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检索条件"任意字段=Iranian Conference on Machine Vision and Image Processing"

共 11454 条记录，以下是21-30 订阅

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Proceedings - 2023 International conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

Proceedings - 2023 International Conference on Machine Visio...

引用

2023 International conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

ISBN: (纸本)9798350306545

The proceedings contain 47 papers. The topics discussed include: Tri-PANet: triplanar ensemble network with parallelly aggregating global and local information for brain tumor segmentation;an object-based extremely weak supervised learning paradigm: case study in classification of crops using UVA images;abdominal multi-organ segmentation based on dual self-attention module;Res2NetFuse: a novel Res2Net-based fusion method for infrared and visible images;D2-LRR a dual-decomposed MDLatLRR approach for medical image fusion;a dual-polarization feature enhancement network for SAR ship detection;a lightweight reconstruction network for surface defect inspection;a method for determining optimal leaf picking amount of litchi based on YOLOv7;and a progressive optimization method for image alignment of cameras with different focal lengths.

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An Improved U-Net image Segmentation Network for Crankshaft Surface Defect Detection 13

An Improved U-Net Image Segmentation Network for Crankshaft ...

引用

13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Moosavian, Ashkan Bagheri, Elmira Yazdanijoo, Alireza Barshooi, Amir Hossein Tech & Vocat Univ TVU Dept Agr Engn Tehran Iran Iran Univ Sci & Technol IUST Sch Automot Engn Tehran Iran Irankhodro Powertrain Co IPCo Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Crankshaft is one of the mechanical components of the vehicle engine, and quality control of it holds significant importance in the production line. In this paper, a vision-based system was developed to detect apparent structural defects on the crankshaft surface. By examining the different approaches in computer vision tasks, the semantic segmentation technique was chosen to solve this problem. In the first stage, a dataset consisting of 400 crankshaft experimental images with structural defects such as scratch, pitting, and grinding were collected. Then, the Convolutional Neural Network (CNN) with MobileNet architecture was trained to detect apparent defects, and an Intersection over Union (IoU) evaluation criteria of 64.7% was obtained. In the third stage, some image processing techniques were used to increase the performance. By applying the DexiNed edge detection filter on the train-set images, the IoU was increased by 8.4%. Considering the importance of this issue in the automotive industry, it has been tried again to boost the performance by augmenting the dataset images. On the other hand, this can also prevent overfitting of the model. By training the model under the same conditions as the previous stages, the IoU in this stage increased by 13.2% and reached 86.3%.

关键词： defect detection crankshaft deep learning segmentation pitting scratch

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Enhancing Weakly Supervised Semantic Segmentation through Patch-Based Refinement 13

Enhancing Weakly Supervised Semantic Segmentation through Pa...

引用

13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Tajrishi, Narges Javid Afshar, Sepehr Amini Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Weakly-Supervised Semantic Segmentation (WSSS) with image-level labels, commonly uses Class Activation Maps (CAM) to generate pseudo-labels. However, Convolutional Neural Networks (CNNs), with their limited local receptive field, often struggle to identify entire object regions. Recently, the vision Transformer (ViT) architecture has been employed instead of CNNs to capture long-range feature dependencies, by using the self-attention mechanism. Despite its advantages, ViT tends to overlook local feature details, leading to attention maps with low quality and unclear object details. This paper introduces a novel method to enhance the local details in attention maps by leveraging local patches. These local patches are selected from regions that are more likely to contain the desired objects. By effectively utilizing these local patches during the training and generation stages, the model yields more detailed attention maps. Extensive experiments were conducted on the PASCAL VOC 2012 benchmark dataset to demonstrate the efficacy of the proposed approach. The results show significant improvements (+2.6% mIoU) with minimal computational overhead, underscoring the potential of the proposed method in the field of Weakly-Supervised Semantic Segmentation.

关键词： Weakly-supervised Semantic Segmentation image Classification Deep Learning vision Transformer

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Enhancing Multi-view Mammography image Classification: By using Breast Region Extraction Method and Statistical Features 13

Enhancing Multi-view Mammography Image Classification: By us...

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13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Bidabadi, Neda Shirany Mahmoudzadeh, Elham Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9798350350494;9798350350500

Breast cancer is one of the most dangerous diseases among women. Different methods are used to diagnose this cancer that among these, imaging and computer-aided systems are more common. In these systems, one of the most important step is preprocessing and removing unnecessary areas of the images, as well as extracting the chest area. In this paper, we present a method that consists of preprocessing, feature extraction, and using a machine learning classifier. In the preprocessing step, we propose a method to extract the region of interest in both angles of mammography images. The proposed novel method includes applying gamma correction thresholding to the images and obtaining two binary images based on the proposed threshold using the Otsu method. Results show the proposed method successfully removes the chest muscle with 98% accuracy. In the next, for feature extraction phase, we utilize three different methods for extracting features. Finally, by employing an Extra tree model classifier, we classify mammography images into normal and abnormal. By incorporating the block-based feature extraction method, we achieve 98% accuracy in classification. Overall, our approach demonstrates the effectiveness of preprocessing and feature extraction for diagnosing breast cancer using mammography images.

关键词： Mammography image Region of Interest Extraction Breast Cancer Statistical Feature Extraction

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Highresolution Remote Sensing image Classification With Limited Training Data 13

Highresolution Remote Sensing Image Classification With Limi...

