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Interactive Attention AI to Translate Low-Light Photos to Captions for Night Scene Understanding in Women Safety 2nd

Interactive Attention AI to Translate Low-Light Photos to Ca...

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2nd International conference on Big data, Machine Learning, and Applications, BigDML 2021

作者： Rajagopal, A. Nirmala, V. Vedamanickam, Arun Muthuraj Indian Institute of Technology Madras India PG and Research Department of Physics Queen Mary’s College Tamilnadu Chennai India National Institute of Technology Tiruchirapalli India

ISBN: (纸本)9789819934805

There is amazing progress in deep learning-based models for image captioning and low-light image enhancement. For the first time in literature, this paper develops a deep learning model that translates night scenes to sentences, opening new possibilities for AI applications in the safety of visually impaired women. Inspired by image captioning and visual question answering, a novel ‘Interactive image Captioning’ is developed. A user can make the AI focus on any chosen person of interest by influencing the attention scoring. Attention context vectors are computed from CNN feature vectors and user-provided start words. The encoder–attention–decoder neural network learns to produce captions from low-brightness images. This paper demonstrates how women safety can be enabled by researching a novel AI capability in the interactive vision–language model for perception of the environment in the night. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Domain-Adaptive Deep Transfer Learning for Cross-Modal Heterogeneous image Matching under Label-Scarce Constraints 2

Domain-Adaptive Deep Transfer Learning for Cross-Modal Heter...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Gong, Guoqing Yang, PeiZhen Yang, Chao Han, Zhiqiang Ye, Yuanxin Southwest Jiaotong University Faculty of Geosciences and Engineering Sichuan Chengdu611576 China

ISBN: (纸本)9798331515669

Deep learning has made significant strides in cross-modal heterogeneous remote sensing image matching, yet it remains heavily dependent on annotated target scene data for training models. Under label-scarce constraints, domain discrepancies between actual test data and training data often result in a decline in the performance of trained models. To tackle this challenge, a domain adaptation method is put forth for consideration in the paper for cross-modal heterogeneous image matching. The method achieves the transformation between the images of the existing scene and the target scene through style transfer, and narrows the domain offset between the images of the target scene generated by the transformation of images of the existing scene and the original target scene through adversarial learning. It aligns the target domain with unlabeled information and the source domain with labeled information from both the image and feature perspectives, thereby solving the matching tasks inside the target domain through the application of knowledge from the source domain. Experiments were conducted using two different optical and synthetic aperture radar (SAR) cross-modal datasets, alternating as both target and source domains in training and testing. Results confirmed that compared to current traditional matching methods and deep learning matching methods, our method demonstrates feasibility and effectiveness in cross-dataset transfer, effectively reducing the dependency on annotated data for matching tasks in the target scene. © 2024 IEEE.

关键词： Contrastive Learning

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image-Based Extraction of Prescription Information using OCR-Tesseract 2

Image-Based Extraction of Prescription Information using OCR...

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2nd International conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Ponnuru, Mahesh Ponmalar, Sridevi P. Likhitha, A. Sree, Tanu B. Chaitanya, Guna G. School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India School of Computer Science Vellore Institute of Technology Amaravathi522237 India

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. © 2024 Elsevier B.V.. All rights reserved.

关键词： Optical character recognition

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Outlier Elimination of Telemetry data Based on Arithmetic of 53H 2

Outlier Elimination of Telemetry Data Based on Arithmetic of...

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2nd International conference on Signal image Processing and Communication, ICSIPC 2022

作者： Sun, Hao Liu, Lei Sun, Jian-Wei Duan, Si-Jia The 15th Research Institute China Electronics Technology Group Corporation Beijing100083 China

ISBN: (纸本)9781510657694

In the process of telemetry data distribution from the satellite to each telemetry station, due to the influence of weather, magnetic field interference and other accidental factors, the observed data will seriously deviate from the target true value. We call this part of data outliers. The accuracy of telemetry data is very important. If there are outliers in telemetry data, it will directly affect the subsequent fault detection and prediction, produce abnormal misjudgment, and even cause false alarm, which will perplex the subsequent staff to confirm the satellite state and deal with satellite abnormalities. In this paper, an improved arithmetic of 53h is proposed to eliminate outliers from the telemetry data of a navigation satellite. The elimination rate of isolated outliers can reach 100%, and it can avoid the wrong elimination of correct data by continuous *** model is simple and fast, and can be well applied to non real-time and real-time telemetry data preprocessing. © 2022 SPIE.

