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检索条件"任意字段=3rd International Conference on Image and Signal Processing"

共 14223 条记录，以下是31-40 订阅

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Enhancing the Generalization of Synthetic image Detection Models through the Exploration of Features in Deep Detection Models 13

Enhancing the Generalization of Synthetic Image Detection Mo...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Javaheri, Alireza Hajabdollah Motamednia, Hossein Mahmoudi-Azanveh, Ahmad Shahid Beheshti Univ Cyberspace Res Inst Tehran Iran Inst Res Fundamental Sci Sch Comp Sci High Performance Comp Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

One of the major challenges of AI is the misuse of images generated by generative models. Advances in this field have reached a point where distinguishing between real and fake images can be impossible for humans and challenging even for machines. Although significant work has been done on detecting fake images, there is an ongoing competition between content generation and detection methods. However, a significant challenge for detection methods is their limitation to content generated by specific models. This study aims to enhance the generalization of fake image detection methods. Experimental results indicate that modifications made to the base model have contributed to improving its generalizability.

关键词： Synthetic image Detection image Forensics GANs Diffusion Models CNN

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Weighted Features Based Classification of Polarimetric SAR images 13

Weighted Features Based Classification of Polarimetric SAR I...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Saneipour, Fatemeh Imani, Maryam Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Today, classification of polarimetric images is an important topic where various statistical pattern recognition methods have been used to achieve the high accurate classification maps. In this work, weighting the polarimetric features according to their statistical behavior (the mean vector and variance values as the first and second statistics) is suggested to improve the PolSAR image classification. A weighted feature matrix is composed and applied to the popular classifiers such as maximum likelihood, K-nearest neighbor and support vector machine. The weighted feature matrix can be also implemented on other arbitrary classifiers to improve their discrimination ability. The experiments on the L-band AIRSAR dataset show appropriate classification results.

关键词： polarimetric synthetic aperture radar (PolSAR) weighted features classification

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Residual Convolutional Neural Network With Autoencoder Based Attention For PolSAR image Classification 13

Residual Convolutional Neural Network With Autoencoder Based...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Imani, Maryam Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

The polarimetric synthetic aperture radar (PolSAR) images contain fine characteristics and abstract spatial features, which attention to them can improve the classification accuracy. In this work, the residual convolutional neural network with autoencoder based attention (RCNN-AA) is proposed for PolSAR image classification. The scaled difference of the convolutional autoencoder with the original input patch is used as the weight, which contains information about the fine spatial features. Multiplication of this normalized difference in the input patch provides the attention feature maps that can be concatenated with the original input and used as input of the RCNN. An ablation study is done, and also, the proposed RCNN-AA model is compared to some deep learning based models. The results show preference of the RCNN-AA with respect to the competitors.

关键词： autoencoder convolutional neutral network (CNN) residual learning PolSAR classification

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AI-ENABLED ANALYSIS OF 3-D CT SCANS FOR DIAGNOSIS OF COVID-19 & ITS SEVERITY

AI-ENABLED ANALYSIS OF 3-D CT SCANS FOR DIAGNOSIS OF COVID-1...

引用

IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kollias, Dimitrios Arsenos, Anastasios Kollias, Stefanos Queen Mary Univ London London England Natl Tech Univ Athens Athens Greece

ISBN: (纸本)9798350302615

This paper describes the 3rd COVID-19 Competition, taking place in the AI-enabled medical image analysis (AIMIA) Workshop of the 2023 IEEE international conference on Acoustics, Speech and signal processing (ICASSP 2023). The 3rd COVID-19 Competition is a continuation of the Competitions held at ECCV 2022 and ICCV 2021 conferences, and aims to tackle the challenges of whole slide image and CT/MRI/X-ray analysis/processing and to identify research opportunities in the context of Digital Pathology and Radiology/COVID19. The 3rd COVID-19 Competition consists of two Challenges targeting COVID19 detection and COVID19 severity detection. Both Challenges are based on an extended version of the database used in the 1st and 2nd COV19D Competitions, the COV19-CT-DB database, which includes chest CT scan series. A large part of the COV19-CT-DB database is annotated for COVID-19 detection and consists of 8,000 3-D CT scans. About 1,000 3-D CT scans of the database are also annotated with respect to four COVID-19 severity conditions. Both parts have been split in training, validation and test datasets. These are used for training and validation of machine learning models, as well as for evaluation. The paper further describes the baseline methods for the 3rd COVID-19 Competition, which are deep learning approaches, based on CNN-RNN networks. Their performance on detecting the existence and the severity of COVID-19 is reported.

关键词： COVID-19 Competition AI-MIA COVID-19 detection COVID-19 severity COV19-CT-DB

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Unsupervised Classification of Remotely Sensed High resolution images using RP-CNN 13

Unsupervised Classification of Remotely Sensed High resoluti...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Fallahreyhani, Maedeh Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Fac Elect Image Proc & Informat Analysis Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

In recent years, various deep learning frameworks have been developed for the classification of remotely sensed images. However, the network models proposed in these frameworks exhibit high complexity and do not yield high classification accuracy when applied to unlabeled scenarios. This paper introduces a Multi spectral image (MSI) classification approach that combines the random patches network with self-supervised branch (RPSS) to extract informative deep features. The proposed method involves convolving image bands with random patches to obtain multi-level deep features. Subsequently, we use panchromatic image (PAN) to extract spatial features. The MS spectral features, the derived RPSS features and spatial features then merged to classify the MSI using a support vector machine (SVM) classifier. The experimental results on real remotely sensed images have been presented.

