Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhan...
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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.
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.
The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Dee...
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ISBN:
(纸本)9798350373301;9798350373295
The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Deep Learning (DL) architectures to draw out key details from images, localizing this retrieved data to mitigate the impact of haze. The work explores using DL methods, particularly contrasting the regression and classification models of Convolutional Neural Networks (CNN), to remove haze from foggy images. This work sets the stage for further developments in imageprocessing, particularly in conditions with poor visibility. It opens opportunities for improving image quality in various applications, such as autonomous driving and outdoor robotics, where clarity of vision is crucial. The final stage of the proposed model involves three specific pre-processing methods: contextual regularization, air light estimation and boundary constraint for optimal results. The next stage sets out to determine the best DL model for producing clear images from de-hazed ones.
It is important to miniaturize robot systems while maintaining advantages such as high responsiveness and functionality for human-machine interactions and for achieving integration with other robotic systems such as d...
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It is important to miniaturize robot systems while maintaining advantages such as high responsiveness and functionality for human-machine interactions and for achieving integration with other robotic systems such as drones. In this research, we focused on the miniaturization of a high-speed visual feedback system, and developed a "portable saccade mirror," which is a system that can realize active target tracking using 1000 Hz image capturing, processing, and feedback actuation with only 3 ms latency in a hand-held device. By using a three-dimensionally-stacked vision chip, the proposed system achieved high speed, low latency, low power consumption and compact size, and therefore, can be considered as a good example of a miniaturized high-speed visual feedback system. In this study, we evaluated the performance of the proposed system in comparison with the conventional optical gaze controller, and demonstrated some applications, such as tracking field scope and panorama target scanning.
Welding defects are a crucial problem in the manufacturing industry. However, the industry faces enormous losses for these defects. Conditional monitoring and quality control can reduce this loss. In Industry 4.0, art...
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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 ...
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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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