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检索条件"任意字段=4th International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2024"

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Research on combined preprocessing algorithms for detection of center circle in circular coded target image used in ART 4

Research on combined preprocessing algorithms for detection ...

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4th international conference on signal image processing and Communication, ICSIPC 2024

作者： Wu, Qingdong Wang, Zhiqiang Xu, Yao Yan, Jun Liu, Zhaohui Shandong Luqiao Group Company Ltd. Jinan250014 China College of Transportation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510682467

the corded target is an indispensable part in the study of machine vision-based measurement technology, and each coded value corresponds to a different unique value, which makes it be of an important application in image recognition. Since a center circle of the circular coded target can provide precise positioning coordinate, this paper carries out the research on the combined preprocessing algorithm for detecting the center circle of circular coded target images in order to improve the recognition efficiency and accuracy of the center circle of circular coded target image of Autonomous Rail Rapid Transit(ART). Firstly, the color images captured by field experiment are gray-scale processing and smoothed by Gaussian filtering;secondly, Canny edge detection is performed on the images to accurately extract the edge features of the target images. Finally, an improved Hough transform method is used to identify the center circle contour of the extracted edges. the experimental results show that the algorithm not only improves the recognition efficiency, but also ensures the accuracy of recognition. © 2024 SPIE.

关键词： Hough transforms

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Alzheimer's disease MRI image recognition method based on deep learning 4

Alzheimer's disease MRI image recognition method based on de...

引用

4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Jiahao, Huang College of Mechanical and Electronic Engineering Nanjing Forestry University Jiangsu Nanjing China

ISBN: (纸本)9798331528676

Globally, the incidence of Alzheimer's disease is increasing. Although there are many diagnostic methods, traditional methods often rely on professional knowledge and consume much time. therefore, there is an urgent need to introduce automated technology to improve diagnostic efficiency and accuracy. this study used three deep learning models, namely convolutional neural network (CNN), ResNet, and VGGNet. It optimized them according to data characteristics for Alzheimer's MRI image recognition, providing a reference for diagnosing Alzheimer's disease. this study used the dataset from Dataset Alzheimer, based on the data preprocessing of Alzheimer's Disease Neuroimaging Initiative (ADNI). the model was trained after the dataset was processed by data augmentation, pixel value scaling and MOTE oversampling. the experimental results indicated that the VGGNet model attained the highest overall accuracy at 76%, and the CNN and ResNet models reached accuracies of 75% and 74%, respectively. Nonetheless, the model's proficiency in reliably identifying categories with limited samples necessitates enhancement. Furthermore, further analysis is necessary to assess the model's generalisation potential and its relevance in clinical environments. Future research may explore more advanced model architectures, incorporating attention processes and generative adversarial networks (GANs), to enhance the model's recognition capabilities and its suitability for therapeutic applications. ©2024 IEEE.

关键词： Adversarial machine learning

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Lightweight multi-branch feature fusion network for multimodal medical image fusion 4

Lightweight multi-branch feature fusion network for multimod...

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4th international conference on advanced algorithms and Neural Networks, AANN 2024

作者： Shao, Yu Yu, Lei Chongqing Normal University College of Computer and Information Science Chongqing China

ISBN: (纸本)9781510686106

Most existing medical image fusion methods, which are based on deep learning achieve satisfactory results by using complex network architectures and stacking numerous modules. However, these methods often overlook the application scenarios of multimodal medical image fusion. the complex model structure and large parameter load make deployment on mobile devices extremely challenging. Moreover, it is unreasonable to consume substantial computational resources at the low-level image processing stage if the method is to be applied to downstream computational tasks. We have innovatively designed a lightweight multi-branch feature fusion network for multimodal medical image fusion. this method has a lower parameter count and extremely fast forward inference speed. this is due to our designed multi-branch feature channel segmentation method, which divide-and-conquer feature extraction with different receptive fields. We also use a channel attention mechanism based on contrast awareness to fuse and reduce the dimensionality of the feature maps, preserving ample source image information while reducing computational load. Finally, the fusion image reconstruction is completed through a sliding window attention mechanism combined with long-range feature dependencies. © 2024 SPIE.

关键词： image reconstruction

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Stereo Matching Algorithm Based on Improved Census Transform and Adaptive Window Optimization 4

Stereo Matching Algorithm Based on Improved Census Transform...

