Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model tha...
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Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its bi...
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ISBN:
(纸本)9781728198354
Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.
Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to dis...
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For timely identification and treatment in the field of medical imaging, accurate breast cancer segmentation from mammograms is crucial. To improve the identification of breast cancer from mammography images, this pap...
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This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel proce...
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ISBN:
(纸本)9798350310665
This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel processing to improve accurateness is highlighted in the methodology. The proposed method is capable of identifying early warning signs of equipment failure and predicting when the failure is likely to occur. The proposed approach demonstrates the potential for early detection of equipment failure in modern power systems with accurate clustering. The use of machine learning algorithms applied to thermography inspection data provides a reliable and effective way to identify and predict equipment failures, ultimately leading to improved system reliability and reduced maintenance costs.
Yoga, an ancient practice, uses specific hand gestures called mudras, to reduce stress, enhance focus, and improve mental health. However, existing systems for Yoga Mudra Recognition have been limited in scope, recogn...
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image Caption Generation (ICG), situated at the confluence of computer vision and natural language processing, empowers machines to comprehend visual content and express it in human-like language. This research offers...
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ISBN:
(数字)9798350372748
ISBN:
(纸本)9798350372748
image Caption Generation (ICG), situated at the confluence of computer vision and natural language processing, empowers machines to comprehend visual content and express it in human-like language. This research offers a comprehensive overview of key concepts, methodologies, and challenges in ICG. The process involves developing algorithms for the automatic generation of contextually relevant captions, utilizing deep neural networks for feature extraction, and employing natural language processing techniques for coherent composition. Recent advancements, particularly in convolutional neural networks for imageprocessing and recurrent neural networks for language modelling, have significantly elevated the performance of image captioning systems. The study delves into the core components of an ICG system, including pre-processing techniques for image data, feature extraction mechanisms, and the integration of language models. Attention mechanisms, a key innovation in this field, enable the model to focus on relevant image regions while generating captions, closely mirroring human attention patterns. Despite notable progress, ICG faces several challenges, such as handling diverse and complex visual scenes, ensuring cross-modal coherence between images and captions, and addressing biases present in training data. Ethical considerations, particularly in applications like automated content generation, are also discussed. The study concludes by highlighting potential future directions in ICG research, including the incorporation of multimodal learning approaches, enhancing the interpretability of generated captions, and addressing societal concerns related to bias and fairness. As ICG continues to evolve, it holds promise for various applications, ranging from accessibility for the visually impaired to improving content indexing and retrieval in multimedia databases. The research also underscores the significance of the accuracy attainments, showcasing the success of the pr
An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. The system utilizes advanced computer vi...
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ISBN:
(纸本)9789819720521;9789819720538
An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. The system utilizes advanced computer vision techniques and machine learning algorithms to accurately detect and differentiate weeds from crops or other elements in the field. The weed detection system typically consists of hardware components such as cameras or drones which capture high-resolution images or videos of the agricultural area. These images are then analyzed by the artificial intelligence algorithms which have been trained on large datasets of weed images to recognize and distinguish various weed species. This paper examines the pivotal role of AI in weed detection, a critical aspect of farming that determines crop yield and health. Through a comprehensive review, we shed light on the diverse AI-driven techniques including image recognition using Deep Learning, real-time automation, data augmentation, multispectral imaging, and predictive analysis, among others. The ability of AI to distinguish between crops and weeds, often in real-time and under varied environmental conditions, underscores its transformative potential. As weed management represents a significant challenge in agriculture, the precise and proactive capabilities offered by AI can lead to optimized herbicide usage, reduced costs, and enhanced crop productivity.
Wavelet theory has been widely applied in imageprocessing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel ima...
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The accurate identification of large and medium slag in power plant slag transport system has an important impact on the safety, efficiency, environmental protection and economic benefits of power plant. The timely pr...
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