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Fine-Grained Spatial-Temporal Framework for Engagement Prediction 2nd

Fine-Grained Spatial-Temporal Framework for Engagement Pred...

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2nd CSIG conference on Emotional Intelligence, CEI 2024

作者： Wang, Shanshan Zheng, Yong Wang, Keyang Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Beijing China Institutes of Physical Science and Information Technology Anhui University Anhui China Zhejiang Dahua Technology Co. Ltd. Zhejiang China School of Computer Science and Technology Anhui University Anhui China

ISBN: (纸本)9789819650835

The engagement prediction task aims to identify the current level of involvement of individuals based on the information presented in video clips. In recent years, engagement prediction has attracted considerable attention due to its significance in real-life scenarios. We observed that an individual’s engagement status is often primarily manifested in the facial region. This observation prompted us to solve the engagement prediction problem by exploiting facial information to learn features related to engagement. In this paper, we propose a novel model for learning fine-grained features, called Fine-Grained Spatial-Temporal Framework (FGST). Specifically, we consider both spatial and temporal aspects. In the spatial domain, we propose an entropy-weighted method based on Fourier transform. By applying Fourier transform to video frames and introducing the concept of information entropy, we can segregate engagement-related information from engagement-unrelated information and mining difficult samples. This aids in enabling the model to learn features conducive to engagement prediction. In the temporal domain, we calculate the differences between adjacent frames and sum these differences to obtain regions of pixel variation between video frames, thereby generates saliency weight and improving model performance. Experiments on several public datasets demonstrate the proposed model can outperform the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Engagement prediction Fourier transform Frame difference Information entropy

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SafeARUnity: Real-Time image processing to Enhance Privacy Protection in LBARGs 14th

SafeARUnity: Real-Time Image Processing to Enhance Privacy ...

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14th International conference on Videogame Sciences and Arts, VJ 2024

作者： Ribeiro, Tiago Marto, Anabela Gonçalves, Alexandrino Santos, Leonel Rabadão, Carlos de C. Costa, Rogério Luís CIIC ESTG Polytechnic University of Leiria Leiria Portugal

ISBN: (纸本)9783031817120

Augmented Reality applications overlay our physical world with digital components in an interactive 3D space. These applications generally capture information about the physical world around the user through cameras and sensors, which can identify user movements and interactions with objects in the real world. In recent years, Location-Based Augmented Reality Games (LBARGs) have been used in several contexts, such as entertainment, tourism, and education. However, by capturing information about the environment, AR applications can lead to failures in maintaining user and bystander privacy. This paper addresses the identification and protection of sensitive data in LBARGs. We introduce LootAR, a location-based mobile AR game, and the SafeARUnity library, a real-time image processing middleware that acts as a layer between the AR application and the device’s camera, identifying and sanitizing sensitive data prior to rendering. Implementation aspects are discussed, involving Unity Sentis, a toolkit for running machine learning models in Unity, and YOLO, a fast single-stage object detector optimized for real-time applications. We also demonstrate the integration of SafeARUnity in mobile games, using LootAR as a case study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Middleware

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Extraction of Normal and Abnormal Region in Colposcopy image to Support Cervical Cancer Clinical Decision 3rd

Extraction of Normal and Abnormal Region in Colposcopy Image...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Tamang, Parimala Thatal, Annet Gupta, Mousumi Department of Computer Application Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar Sikkim Gangtok India Al-Fahal University Faridabad India

ISBN: (纸本)9783031862984

Colposcope is a device which generates a colposcopic images of the cervix which is vital for detecting cervical neoplasia. Thus, analysis of these images plays a significant role in identifying and diagnosing cervical abnormalities. In this paper, we present a UNET model for automatic segmentation of normal and abnormal regions from colposcopic images. This will be beneficial for swift analysis and minimizes the subjective biases between clinicians. The segmentation works by pre-processing the images, training the UNET model on the set of annotated dataset and finally applying the trained model to predict the normal and abnormal regions. The whole algorithm is tested on Kaggle and ICAR dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Harnessing Elastic Adaptive Learning Network for Low-Light image Enhancement 2nd

Harnessing Elastic Adaptive Learning Network for Low-Light I...