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4th international conference on Computer Science and Blockchain, CCSB 2024

作者： Li, Tongle Huang, Zucheng He, Lile Wang, Weijun Yuan, Hai Wang, Jian Guangzhou Institute of Advanced Technology Robot and Intelligent Equipment Center Guangdong Guangzhou511458 China School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Shaanxi Xi'an710311 China

ISBN: (纸本)9798350356502

To address the issue of insufficient accuracy in local stereo matching algorithms, this paper proposes an improved stereo matching algorithm that integrates an enhanced Census transform with adaptive window optimization. Initially, by utilizing interpolated information from the mean of the central pixel along with the maximum and minimum pixel values, the encoding of the Census transform is augmented, thereby increasing the utilization of neighboring pixels and reducing the impact of noise. Subsequently, a region-based partitioning method, grounded in color similarity, is introduced. this method sets thresholds according to different region characteristics to define appropriate arm length conditions, thereby optimizing the aggregation process in low-texture and enhancing matching accuracy in such areas. Finally, disparity maps are computed and optimized using the derived disparity values. On the standard image set of the Middlebury evaluation platform, the proposed algorithm achieved an average error rate of 3.95%, demonstrating a significant improvement in matching accuracy compared to existing high-performing local stereo matching algorithms. © 2024 IEEE.

关键词： Stereo image processing

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AI-Based image Analysis for Early Disease Detection in Medical Imaging 4

AI-Based Image Analysis for Early Disease Detection in Medic...

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4th international conference on Computer, Communication, Control and Information Technology, C3IT 2024

作者： Dwivedi, Pulkit Rattan, Nikita Kajal, Mansi School of Computer Science & Engineering Iilm University Greater Noida India Chandigarh University India K. R. Mangalam University Gurugram India

ISBN: (纸本)9798350389128

Early disease detection and accurate segmentation in medical imaging, particularly using MRI scans, are critical for effective treatment and patient survival. this research focuses on developing a robust system for early disease detection and segmentation utilizing machine learning and deep learning techniques. the Disease Doc 2019 dataset from Kaggle was employed to train and test various models, including Support Vector Machines (SVM), Naive Bayes, and Random Forests. Our methodology involves a comprehensive process starting from data collection and pre-processing to feature extraction, classification, and evaluation. the implementation of a GUI-based interface allows users to upload MRI images, detect diseases, and view results seamlessly. the models demonstrated high performance with metrics such as accuracy, precision, recall, F-score, Sorensen-Dice index, Jaccard index, and specificity. this study underscores the potential of advanced machine learning algorithms in enhancing the accuracy and efficiency of medical image analysis, thereby supporting clinical decision-making and improving patient outcomes. © 2024 IEEE.

关键词： Support vector machines

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image Feature Extraction with Fourier Transform and Multi-task Swin-Transformer for Alzheimer's Disease Prediction and Detection 4

Image Feature Extraction with Fourier Transform and Multi-ta...

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4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Hu, Chengli Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798331528676

Alzheimer's disease (AD) patients often miss the optimal treatment time due to their late onset and unclear early clinical manifestations. there is little difference between the CT images of the brain of AD patients and normal people, and the efficiency of manual diagnosis is low. However, classic models (such as Resnet, Swin Transformer, CNN, etc.) do not have strong targeting for brain disease diagnosis, and they are more inclined towards general classification tasks, so there is great room for improving the accuracy of the model. this article (FTM Swin) is based on a learnable mask mechanism with algorithm combined with SVM and swin-transformer, to filter and extract features in the frequency domain graph of 3D images. Fast Fourier transform (FFT) is used to achieve the interaction and combination of frequency domain and spatial domain feature matrices, ultimately completing the classification task of data points in the time domain, which simplifies the feature extraction process and makes the classification boundary of the classifier more accurate. Subsequently, this article utilized the ADNI brain imaging dataset and trained the previous classifier model using a dataset annotated with the hippocampus region, which plays a crucial role in Alzheimer's disease lesions. this further enhanced the model's ability to detect lesion sites and provided information on the stage of the condition. Compared with the other eight advanced models for image classification tasks in the past three years, FTM-Swin has an accuracy of 97.18% on the validation set, with the best performance. ©2024 IEEE.

关键词： Neurodegenerative diseases

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Fourth international conference on signal processing and Machine Learning, CONF-SPML 2024

Fourth International Conference on Signal Processing and Mac...

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4th international conference on signal processing and Machine Learning, CONF-SPML 2024

ISBN: (纸本)9781510674721

the proceedings contain 28 papers. the topics discussed include: phototropic bionics: realization of intelligent machine detection and obstacle avoidance;a study of model predictive control and reinforcement learning control system;advancements and challenges in speech emotion recognition: a comprehensive review;revolutionizing ADHD diagnosis: deep learning in 3D medical imaging;improving robustness in emotion recognition via adversarial training;real image improvement study based on pivotal tuning inversion;a review of 3D printing slicing algorithms;and analysis of two variants of U-net for pulmonary nodule segmentation: attention U-net and dense-attention U-net.

