tracking visual targets in fine-grained images is a critical issue for industry 4.0, where manufacturing units use visual targets on the production line. There are many existing algorithms for tracking visual targets in fine-grained images, but more methods need to be investigated to provide more accurate results and to overcome the problems in the existing approaches. Since the error rate in visual target prediction is high, the proposed research in this paper focuses on visual target tracking using soft computing methods such as modified hierarchical regression algorithm (HRA), ant colony optimization (ACO), and imageprocessing tools. To remove noise from the fine-grained images and to eliminate noise interference, the fast non-local mean filter and non-subsampled Shearlet transformation are used. The ACO method has been utilized to fragment the fine-grained image after de-noising. The core image engine is used to render an image to save the rendering time. The HRA is utilized to track the visual targets based on the rendering results. The proposed method has a high tracking accuracy and efficiency according to the experimental results. It can be used in industry 4.0 for automation based on fine-grained images on assembly lines or production lines.
This article describes a solution for automating measurements on CNC machines equipped with automatic measurement tools using a vision system that simulates stereoscopic vision. The proposed method of measurement allo...
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Vehicle detection plays an important role in the development of an autonomous driving system. Fast processing and accurate detection are two major aspects of generating the autonomous vehicle detection system. This pa...
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Vehicle detection plays an important role in the development of an autonomous driving system. Fast processing and accurate detection are two major aspects of generating the autonomous vehicle detection system. This paper proposes a novel computer vision-based cost-effective vehicle detection system. Here, a Gentle Adaptive Boosting algorithm is trained with Haar-like features to generate the hypothesis of vehicles. Haar-like feature generates hypotheses very fast but may detect false vehicle candidates. The support vector machine algorithm is trained with the histogram of oriented gradient features to filter out the generated false hypothesis. The histogram of oriented gradients descriptor utilizes the shape and outlines of the vehicles, hence detects vehicles more accurately. Haar-Likes features and histogram of oriented gradients features are organized to accomplish the aspects of autonomous driving. The performance of the proposed vehicle detector is evaluated for day time and night time captured images and compared with three different existing vehicle detectors. The average precision of the proposed system for day time captured image is 0.97 and for night time captured image is 0.94. The proposed system requires 15 times less training time as compared to the existing technique for the same number of image data and on the same CPU.
Generating textual descriptions for images is called image captioning (IC). IC is an innovative concept of Computer vision (CV) with several applications such as visual assistance, multi-modal search, machinevision, ...
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