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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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1st international conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843969

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using Machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing Vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing computer Vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered Machine Vision applications.

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Fault Diagnosis and Region Detection of CCD image in Double Delay Three-Step Autocorrelator

Fault Diagnosis and Region Detection of CCD Image in Double ...

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Pattern Recognition and Machine Learning (PRML), ieee international conference on

作者： Suicheng Li Zizhou He Wenwen Shen Yanwen Xia Zhigao Ni Xiaowei Zhou Department of Computer Engineering and Science Institute of Computer Application CAEP Sichuan Province China Department of Optical instrument Research Center of Laser Fusion CAEP Sichuan Province China

With the development of national optical experimental facilities, countries worldwide have invested significant amounts of funding into constructing various facilities. Among them, the double delay three-step autocorrelator is an important instrument for measuring the ultrashort pulse laser waveform and is frequently used in laser experiments. Because electronic instruments are too slow to measure directly, we can only use CCD images to reconstruct the laser waveform. Therefore, CCD, in particular, plays a vital role in the closed-loop control system of double delay three-step autocorrelator, and CCD image quality and processing can directly affect the measurement results of the pulse laser. In this paper, we propose an image feature-based CCD fault diagnosis and effective region detection method for the double delay three-step autocorrelator. This fault diagnosis method first calculates the pixel sum of each row and analyzes the primary pixel distribution of each frame which could represents the main features of CCD image. Next, we design a fault diagnosis model to classify those features and pick out the suitable image. Finally, we analyze and process the above image and design a boundary detection algorithm to detect the effective region for further waveform recovery algorithm. In addition, to verify performance of the proposed method, we conduct a significant number of experiments. Through qualitative and quantitative analyses of the experimental results, we verify the effectiveness and robustness of the proposed method for CCD fault diagnosis.

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8th international conference on computer Vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th international conference on computer Vision and image processing, CVIP 2023

ISBN: (纸本)9783031585340

The proceedings contain 140 papers. The special focus in this conference is on computer Vision and image processing. The topics include: A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity;MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement;feature Fusion and Multi-head Attention Based Hindi Captioner;Payload Length and Location Identification Using a Novel CNN and Re-embedding Strategy in Stego images Created by Content Adaptive Steganographic Algorithms;ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions;driver Drowsiness Detection Using Vision Transformer;automated Cricket Commentary Generation for Videos;image Captioning with Visual Positional Embedding and Bi-linear Pooling;Cartilage Segmentation from MRI images Towards Prediction of Osteoarthritis;Reinforcement Algorithm-Guided ROI Extraction of Fingerprint Biometric Data;improved Metric Space for Shape Correspondence;an Integrated Approach: Combining GrabCut and Contour-Matching for Hand Gesture Segmentation in Indian Sign Language;identity Preserved Expressive Talking Faces with Synchrony;isolated Sign Language Recognition Using Deep Learning;a Gradient-Based Approach to Interpreting Visual Encoding Models;adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation;Efficient Seizure Prediction from images of EEG Signals Using Convolutional Neural Network;coping with Increased Levels of Label Noise in Facial Expression Recognition;experimental Evaluation of Needle Tip Prediction Using Kalman Filtering Approach;a Deep Face Antispoofing System with Hardware Implementation for Real-Time applications;Bit Plane Segmentation and LBP-Based Coverless Video Steganography for Secure Data Transmission;a Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear images Using

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Detecting Malicious Migration on Edge to Prevent Running Data Leakage

Detecting Malicious Migration on Edge to Prevent Running Dat...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuchen Wong Qingni Shen Cong Li Cunzhan Liu Tianxiang Ai School of Software and Microelectronics & National Engineering Research Center for Software Engineering & PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University