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Time Based image Synthesis: Light and Color Conversion Using Machine Learning Techniques 4

Time Based Image Synthesis: Light and Color Conversion Using...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Labh, Jyoti Ranjan Dwivedi, R.K. College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350387490

the progress made in camera technology and computational imaging has been astounding when it comes to photography. A specialized neural image signal processor is currently used by many top smartphone cameras and DSLR to convert chaotic raw sensor photos into the final, polished output. A sizable dataset of image pairs-one with a noisy, short-exposure raw picture and another with a clean, long-exposure ground-truth raw image-is needed to train these neural ISPs for low-light, processing It takes a lot of effort and time to capture these aligned image pairs, and motion blur in the long-exposure ground truth frequently makes the process difficult. Artificial nighttime datasets can be used to train various image processing algorithms and neural ISPs that are more resilient and effective. In order to overcome this difficulty, we proposed a model that uses to convert image colour and lighting of a raw picture to artificially create realistic image of different time zone raw photos, brighter photos are typically easier to take, have less noise, and are less likely to suffer from motion blur. the objective of the suggested processing framework is to transform raw photographs taken during the different time into simulated images of different timing with different levels of noise and other low-light features. More ease and flexibility in training neural ISPs for time-based rendering is made possible by this artificial time- based picture dataset. Additionally, in our proposed method the performance of the trained neural ISPs can resemble that of models trained only on real different timings photos if they mix their synthetic data with a tiny percentage of real data. the potential to produce accurate raw photos of midnight and different night time in a synthetic manner holds significant implications for computational photography and image processing applications and even object recognition under difficult nocturnal conditions. © 2024 IEEE.

关键词： Color photography

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Skin Cancer Diagnosis Using image processing and Xception-CNN Model 4

Skin Cancer Diagnosis Using Image Processing and Xception-CN...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sunitha, R. Pankaja, R. Pavithra, H.C. Kumar, Abhilasha P. Mohanesh, B.M. B N M Institute of Technology Department of Ai & Ml Bangalore India

ISBN: (纸本)9798350352931

Skin cancer is recognized as one of the most lethal forms of cancer, often resulting in death if not detected early. Its aggressive tendency to spread to other parts of the body makes treatment in later stages particularly difficult, emphasizing the crucial need for early detection. As the incidence of skin cancer continues to rise, along with its associated high mortality rates and treatment expenses, there is an urgent demand for more effective early detection methods. Leveraging deep learning algorithms through a model-driven architecture has emerged as a promising solution for improving skin cancer diagnosis. In this work, present the development and implementation of a deep learning model designed to accurately classify images of skin lesions. the proposed approach uses the Xception model, an advanced convolutional neural network (CNN) architecture. through training, the Xception model identifies patterns and features in skin lesion images that indicate whether the condition is benign or malignant. Proposed model achieved an accuracy rate of 86% in the classification task. © 2024 IEEE.

关键词： Convolutional neural networks

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Learnable upsampling bilateral grid refinement for stereo matching network 4

Learnable upsampling bilateral grid refinement for stereo ma...

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4th international conference on advanced algorithms and Neural Networks, AANN 2024

作者： Qiu, Wenfeng Ma, Chihao Li, Jianhua Qian, Ren Zhao, Yong College of Computer Science and Technology Guizhou University Guiyang550025 China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen518055 China College of Artificial Intelligence and Big data Luzhou Vocational and Technical College Sichuan Luzhou646000 China

ISBN: (纸本)9781510686106

Most existing binocular stereo matching algorithms require a trade-off between accuracy and speed, unable to achieve both simultaneously. One reason lies in the complexity and variability of scenes that stereo matching tasks must handle, where disparities in heavily textured, weakly textured, and occluded areas are often difficult to infer correctly. therefore, this paper proposes the Learnable Upsampling Bilateral Grid Refinement for Stereo Matching Network (LUGNet). through learnable bilateral grid upsampling guided by the left image, LUGNet calculates offsets for cost volume upsampling, while simultaneously leveraging the network to automatically learn interpolation weights to accommodate features of different datasets. Ultimately, LUGNet achieves error rates comparable to high-precision networks with a parameter count of 2.6M and an inference time of 58ms. © 2024 SPIE.

关键词： Stereo image processing

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