关键词： Satellites

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MB2C: Multimodal Bidirectional Cycle Consistency for Learning Robust Visual Neural Representations 24

MB2C: Multimodal Bidirectional Cycle Consistency for Learnin...

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32nd ACM International conference on Multimedia, MM 2024

作者： Wei, Yayun Cao, Lei Li, Hao Dong, Yilin College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400706868

Decoding human visual representations from brain activity data is a challenging but arguably essential task with an understanding of the real world and the human visual system. However, decoding semantically similar visual representations from brain recordings is difficult, especially for electroencephalography (EEG), which has excellent temporal resolution but suffers from spatial precision. Prevailing methods mainly focus on matching brain activity data with corresponding stimuli-responses using contrastive learning. They rely on massive and high-quality paired data and omit semantically aligned modalities distributed in distinct regions of the latent space. This paper proposes a novel Multimodal Bidirectional Cycle Consistency (MB2C) framework for learning robust visual neural representations. Specifically, we utilize dual-GAN to generate modality-related features and inversely translate back to the corresponding semantic latent space to close the modality gap and guarantee that embeddings from different modalities with similar semantics are in the same region of representation space. We perform zero-shot tasks on the ThingsEEG dataset. Additionally, we conduct EEG classification and image reconstruction on both the ThingsEEG and EEGCVPR40 datasets, achieving state-of-the-art performance compared to other baselines. © 2024 ACM.

关键词： Electroencephalography

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

引用

9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

The proceedings contain 23 papers. The special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical image Computing, Embodied AI and Robotics for HealTHcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. The topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion images;a Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen data Properties with Diffusion Autoencoder Embeddings Using Spine MRI data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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2nd Serbian International conference on Applied Artificial Intelligence, SICAAI 2023

2nd Serbian International Conference on Applied Artificial I...

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2nd Serbian International conference on Applied Artificial Intelligence, SICAAI 2023

ISBN: (纸本)9783031608391

The proceedings contain 28 papers. The special focus in this conference is on Applied Artificial Intelligence. The topics include: HR Analytics: Opportunities and Challenges for Implementation in SME;comparison of data-Driven and Physics-Informed Neural Networks for Surrogate Modelling of the Huxley Muscle Model;Different dataset Preprocessing Methods for Tree Detection Based on UAV images and YOLOv8 Network;data-Driven Digital Twins of Renewable Energy Grids;AI Anomaly Detection for Smart Manufacturing;drawbacks of Programming dataflow Architectures and Methods to Overcome Them;forecasting River Water Levels Influenced by Hydropower Plant Daily Operations Using Artificial Neural Networks;Enhancement of Outdated Vision Systems in SMEs with Artificial Intelligence Powered Solutions;evaluation of Nano-Object Magnetization Using Artificial Intelligence;Predicting Discus Hernia from MRI images Using Deep Transfer Learning;prediction of Subsonic Flutter Speeds for Composite Missile Fins Using Machine Learning;application of Ensemble Machine Learning for Classification Problems on Very Small datasets;an Unsupervised image Segmentation Workflow for Extraction of Left Coronary Artery from X-Ray Coronary Angiography;artificial Intelligence in Intelligent Healthcare Systems–Opportunities and Challenges;object Detection in Precision Viticulture Based on Uav images and Artificial Intelligence;Material Classification of Underground Objects from GPR Recordings Using Deep Learning Approach;Genetic Programming Approach in Better Understanding of the Relationship Between the Number of Viable Cells and Concentration of O2-, NO2- and GSH Produced in Cancer Cells Treated with Pd(II) Complexes;application of Artificial Intelligence for Predicting of New Potential Inhibitors of Vitamin K Epoxide Reductase;digital Empathy;Embedding Artificial Intelligence into Wearable IoMT Systems.

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Auxiliary Dense Layered LeNet Deep Learning based Mango Variety Classification 2

Auxiliary Dense Layered LeNet Deep Learning based Mango Vari...

