In this paper, a multi-feature detection method based on graph cut for photovoltaic panels is proposed. Combined with multi-dimensional features such as optical flow field and light intensity, an interactive feature r...
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A fundus image is a two-dimensional pictorial representation of the membrane at the rear of the eye that consists of blood vessels, the optical disc, optical cup, macula, and fovea. Ophthalmologists use it during eye ...
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A fundus image is a two-dimensional pictorial representation of the membrane at the rear of the eye that consists of blood vessels, the optical disc, optical cup, macula, and fovea. Ophthalmologists use it during eye examinations to screen, diagnose, and monitor the progress of retinal diseases or conditions such as diabetes, age-marked degeneration (AMD), glaucoma, retinopathy of prematurity (ROP), and many more ocular ailments. Developments in ocular optical systems, image acquisition, processing, and management techniques over the past few years have contributed to the use of fundus images to monitor eye conditions and other related health complications. This review summarizes the various state-of-the-art technologies related to the fundus imaging device, analysis techniques, and their potential applications for ocular diseases such as diabetic retinopathy, glaucoma, AMD, cataracts, and ROP. We also present potential opportunities for fundus imaging-based affordable, noninvasive devices for scanning, monitoring, and predicting ocular health conditions and providing other physiological information, for example, heart rate (HR), blood components, pulse rate, heart rate variability (HRV), retinal blood perfusion, and more. In addition, we present different types of technological, economical, and sociological factors that impact the growth of the fundus imaging-based technologies for health monitoring.
We investigate GAN inversion problems of using pre-trained GANs to reconstruct real images. Recent methods for such problems typically employ a VGG perceptual loss to measure the difference between images. While the p...
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We investigate GAN inversion problems of using pre-trained GANs to reconstruct real images. Recent methods for such problems typically employ a VGG perceptual loss to measure the difference between images. While the perceptual loss has achieved remarkable success in various computer vision tasks, it may cause unpleasant artifacts and is sensitive to changes in input scale. This paper delivers an important message that algorithm details are crucial for achieving satisfying performance. In particular, we propose two important but undervalued design principles: (i) not down-sampling the input of the perceptual loss to avoid high-frequency artifacts;and (ii) calculating the perceptual loss using convolutional features which are robust to scale. Integrating these designs derives the proposed framework, HRInversion, that achieves superior performance in reconstructing image details. We validate the effectiveness of HRInversion on a cross-domain image synthesis task and propose a post-processing approach named local style optimization (LSO) to synthesize clean and controllable stylized images. For the evaluation of the cross-domain images, we introduce a metric named ID retrieval which captures the similarity of face identities of stylized images to content images. We also test HRInversion on non-square images. Equipped with implicit neural representation, HRInversion applies to ultra-high resolution images with more than 10 million pixels. Furthermore, we show applications of style transfer and 3D-aware GAN inversion, paving the way for extending the application range of HRInversion.
Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges i...
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Low-light image enhancement (LLIE) is essential for numerous computer vision tasks, including object detection, tracking, segmentation, and scene understanding. Despite substantial research on improving low-quality im...
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This paper outlines the creation of a blind assistance application that detects path in real-time and aids the visually challenged people to navigate their surroundings safely. The web application uses flask framework...
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The primary problem facing agriculture, which is essential to ensuring the world's food security, is maximizing crop productivity while reducing the effects of plant diseases. Advanced technologies have the potent...
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The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and applications. The topics include: User-Based Collaborative Filtering Multi-criteria Recommender System Based on I...
ISBN:
(纸本)9783031588778
The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and applications. The topics include: User-Based Collaborative Filtering Multi-criteria Recommender System Based on Interaction Between Criteria, Criteria Set with Choquet Integral;application of machine Learning Techniques to Classify Intention to Pay for Forest Ecosystem Services;Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Method;application of machine Learning Models for Predicting Glucose-Level in the Pure Fluid with Algorithm for Reducing Data Dimension Based on Data Series Extraction;comprehensive Survey On Remote Sensing imageprocessing Techniques for image Classification;item-Based Energy Clustering Recommendation;General Evaluation of EtherCAT-Based Techniques in Various Industrial Systems: Review and applications;towards an IoT-Based Unmanned Surface Vehicle Design for Environment Monitoring in Mekong Delta;3D CNN with BERT and vision Transformer for Video Recognition;Identify Tumors on Lung CT images;a Context-Aware Application to Monitor the Air Quality;applying Guided Discovery Learning to Enhance the Achievement of Information Technology Team.
Uniform laser beams with controllable patterns are crucial for various applications, including laser processing and inertial confinement fusion. While some methods have been proposed to generate flattop beams, they of...
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Uniform laser beams with controllable patterns are crucial for various applications, including laser processing and inertial confinement fusion. While some methods have been proposed to generate flattop beams, they often require complex optical systems that can become ineffective because of the misalignment of the system or the imperfection of optical elements. To overcome these issues, we utilized feedback-based wavefront shaping (FWS) technology to generate flattop beams with desired patterns from a disordered light. To solve the multi-goal optimization problem, we propose some modifications based on the Non-dominated Sorting Genetic Algorithm ii (NSGA2) and success-fully generate focal beams with a uniform intensity distribution and controllable beam shape from the disordered light field. (c) 2023 Optica Publishing Group
Advances in machine learning and neural networks have transformed natural language processing (NLP) and computer vision (CV) applications. Recent research efforts have begun to bridge the gap between the two domains. ...
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ISBN:
(纸本)9798350363029;9798350363012
Advances in machine learning and neural networks have transformed natural language processing (NLP) and computer vision (CV) applications. Recent research efforts have begun to bridge the gap between the two domains. In this work, we propose a semi supervised Multi-Modal Encoder Decoder Network (MMEDN) to capture the relationship between images and textual descriptions, allowing us to generate meaningful descriptions of images and retrieve images from a database using cross-modality search. The semi-supervised training approach, which combines ground truth text descriptions and pseudotext generated by the text decoder within the model, requires far fewer image-text pairs in the training data and can directly add new raw images without manual text labelling for training. This approach is particularly useful for active learning environments, where labels are expensive and hard to obtain. We show that our model performs well with qualitative evaluations. We applied our model for finding images of a person from large databases and generating descriptions of people involved in an event for adding to an automatically generated report. The model was able to retrieve relevant images and generate accurate descriptions, demonstrating its applicability to more practical use cases.
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