Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are of...
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image captioning is essential in many fields including assisting visually impaired individuals, improving content management systems, and enhancing human-computer interaction. However, a recent challenge in this domai...
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Our previous studies have shown that laser speckle imaging with sensitive subpixel correlation analysis is able to detect bacterial growth activity and the pattern of colony growth. In the current study, we demonstrat...
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ISBN:
(纸本)9781510673311;9781510673304
Our previous studies have shown that laser speckle imaging with sensitive subpixel correlation analysis is able to detect bacterial growth activity and the pattern of colony growth. In the current study, we demonstrate the potential of this method to analyze fungal growth. We compare the characteristics of the signals obtained from bacteria and fungi. The obtained results will help to improve the parameters of the speckle image acquisition system and the signal processingalgorithms useful for microorganism (both eukaryotic and prokaryotic) growth analyses and speeding up and facilitating microbiological diagnostics.
Leveraging the spatio-spectral modulation and sophisticated reconstruction algorithms, the colorful compressive spectral imaging (CCSI) method can reconstruct a three-dimensional spectral image from a single compressi...
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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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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
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