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2nd International conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Janani, G. Fernandes, Jonita Maria Harshini, T. Nivetha, R. Joshika Rao, O.R. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

In the agriculture sector, physical classification of fruits is a costly process that can produce inconsistent outcomes due to human negligence. Fruit categorization from snapshots is an extremely difficult venture, especially for mango fruits because various species of mango fruits share significant resemblance. This paper proposes Auxiliary Dense Layered LeNet (ADL-LeNet) that classifies the mango type with high accuracy. The Mango Varieties Classification and Grading dataset, which includes 1200 mango pictures was used for this work. The Mango image dataset is applied with the existing CNN model to select the best CNN model. The LeNet outperforms with the accuracy of 92.77% when compared to the other CNN model. Now LeNet is selected for finetuning the number of dense layers. Generally, the LeNet is composed of three convolutions, two subsampling followed by two dense layers. Now, the LeNet is modified by adding one extra dense layer at the end that compress the output further into vector size of 42 to suggest the proposed model ADL-LeNet. The ADL-LeNet composed of three convolutional, two subsampling and three dense layers ending with the final output layer. The Mango image dataset has been applied with the existing CNN and proposed ADL-LeNet. The implementation shows that the proposed ADL-LeNet model outperforms with the high accuracy of 97.6% in mango type classification. © 2024 IEEE.

关键词： Fruits

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Implementation of Chaos-Based image Encryption on FPGA 2

Implementation of Chaos-Based Image Encryption on FPGA

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2nd IEEE International conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Lakshmi, C. Sri, Yasvanthira D. Subashini, R. Vinizia Amirtharajan, Rengarajan Thanikaiselvan, V. Mahalingam, Hemalatha School of EEE SASTRA Deemed University Thanjavur India School of Electronics Engineering Vellore Institute of Technology Vellore India King Abdulaziz University Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia

ISBN: (纸本)9798350347982

Information security is becoming more crucial to data transmission and storage in this era of digital communication. In numerous processes, images are frequently used. As a result, protecting the image data from misuse is equally crucial. image encryption is a solution to protect visual data from hackers. The chaotic image encryption algorithm is a unique choice, but software algorithms can be hacked from anywhere. The paper proposes the encryption scheme along with a standalone device using FPGA. Even if the software is compromised, the FPGA encryption keys are more secure. Because the encryption scheme is developed on core reconfigurable hardware, which can increase the security level and speed since multiple operations can be performed simultaneously as also FPGA, it is generally true that without physical access to a standalone FPGA device, it is difficult for a non-intended user to hack the key stored on the device. FPGAs typically store their configuration information (including the key) in non-volatile memory, such as flash memory, which is not easily accessible to an attacker without physical access to the device. Furthermore, FPGA is more flexible in altering its internal architecture based on the proposed encryption scheme, which adapts to different encryption schemes. Chaos-based image encryption using FPGA can overcome several issues like security, key management, robustness, and scalability. The proposed encryption scheme is validated by performing entropy, correlation, and histogram test analyses. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Automated Module for image Quality Assessment from Narrow-Banding Imaging Endoscopy Cameras 2nd

Automated Module for Image Quality Assessment from Narrow-...

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2nd International conference on Applied Algorithms, ICAA 2025

作者： Bui, Van Hieu Pham, Khac Long Nguyen, Thuan Thanh Nguyen, The Anh Education Zone FPT University Hoa Lac Hi-Tech Park Ha Noi Viet Nam

ISBN: (纸本)9783031845420

Gastrointestinal diseases pose a significant threat to human health, with gastrointestinal tumors ranking among the most common and fatal conditions. Narrow Band Imaging (NBI), an advanced endoscopic method, plays a crucial role in diagnosing these diseases by producing detailed imaging data. However, the large volume of generated data often includes low-quality images, making it challenging to identify frames with diagnostic value. This underscores the importance of an efficient image quality control mechanism for data optimization. In this study, we introduce a non-reference image quality assessment (IQA) framework specifically designed for NBI endoscopy. The proposed two-stage approach employs deep learning for accurate evaluation. In the first stage, a patch-based classification model assesses image regions by leveraging local features extracted through a convolutional neural network. In the second stage, a breadth-first search algorithm combines these patch-level outcomes to generate an overall image quality score. The framework achieves outstanding performance, with precision and recall rates of 96% and 97%, respectively. Additionally, it reduces storage requirements by approximately 90% and adapts effectively to various application needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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