关键词： Deep learning Multi spectral data Unsupervised classification

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Adaptive Blind Watermarking Using Psychovisual image Features 13

Adaptive Blind Watermarking Using Psychovisual Image Feature...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： PariZanganeh, Arezoo Ghorbanzadeh, Ghazaleh ShahreBabak, Zahra Nabizadeh Karimi, Nader Samavi, Shadrokh Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9798350350494;9798350350500

With the growth of editing and sharing images through the internet, the importance of protecting the images' authority has increased. Robust watermarking is a known approach to maintain copyright protection. Robustness and imperceptibility are two factors that are tried to be maximized through watermarking. Usually, there is a trade-off between these two parameters. Increasing the robustness would lessen the imperceptibility of the watermarking. This paper proposes an adaptive method that determines the strength of the watermark embedding in different parts of the cover image regarding its texture and brightness. Adaptive embedding increases the robustness while preserving the quality of the watermarked image. Experimental results also show that the proposed method can effectively reconstruct the embedded payload in different kinds of common watermarking attacks. Our proposed method has shown good performance compared to a recent technique.

关键词： robust watermarking algorithms digital cosine transform (DCT) digital wavelet transform (DWT)

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Spatial-Spectral Neural Network for High Resolution Multispectral image Classification 13

Spatial-Spectral Neural Network for High Resolution Multispe...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Tanha, Roozbeh Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Classification of multispectral images in remote-sensing area having the capability to analyze and categorize diversified land cover. In this issue, extracting suitable spatial, spectral and even temporal features is one of the main challenges. Also, the existence of sufficient data required for the classification training process is another challenge, because in many cases it may not be available and we may not even have a reliable classification map. The use of neural networks for simultaneous feature extraction and classification is very popular and significant progress has been made in this field, but these networks usually have a high computational cost and require significant training data in the training process. In this work we propose a neural network for multispectral image classification purpose which requires few training samples and less calculation without using filterbanks for spatial feature extraction and it can improve classification accuracy by fusion of spatial and spectral features. The simulations indicate that the proposed method shows an acceptable performance.

关键词： Remote sensing multispectral image classification feature extraction neural network

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A Burst signal Detection Method Based on Probabilistic Spectral Intensity Matrix Strain Features 24

A Burst Signal Detection Method Based on Probabilistic Spect...

引用

3rd international conference on Networks, Communications and Information Technology (CNCIT)

作者： Ma, Ding Kun Jiang, Tong Ding, Zong Jie Ma, Yan Ding, Yi Wang, Su Jun Ping, Yi Fan China Acad Space Technol Xian Xian Peoples R China

ISBN: (纸本)9798400717048

Burst signals have recently become a critical factor in radiation source processing, and the action of detecting and capturing is one of the most important factors in resolving this issue. The article proposes an innovative method of burst signal detection with a probabilistic spectral intensity matrix, which accumulates the probabilistic features of the signal for some time. Strain features corresponding with burst signal can be extracted to capture the desired signal. The proposed method provides a novel way to solve the problems of burst signal detection and has an excellent success rate. Experiment results show the effectiveness of the method in burst signal stable capture, offering significant value and bright prospects for resolving the growing burst signal detection in the radiation source processing field.

关键词： radiation source processing burst signal probabilistic spectral intensity matrix strain features detection and capture

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MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification 13

MaskChanger: A Transformer-Based Model Tailoring Change Dete...

引用

13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Ebrahimzadeh, Mohammad Manzuri, Mohammad Taghi Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated change interpretation challenging. We propose MaskChanger - a novel deep learning paradigm tailored for satellite image change detection. Our method adapts the segmentation-specialized Mask2Former architecture by incorporating Siamese networks to extract features separately from bi-temporal images, while retaining the original mask transformer decoder. To our knowledge, this is the first study in which change detection is converted from the existing per-pixel classification approach into a mask classification approach. Evaluated on the LEVIR-CD benchmark of over 600 very high-resolution image pairs exhibiting real-world rural and urban changes, MaskChanger achieves F1-Score of 91.96%, outperforming prior transformer-based change detection approaches.

关键词： Change Detection Remote Sensing image Transformer

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Analyzing different loss functions for Single image Super-Resolution 13

Analyzing different loss functions for Single Image Super-Re...

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13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Shojaei, Shakiba Mahmoudi-Aznaveh, Ahmad Shahid Beheshti Univ Cyberspace Res Inst Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Single image Super-Resolution (SISR), which aims to recover a high-resolution (HR) image from a low-resolution (LR) one, is an ill-posed problem. Convolutional Neural Networks (CNNs) have been used in low-level vision tasks such as Super-Resolution (SR), and inspired by impressive results in high-level tasks. By choosing the proper structure, the methods can be improved significantly. In this case, selecting an appropriate loss function is essential for any deep learning task, especially in SISR. The exploited loss function impacts the quality of the images produced by the SISR algorithms. Some loss functions can make the output image look blurred or unnatural, which goes against the purpose of SR. To ensure that the output image retains the content of the original photo while also improving the structure and texture, it is essential to choose a loss that is well suited for the task. In this paper, various loss functions for SISR are reviewed. Then, we present an overall analysis of loss functions for SISR based on our exploration.

关键词： super-resolution loss function image quality assessment feature extraction

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