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2nd International conference on Applied Intelligence, ICAI 2024

作者： Xing, Meng Li, Xiaomei Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology NJ Ningbo315201 China Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Medicine Institute for Regenerative Medicine Tongji University Shanghai200123 China Department of Artificial Intelligence Institute Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789819619030

In recent years, curve estimation-based low-light image enhancement methods have gained increasing attention due to they do not need reference images for training. However, existing methods often encounter issues such as overexposure, color distortion, and artifacts when processing images with uneven luminance distribution. To address this issue, this paper proposes an approximate global convergence curve estimation based unsupervised low-light image enhancement method, called Harnessing Elastic Adaptive Learning Network (HEAL-Net). Specifically, we design an Approximate Global Convergence Curve (AGC-Curve) to reduce the model's sensitivity to input variations. Additionally, we introduce a Dynamic Deep Feature Mapping (DDFM) branch, which further smooths the luminance distribution by capturing the correlations between color channels. Experimental results demonstrate that our method achieves advanced performance across multiple benchmarks and exhibits strong robustness to inputs with uneven luminance distribution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mapping

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Intrusion Detection from Surveillance Video Using Frame Based image processing Techniques 1st

Intrusion Detection from Surveillance Video Using Frame Base...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Mohamed Yaseen, M.S. Radeesh, P. Chitra, P. Ramesh, Kishore SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India

ISBN: (纸本)9783031823824

In India, the number of surveillance cameras has increased rapidly, making it challenging for law enforcement to go through the vast amount of recorded footage. We suggest a technique for quickly reviewing large amounts of video feed in order to address this issue. By utilizing sophisticated algorithms such as facial recognition, lightweight YuNet model face detection, and motion detection through background subtraction, our system accelerates the analysis of videos from various camera systems. Our model is a great tool for any agency looking to improve its operational efficiency when handling large amounts of monitoring data because it can quickly identify important events and produce concise summaries with timestamps. This piece of research contributes towards development within this field since such new methods are combined into one single solution thereby advancing surveillance technology while at the same time providing concrete benefits for everyday challenges faced by law enforcement agencies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intrusion detection

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A Comparative Analysis of Nerve Vessels Enhancement and Segmentation Techniques Through Edge Detection on Retinal Fundus images 3rd

A Comparative Analysis of Nerve Vessels Enhancement and Segm...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Durga, N. KeranaHanirex, D. Muthukumaravel, A. Department of Computer Applications Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of MCA Bharath Institute of Higher Education and Research Tamil Nadu Chennai India

ISBN: (纸本)9783031863011

Diagnosing wider disease in retina is more significant in Ophthalmology. Using image processing techniques to identify the retinal diseases has proven more precise than other technology implementations, rapidly. Certain disorder identifications lowers the quality with respect to the limiting capacities and diagnosing them. Irrespective of several algorithms and researches the issue remains unsolved on the retinal disease identifications using image processing. Where huge number of observations turned negative with retinal color images, which in turn formulated an enhanced technique that is solely focuses on the green component of the retinal image. Corona virus spread during the year 2019 is predicted to have allied with the variations in the nerve vessel, that is seen into the partitioned eye fundus with the patients in the process of detecting the variations in the retinal via image processing which is also used to assess the potential correlation over medical ***, the retinal image of the eye is pre-processed which is further processed by identifying the presence of the pictorial particulars using contrast improvisation and histogram equalization, CLAHE. Secondly by removing the noise from the image, using WMF (www mmmm ffff). Upon comparison with filters like median, wiener etc., WMF is proved to be the best from the results obtained in this proposed research work. Finally, retinal images were segmented with the nerve cells in the retinal images of Covid-19 infected individuals using SOBCAN (SSSooobbb ccc aaa nnn) methodology. The fundus images and performance was evaluated based on parameters including MSE, PSNR and SSIM. The results were compared with other technologies like sobel, canny etc., and concludes that the proposed methods’ formulated outcome outperforms the equivalent enhancement method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image correlation

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12th CCF conference on BigData, BigData 2024

12th CCF Conference on BigData, BigData 2024

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12th CCF conference on BigData, BigData 2024

ISBN: (纸本)9789819610235

The proceedings contain 26 papers. The special focus in this conference is on BigData. The topics include: A Multi-dimensional Early Warning Mechanism for Biological Invasions: A Case Study of Vespa Mandarinia;DLP-FR: Learning Predictable Degradation for Robust Blind Face Restoration;A Joint Relation Extraction Model Utilizing Message Passing Mechanism for Feature Fusion to Alleviate FN Problem from Distant Supervision;urban Traffic Management: A Predictive Approach Using Mobile Phone Data;FedPSED: Federated Public Safety Emergency Detection Based on Adaptive Aggregation Strategy;evaluation of Retrieval-Augmented Generation: A Survey;CMNet: Fast Time Series Forecasting Based on Hybrid Convolution-MLP Architecture;a Domain Adaptive Based Reinforcement Learning Algorithm for Job Shop Scheduling Problems;Large-Scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks;Enhancing Interaction Graph of Data Schema and Syntactic Structure with Pre-trained Language Model for Text-to-SQL;spatial Evolution Analysis of the Level of Digital Economy Development in China;predicting Calibrated Conversion Rate of Online Advertising Using a Multi-task Mixture-of-Experts Calibration Model;a Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs;morphological Semantic Ensemble Filtering of Massive Sentence Pairs for Neural Machine Translation;improving Event-Level Financial Sentiment Analysis with Retrieval-Augmented Multipath Chain-of-Thought Prompting;POSRho: Efficient Spearman’s Rho Calculation for Big Data;a Fusion Tuning Method for Named Entity Recognition;Design of AXI Bus-Based IP Core for image processing;automated Clinical Summary Generation via Integrating Structured and Unstructured Data;a Review on Deep Learning for Sequential Recommender Systems: Key Technologies and Directions;TrajUT: Intruder Trajectory Recovery on Utility Tunnel via Video Surveillance S