With the popularity of the Internet of Things (IoT) applications, for instance, smart homes and smart medical, edge servers have become increasingly critical infrastructures. Nevertheless, the loose management puts the edge server under the threat of malicious administrators, which causes the leaking risks of the user’s data security. We first give a Data Sniffing Attack that malicious administrators can use live migration to complete without being discovered. To resist the attack, the transparency of live migration to users is the most severe difficulty, where there has not been an effective solution yet. In this paper, we propose a live migration detection model to simulate the migration process, namely observing the indicator values that can obtain without high authorities and calculating the possibility of state transition. Then through many migration experiments, we present the immediate indicators represented by the OS interrupts and the persistent indicators represented by the IO speed, and sort these indicator values into datasets. Next, we train ten frequently-used classifiers and show their accuracy. Eventually, we analyze the advantages and disadvantages of different algorithms in predicting migration and provide the weight recommendation if applied in the detection model.

关键词： Analytical models image edge detection Signal processing algorithms Smart homes Predictive models Prediction algorithms Classification algorithms

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Hybrid Architecture using CNN and LSTM for image Captioning in Hindi Language

Hybrid Architecture using CNN and LSTM for Image Captioning ...

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2022 international conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Poddar, Ayush Kumar Rani, Rajneesh Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

With the advent of deep learning in recent years, the integration of computer vision with natural language processing has garnered a lot of attention. Generating descriptions from images is one of the most intriguing and focused areas of Machine Learning that faces a number of obstacles, particularly when describing images in languages other than English. In image captioning, a computer is trained to understand the visual information of an image and then generate a description based on the image features and reference sentences. Having an application that automatically describes events in their environment and then translates them into a caption or message can make a significant contribution to society. This paper presents a multi-layered CNN-LSTM neural network model that is utilized to recognize and generate Hindi captions for the objects in images. In addition, a variety of models were trained by adjusting hyperparameters and the number of hidden layers to find the optimum model and maximize the likelihood of the resultant Hindi description. Moreover, after testing the effectiveness of our models, it was observed that our model has shown an increase of 34.64% and 29.13% in terms of BLEU score (Unigram) and BLEU score (Bigram) respectively when compared to the existing work done in this field. image captioning in Hindi can have a multitude of applications in today's society, and it can also provide a user-friendly interface for Hindi speakers. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Understanding Data Pre-processing with a Hybrid Diagram Integrating ER and Data Flow Notation

Understanding Data Pre-processing with a Hybrid Diagram Inte...

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ieee international conference on Big Data

作者： Robin Varghese Carlos Ordonez University of Houston USA Department of Computer Science University of Houston Houston TX USA

In a data science project, a significant effort is spent pre-processing databases, text and image data sets, before a machine learning model can be computed. Moreover, the analytic process is iterative, requiring a team of data scientists to add and remove features (attributes) from the target data set going all the way back to data sources. However, such programming effort is carried out without a data model behind, which results in redundant and inconsistent data sets and source code difficult to extend and maintain. On the other hand, the ER model has a proven track record to design databases before developing source code. Heeding the importance of data-centric models to store and analyze data, we propose a hybrid diagram (FLOWER=FLOW+ER) mixing data objects and processing flow, under the CRISP standard. Specifically, our novel diagram mixes ER entities (data objects) and processing steps (function calls). We explain how diagram creation can be partially automated by parsing source code. We present two case studies illustrating how our proposed hybrid diagram works in two challenging problems from biomedical engineering and computer vision: (1) detecting and classifying spikes in biomedical signals and (2) labeling identified objects in images. We argue our diagram can reduce code development time, enhance team collaboration and eliminate redundant data. We hope our work will motivate research bridging database design, project management and machine learning.

