Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant ...
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image Captioning (ICs) seamlessly combines the realms of Computer Vision (CV) and Natural Language processing (NLP) task involved in producing textual sentences that summarise the image content in a way which is under...
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For many practical applications, we face the problem that computer vision systems must be installed in the wild, without or with a limited permanent power supply. Therefore, computationally and energy efficient soluti...
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ISBN:
(纸本)9781510673199;9781510673182
For many practical applications, we face the problem that computer vision systems must be installed in the wild, without or with a limited permanent power supply. Therefore, computationally and energy efficient solutions are needed. In particular, in this work, we show that the meaningful use of single-board computers (SBCs) can help achieve these goals. This is in line with the goals of Green AI. In particular, we show that the computer vision algorithms adopted on SBCs yield competitive results compared to high-performance computing devices. To this end, in addition to quantitative performance evaluations, we also measured and compared the power consumption of the algorithmic and technical setup used for various practical problems. These examples demonstrate the practical sustainability of SBCs. They show their performance, reduced power consumption, and lower environmental impact, while still providing real-time performance.
Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more br...
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ISBN:
(纸本)9798350391558;9798350379990
Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCI). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with InceptionV3 model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.
This paper focus on SAR ship detection, which has been widely used in tasks such as marine traffic, fisheries management, battlefield posture assessment and military target reconnaissance. One popular solution is to u...
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ISBN:
(纸本)9789819784929;9789819784936
This paper focus on SAR ship detection, which has been widely used in tasks such as marine traffic, fisheries management, battlefield posture assessment and military target reconnaissance. One popular solution is to utilise deep learning algorithms in conjunction with SAR image object detection. However, due to the unique imaging characteristics of SAR, existing solutions usually do not distinguish well between ships and interference targets in complex backgrounds. In this paper, we address this problem by proposing a sidelobe-aware semi-deformable convolution that takes full advantage of the combination of both standard and deformable convolution to learn high-quality ship features without significantly increasing the computational complexity. Specifically, it makes the feature extraction location more closely fit to the ship shape, strengthens the extraction capability of the region of the ship target itself and the sidelobe features, while reducing the extraction of background information. The channel attention mechanism is then proposed to enhance the ship local detail information extraction. Experiments on two widely used datasets show that the proposed method outperforms the state-of-the-art methods, which is effective and efficient to improve SAR ship detection.
In several domains, such as remote sensing, agriculture, and environmental monitoring, hyperspectral imageprocessing is essential. In this work, the Indian Pines dataset is used to investigate hyperspectral picture c...
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Face recognition is an important technology in the field of digital imageprocessing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of...
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image edges contain most of the information in an image, which is one of the most basic and important features in an image. image edge extraction occupies a special place in computer vision and imageprocessing, which...
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The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infr...
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This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art imageprocessing and machine learning techniques to automate the identification of various materials, ...
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