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Brain Tumor Diagnosis using Deep Learning: A Systematic Review and Meta-analysis of MRI image-Based Studies 3rd

Brain Tumor Diagnosis using Deep Learning: A Systematic Revi...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Dineshbhai, Patel Kruti Maniar, Himanshu Bhagwan Mahavir University Gujarat Surat India

ISBN: (纸本)9783031863042

Traditional methods for the categorization of brain tumors identification and mostly depend on radiologists' time-consuming manual interpretation and subjective. Detecting a brain tumor in its early stages can be quite very difficult for doctors. Traditional methods for the categorization of brain tumors identification and mostly depend on radiologists' time-consuming manual interpretation and subjective. This process can be subjective, complex and laborious. Recent developments in deep learning have greatly improved the automation and accuracy of medical image processing, including the categorization and identification of brain tumors from MRI scans. The study emphasizes how crucial it is to solve MRI image dataset: A variety of datasets, including medical datasets, are hosted by Kaggle, a platform for data science competitions (Kaggle offers datasets related to brain tumors at (https://***/datasets) in MRI image constraints, optimize segmentation algorithms, and incorporate cutting-edge methods like data. MRI pictures may be more susceptible to noise and other environmental disruptions. Doctors find it more challenging to diagnose the tumor and its sources as a result. In order to overcome this, we created a method that uses grayscale image conversion to identify brain tumors from images. To remove noise and other distracting elements from the image, we employ filters. The system's processing will include a preprocessing step for the selected image. To find brain cancers in the MRI pictures, multiple algorithms are applied at the same time. However, the edges of the image may not be crisp and distinct in the early stages of a brain tumor. As a result, to identify the edges in the pictures, we are using image segmentation. We have developed a number of filtering algorithms and segmentation procedures to extract information from photos. Precision can be increased overall with this strategy. © The Author(s), under exclusive license to Springer Nature Switz

关键词： image segmentation

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Early Diagnosis of Glaucoma and Diabetic Retinopathy Using Fundus images Based on Ensemble Approach 1st

Early Diagnosis of Glaucoma and Diabetic Retinopathy Using F...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Parthasarathy, Arjun Vasu, Harini Palani, Durgadevi Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031823886

Detecting eye diseases like Glaucoma and Diabetic Retinopathy early is crucial to prevent irreversible vision loss;these conditions, characterized by optic nerve damage are recognized as leading causes of blindness. Fundus photography involves capturing images of the back of the eye and it plays a crucial role in diagnosing and monitoring various eye conditions, aiding in the early detection and management of these diseases and their subtypes, such as the different stages of glaucoma and diabetic retinopathy. Existing methods for analysing fundus images rely on traditional image processing techniques like filtering and transformation, and classification methods like CNN or SVMs. These methods do not reliably capture data, leading to lower accuracy in identification. In this study, we utilized fundus images from the Fundusimage1000 dataset obtained from Kaggle, which contains a comprehensive collection of images capturing various stages of glaucoma and diabetic retinopathy. This study proposes an ensemble method for glaucoma and diabetic retinopathy detection using fundus images. By utilizing Grey-Level Co-occurrence Matrix for texture analysis, this method integrates classification models XGBoost and Light GBM to make accurate predictions for early identification of the subtypes of these two diseases, including the various stages such as Mild, Moderate, Advanced, Proliferative or Nonproliferative. This combination of texture analysis and ensemble learning yields effective results in automated disease identification. The proposed approach also achieves a high accuracy of 99%, offering a valuable tool for medical professionals to improve diagnostic outcomes and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image texture

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image Splicing Detection: A Deep Learning Based Approach 3rd

Image Splicing Detection: A Deep Learning Based Approach

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Kaushik, Bhavana Jha, Smita Pratap, Vishwa Vinay, K. Ettam, Vinnel The School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

ISBN: (纸本)9783031862922

image splicing refers to the process of manipulating digital images by copying, pasting, or superimposing parts of different images to create a composite image. The resulting image is intended to trick the viewer into believing it is a real photo when in fact it is a manipulated image. image forgery is often used for malicious purposes such as: Fabricating fake news, slandering people, or manipulating evidence in court. There are several methods for detecting fakes in image splicing, including visual inspection, statistical analysis, and Deep learning-based approaches. visual inspection involves manually examining an image to identify discrepancies in lighting, color, texture, and geometry in different parts of the image. Feature Extraction is done from the Casia dataset. The proposed method described below gives an accuracy of 96.59% with an epoch of 30. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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