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UNeXt: a Low-Dose CT denoising UNet model with the modified ConvNeXt block

UNeXt: a Low-Dose CT denoising UNet model with the modified ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Farzan Niknejad Mazandarani Paul Babyn Javad Alirezaie Department of Electrical and Computer Engineering Toronto Metropolitan University Toronto ON Canada Department of Medical Imaging University of Saskatoon Health Region Royal University Hospital Saskatoon SK Canada

In recent decades, clinicians have widely utilized computed tomography (CT) for medical diagnosis. Medical radiation is potentially hazardous and therefore reducing x-ray radiation in CT scanning is desired. However, decreasing radiation dose leads to increased noise and artifacts. In this paper, low-dose CT images (LDCT) have been denoised in the UNet-based novel architecture of convolutional neural network (CNN) and compared with normal-dose images (NDCT). A multi-feature extraction block (MFEB) is placed to get extra features in the different receptive fields. The modified ConvNeXt block for CT images (CTNeXt) is developed to extract diverse feature data at various scales. Furthermore, we introduced the image reconstruction block to gradually merge the group convolutions’ feature information and eliminate the gap between the features to ease the transmission of multi-scale information from subsequent stages. The network is optimized using the integration of mean-squared error (MSE), mean-absolute error (MAE), and contrastive loss via vgg16-net. These functions show that they could effectively prevent edge over-smoothing, improve image texture, and preserve structural details. A comparative analysis of the proposed network demonstrates that our method outperforms state-of-the-art denoising models, such as Wasserstein Generative Adversarial Network (WGAN-vgg) and Residual Convolutional Encoder-Decoder (RED-CNN).

关键词： Convolution Computed tomography Computational modeling Noise reduction Feature extraction Convolutional neural networks Speech processing

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Make Encoder Great Again in 3D GAN Inversion through Geometry and Occlusion-Aware Encoding

Make Encoder Great Again in 3D GAN Inversion through Geometr...

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international conference on computer Vision (ICCV)

作者： Ziyang Yuan Yiming Zhu Yu Li Hongyu Liu Chun Yuan Tsinghua Shenzhen International Graduate School International Digital Economy Academy (IDEA) Hong Kong University of Science and Technology

3D GAN inversion aims to achieve high reconstruction fidelity and reasonable 3D geometry simultaneously from a single image input. However, existing 3D GAN inversion methods rely on time-consuming optimization for each individual case. In this work, we introduce a novel encoder-based inversion framework based on EG3D, one of the most widely-used 3D GAN models. We leverage the inherent properties of EG3D’s latent space to design a discriminator and a background depth regularization. This enables us to train a geometry-aware encoder capable of converting the input image into corresponding latent code. Additionally, we explore the feature space of EG3D and develop an adaptive refinement stage that improves the representation ability of features in EG3D to enhance the recovery of fine-grained textural details. Finally, we propose an occlusion-aware fusion operation to prevent distortion in unobserved regions. Our method achieves impressive results comparable to optimization-based methods while operating up to 500 times faster. Our framework is well-suited for applications such as semantic editing.

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The Intelligent Investigating K-Nearest Neighbors for image Segmentation

The Intelligent Investigating K-Nearest Neighbors for Image ...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), ieee international conference on

作者： Pallavi Prasad Jamsandekar Saket Mishra S. Hemelatha Prasanta Kumar Parida Evelyn Alina Anicama Navarrete Docente Elio Javier Huamán Flores Docente Department of Computer Applications Bharati Vidyapeeth (Deemed to be University) Institute of Management and Rural Development Administration Sangli Maharashtra India Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Rural Management KIIT University Patia Bhubaneswar Odisha India Universitario Ica Universidad Autónoma de Ica Ica Peruana

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

The sensible Investigating ok-Nearest buddies for photograph Segmentation (MIKKNS) is a brand new method for the automated photograph segmentation task. It combines the traditional and ok-nearest friends (KNN) set of rules with contemporary intelligent algorithms, which include deep learning, to deal with the challenges of guide segmentation in scientific imaging. It combines the two to enhance the segmentation accuracy even with fewer schooling parameters. Similarly, MIKKNS introduces a singular studying framework that uses a convolutional neural network (CNN) for characteristic extraction and the KNN to produce an optimized segmentation. Empirical opinions reveal that the proposed method outperforms traditional strategies by up to two. 3% in segmentation overall performance.

关键词： Deep learning image segmentation Clustering algorithms Imaging Convolutional neural networks image preprocessing Task analysis

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), ieee international conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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