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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

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Building Detection Using Very High Resolution SAR images with Multi-Direction Based on Weighted-Morphological Indexes 12

Building Detection Using Very High Resolution SAR Images wit...

引用

12th Iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Amjadipour, Fateme Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Tehran Iran

ISBN: (纸本)9781665412162

Today, technological advancement in production of radar images can be seen with high spatial resolution and also the availability of these images' significant growth in interpretation and processing of high-resolution radar images. The building extraction from urban areas is one of the most challenging applications in VHR SAR image, which is used to estimate the population and urban development. Detection of individual buildings in the urban context is highly considered by researchers due to complexity of interpreting radar images in these fields. On the other hand, one of the main issues in the complexity of the scatters received from buildings is change in direction of the building relative to the horizon, which is correlated with the look angle. Other influential parameters are geometric distortions, which include layover and shadow effects. In some cases, the effect of shadow is an auxiliary parameter in detection of these targets that increases accuracy of the detection. In this paper, we intend to extract the building from high spatial resolution SAR images using fuzzy fusion of two morphological indicators, SI and DI, which represent the shadow and bright area, respectively. Due to the effect of SAR imaging geometry on ground targets, different sizes and directions of structural elements were applied to the image. The use of indicators weights with different sizes is proposed in this work. The Detection Ratio of experiment of TerraSAR-X image has a result of 95.3%. © 2022 IEEE.

关键词： Morphology

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Design of vision-guided Gripping System for 6DOF Robots Combined with Dexterous Hands 7

Design of Vision-guided Gripping System for 6DOF Robots Comb...

引用

7th International conference on Robotics, Control and Automation Engineering, RCAE 2024

作者： Wang, Chengwen Wan, Guoyang Li, Hanqi Li, Xuna Zheng, Da Teng, Mingyao Anhui University of Engineering Dept. School of Electrical Engineering Wuhu China

ISBN: (纸本)9798350355642

In the robot application system incorporating dexterous hand, a vision-based robot grasping system is proposed to address the lack of robustness of dexterous hand in grasping fixed attitude objects. First, a 6DOF robot grasping system based on machine vision is constructed using dexterous hand, depth camera and 6DOF collaborative robot, which realizes accurate grasping under vision guidance;second, to solve the problem of vision system's poor localization accuracy due to the loss of image information and features caused by image noise, occlusion and complex background in the process of image processing, a pooling layer and attention mechanism to enhance the feature extraction ability;moreover, an optimized dexterous hand grasping strategy is proposed through exhaustive grasping action design and analysis, which effectively improves the robustness of the system. The experimental results show that the accuracy of the target detection model reaches 87% through the localization measurement of the experimental objects, which is 2.1% higher than the original method, and the grasping success rate of the robotic system equipped with dexterous hand and depth camera is improved by 3.5%. These results validate the feasibility of the robotic grasping system incorporating dexterous hands in practical applications and significantly enhance the robustness of the system. © 2024 IEEE.

关键词： Collaborative robots

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Spatial Quality Assessment of Pansharpened images Based on Gray Level Co-Occurrence Matrix 12

Spatial Quality Assessment of Pansharpened Images Based on G...

引用

12th Iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Aghapour Maleki, Shiva Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Assessing the quality of pansharpened images is a critical issue in order to obtain a quantitative score to represent the quality and compare the performance of different fusion methods. Most of the introduced metrics for pansharpened image quality assessment, evaluate the spectral content of the image, while in different applications of remote sensing like detection and identification of image objects, spatial quality has an important role. In the current study, a new index for spatial quality assessment is introduced that extracts gray level co-occurrence matrix (GLCM) from distorted and reference images and compares the similarities of these features. The tempere image database 2013 (TID2013) that provides reference and different types of distorted images with subjective scores of each image is used as the desired database. To solve the high computational complexity of obtaining GLCM features, the fast GLCM method is employed. In this way, 16 different features are extracted. To select the features that have the most consistency with the human visual system (HVS), the forward floating search method is used as a feature selection method and five features are obtained as the final features to form the desired index. Experimental results show the efficiency of the proposed method in determining the spatial quality of fused images compared with that of the available quality assessment metrics. © 2022 IEEE.