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13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Ariaei, Mehdi Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Accurate classification of land cover from aerial images is one of the research topics in remote sensing and is also in high demand in industry. However, obtaining labeled data for training different classifiers that heavily depend on supervision is still a challenging and resource-intensive task. Unsupervised methods have emerged as a powerful alternative to overcome the limitations associated with labeled data. Such methods have a high ability to discover hidden patterns and structures in multi-spectral images and have the possibility of classifying various types of land cover without relying on labeled samples. Our research primarily involved the analysis of World-View3 satellite imagery. Our strategy involved creating an advanced pipeline that extracted features using autoencoders. Through this approach, the multi-spectral images' key characteristics are efficiently extracted. Subsequently, we implement transfer learning to re-train the model with a limited number of labeled data. By applying transfer learning, our pipeline significantly enhances the capability of multispectral image processing, enabling a more comprehensive and accurate interpretation of satellite imagery data. Finally, we evaluate our results not only by providing a confusion matrix but also through a visual comparison between the class map and the RGB composition of the MSI image.

关键词： World-View3 Autoencoders Multi-Spectral images Deep Learning Transfer Learning Limited Training Data

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Ovarian Tumor Ultrasound image Segmentation with Deep Neural Networks 13

Ovarian Tumor Ultrasound Image Segmentation with Deep Neural...

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13th iranian/3rd International machine vision and image processing conference (MVIP)

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

ISBN: (纸本)9798350350494;9798350350500

The precise and automated segmentation of ovarian tumors in medical images plays a pivotal role in the treatment of ovarian cancer in women. U-Net has demonstrated remarkable success in the field of medical image segmentation. However, due to its small receptive field, U-Net faces challenges in extracting global context information. Moreover, due to the significant variation in scale and size among tumors, it is essential to employ a network capable of effectively extracting information at Multiple scales. In this study, we present a U-Net-based network named PCU-Net for the segmentation of ovarian tumors, incorporating ConvMixer and Pyramid Dilated Convolution (PDC) modules. The ConvMixer module captures global context information by utilizing large-size kernels. The PDC module integrates local and global contextual patterns through utilization of parallel dilated convolution with different dilation rate. Furthermore, our model has fewer parameters than U-Net. We assess the proposed method's performance using the Multi-Modality Ovarian Tumor Ultrasound (MMOTU) dataset. The results indicate that in comparison to U-Net, our proposed PCU-Net exhibits an improvement of 4.23% in terms of Intersection over Union (IoU) and 2.99% in terms of Dice Similarity Coefficient (DSC).

关键词： Segmentation Ultrasound images U-Net ConvMixer Pyramid Dilated Convolution

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Cultural-Aware AI Model for Emotion Recognition 13

Cultural-Aware AI Model for Emotion Recognition

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13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Baradaran, Mehrdad Zohari, Payam Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Gorgin, Saeid Shahid Beheshti Univ Dept Comp & Data Sci Tehran Iran Khaje Nasir Univ Technol Dept Comp Engn Tehran Iran Inst Res Fundamental Sci Sch Comp Sci High Performance Comp Lab Tehran Iran Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

ISBN: (纸本)9798350350494;9798350350500

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multi-modal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

关键词： Emotion Recognition DeepFeature Extraction image Representation Text Representation

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Spectral-Spatial Anomaly Detection in Hyperspectral imagery Based on Dual-Domain Autoencoders 13

Spectral-Spatial Anomaly Detection in Hyperspectral Imagery ...

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13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Aghili, Mohamad Ebrahim Ghassemian, Hassan Arani, Maryam Imani Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly detection technique called Dual-Domain Autoencoders (DDA) is proposed to address these challenges. First, Nonnegative Matrix Factorization (NMF) is applied to decompose the hyperspectral data into anomaly and background components. Refinement of the designation is then done using intersection masking. Next, a spectral autoencoder is trained on identified background signature pixels and used to reconstruct the image. The reconstruction error highlights spectral anomalies. Furthermore, a spatial autoencoder is trained on principal component patches from likely background areas. Fused reconstruction error from the spectral and spatial autoencoders is finally used to give enhanced anomaly detection. Experiments demonstrate higher AUC for DDA over individual autoencoders and benchmark methods. The integration of matrix factorization and dual-domain, fused autoencoders thus provides superior anomaly identification. Spatial modeling further constrains the background, enabling accurate flagging of unusual local hyperspectral patterns. This study provides the effectiveness of employing autoencoders trained on intelligently sampled hyperspectral pixel signatures and spatial features for improved spectral-spatial anomaly detection.

关键词： hyperspectral image anomaly detection autoencoder matrix factorization spectral-spatial features

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Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation 13

Leveraging Swin Transformer for Local-to-Global Weakly Super...

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13th iranian/3rd International machine vision and image processing conference (MVIP)

作者： Ahmadi, Rozhan Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudolabels from class activation maps (CAMs). Due to the localized pattern detection of Convolutional Neural Networks (CNNs), CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://***/RozhanAhmadi/SWTformer

关键词： Weakly Supervised Semantic Segmentation Class Activation Map Hierarchical vision Transformer image-level label

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IPMV 2023 - Proceedings of 2023 5th International conference on image processing and machine vision

IPMV 2023 - Proceedings of 2023 5th International Conference...

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5th International conference on image processing and machine vision, IPMV 2023

ISBN: (纸本)9781450397926

The proceedings contain 16 papers. The topics discussed include: performance evaluation of recent object detection models for traffic safety applications on edge;tracking of artillery shell using optical flow;action recognition with non-uniform key frame selector;a view direction-driven approach for automatic room mapping in mixed reality;automatic gait gender classification using convolutional neural networks;deep 3D-2D convolutional neural networks combined with Mobinenetv2 for hyperspectral image classification;attention based BiGRU-2DCNN with hunger game search technique for low-resource document-level sentiment classification;strategies of multi-step-ahead forecasting for chaotic time series using autoencoder and LSTM neural networks: a comparative study;semi-supervised defect segmentation with uncertainty-aware pseudo-labels from multi-branch network;and security analysis of visual based share authentication and algorithms for invalid shares generation in malicious model.

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