关键词： Remote sensing

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applications of Superpixels on Phase Contrast Microscopy 5

Applications of Superpixels on Phase Contrast Microscopy

引用

5th International conference on Transdisciplinary AI, TransAI 2023

作者： On, Vincent Zahedi, Atena Phandthong, Rattapol California State University Bakersfield Department of Computer and Electrical Engineering and Computer Science BakersfieldCA United States Stem Cell Research Center University of California Irvine IrvineCA United States University of California Riverside Department of Molecular Cell and Systems Biology RiversideCA United States

ISBN: (纸本)9798350358018

Cellular microscopy is enhanced by computational paradigms such as image processing, computer vision, and machine learning. image segmentation is vital for quantifying cell images, enabling tracking and subsequent applications. Superpixel algorithms offer a promising solution to challenges in phase contrast image segmentation, as they group pixels with similar characteristics into meaningful regions. By preserving essential structural information and reducing complexity, superpixels improve accuracy in cell-related measurements and facilitate the study of dynamic cellular processes. This paper presents a comparison of popular superpixel algorithms applied to cellular microscopy, highlighting their contributions to image analysis and interpretation in cell biology research. © 2023 IEEE.

关键词： image segmentation

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Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible images

Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrare...

引用

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

作者： Song, Xu Xiao, Yongbiao Li, Hui Wu, Xiao-Jun Sun, Jun Palade, Vasile Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Coventry University Faculty of Engineering Environment and Computing Conventry United Kingdom

ISBN: (纸本)9798350306545

The fusion of visible light and infrared images has garnered significant attention in the field of imaging due to its pivotal role in various applications, including surveillance, remote sensing, and medical imaging. Therefore, this paper introduces a novel fusion framework using Res2Net architecture, capturing features across diverse receptive fields and scales for effective extraction of global and local features. Our methodology is structured into three fundamental components: the first part involves the Res2Net-based encoder, followed by the second part, which encompasses the fusion layer, and finally, the third part, which comprises the decoder. The encoder based on Res2Net is utilized for extracting multi-scale features from the input image. Simultaneously, with a single image as input, we introduce a pioneering training strategy tailored for a Res2Net-based encoder. We further enhance the fusion process with a novel strategy based on the attention model, ensuring precise reconstruction by the decoder for the fused image. Experimental results unequivocally showcase our method's unparalleled fusion performance, surpassing existing techniques, as evidenced by rigorous subjective and objective evaluations. © 2023 IEEE.

关键词： image fusion

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RNCE: A New image Segmentation Approach

RNCE: A New Image Segmentation Approach

引用

International conference on Computer vision and machine Intelligence, CVMI 2022

作者： Kumar, Vikash Ali, Asfak Chaudhuri, Sheli Sinha Electronics and Telecommunication Engineering West Bengal Jadavpur Kolkata700032 India

ISBN: (纸本)9789811978661

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://***/vekash2021/***. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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MFAAnet: New Feature Extraction Network in image Super-Resolution 20th

MFAAnet: New Feature Extraction Network in Image Super-Resol...

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20th International conference on Intelligent Computing (ICIC)

作者： Wang, Ningzhi Yu, Zhenda Li, Zerui Qi, Zhenyu Lv, Wenjun Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei Peoples R China Univ Sci & Technol China Dept Automat Hefei Peoples R China Hefei Zhongke Liheng Intelligent Technol Co Ltd Hefei Peoples R China

ISBN: (纸本)9789819756025;9789819756032

image super-resolution reconstruction constitutes an indispensable aspect within the realm of computer vision, and the efficacy of super-resolution reconstruction is contingent upon the methodology employed. In response to the limitations of current Single image Super-Resolution (SISR) methodologies, particularly in feature extraction capabilities, we introduce a novel solution: the Multiscale Feature And Attention Network (MFAAnet). MFAAnet operates across multiple scales to enable comprehensive feature exploitation. Our model, devoid of dimensionality reduction in its spatial attention mechanism, acquires discriminative channel representations and enhances pixel-level attention for effective high-level feature image processing. By conducting ablation experiments and comparative studies, we demonstrate the effectiveness and correctness of our design, showcasing significant improvements in image super-resolution tasks compared to baseline models. The proposed MFAAnet architecture comprises three main parts: shallow and deep feature extraction modules, a Super-Resolution Reconstruction Block (SUPB), and convolution for final image generation. Our model stands out for its high performance feature extraction, attention mechanisms, and multi-scale capabilities in enhancing the performance of image super-resolution reconstruction techniques.

关键词： image Super-resolution Efficient Multi-scale Attention Multi-Features Extraction Deep Convolution Network

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SHAPE DETECTION IN AN image USING PARALLELIZED TRADITIONAL image ANALYSIS TECHNIQUES 58

SHAPE DETECTION IN AN IMAGE USING PARALLELIZED TRADITIONAL I...

引用

58th Annual International Telemetering conference, ITC 2023

作者： Loreto Cornídez, Alan Manuel Fuentes Gutiérrez, Rubén Diego College of Electrical and Computer Engineering The University of Arizona TucsonAZ85721 United States

Modern day computer vision applications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate training data. Due to a lack of adequate training data, the Arizona Autonomous Vehicles Club (AZA) decided to implement the generalized hough transform to detect shapes in a live video feed from an unmanned aerial system (UAS). The hough transform is computationally intensive and since real-time performance is required, a serial approach may not have the execution speed necessary for the application. image processing techniques include matrix multiplication and convolution operations which are highly parallelizable. Therefore, the algorithm was parallelized and implemented on a graphics processing unit (GPU). Performance profiling was done on both machine learning and traditional approaches where execution time and accuracy were compared. © 2023 International Foundation for Telemetering. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Detection and Segmentation of Grape Bunch by Integrating Channel Attention and Large Kernel Attention 2

Detection and Segmentation of Grape Bunch by Integrating Cha...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Huang, Zhitao Li, Guo Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350331417

Detecting and segmenting fruits in an orchard environment is a vital technique in multiple applications of precision agriculture, such as automated harvesting and yield estimation. This study aims to improve the accuracy and robustness of detectors for detecting and segmenting fruits on trees by integrating detection and segmentation models based on channel and large kernel attention. The proposed Triplet-Large Kernel Attention (TLKA) module inherits the advantages of channel and large kernel attention. It was integrated with YOLOv7 to achieve real-time object detection and segmentation. Several experiments were conducted to verify the effectiveness of the proposed TLKA module. These included comparative attention mechanisms from small to large input image scales, comparative analysis of different attention mechanisms through Grad-CAM visualization, and test experiments with integrated comparisons on mid-term fruits (immature, intermediate, and mature) under three different light conditions (morning, noon, and afternoon). The proposed TLKA module achieved higher accuracy than comparative attention at different input scales. Finally, the proposed model was used to predict the yield of both grapes. The TLKA-YOLOv7 outperformed all other investigated models in terms of grape bunch detection and segmentation and obtained more competitive results in yield prediction. © 2023 IEEE.

关键词： attention mechanism component computer vision deep learning detection YOLO

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ChangeSAM: Adapting the Segment Anything Model to Street Scene image Change Detection 27

ChangeSAM: Adapting the Segment Anything Model to Street Sce...

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27th IEEE International conference on Intelligent Transportation Systems, ITSC 2024

作者： Bauer, Adrian Krabbe, Jan-Christoph Kummert, Anton University of Wuppertal Germany

ISBN: (纸本)9798331505929

In the dynamic field of machine learning, foundation models have recently gained prominence, particularly for their application in natural language processing and computer vision. The foundational Segment Anything Model (SAM), known for its interactive image segmentation via prompts, serves as the basis for this study. We introduce ChangeSAM, a tailored adaptation of SAM for street scene image change detection (CD). ChangeSAM utilizes the versatility and vast knowledge of SAM, adapting it to effectively identify semantic changes in image pairs. Two architectural adaptations are introduced - Pre Decoder Fusion (PreDF) and Post Decoder Fusion (PostDF) - enabling ChangeSAM to process dual images. Enhancements through Prompt Tuning and Low-Rank Adaptation (LoRA) are integrated, achieving a balance between reusability, computational efficiency, and accuracy. Our evaluation on the VL-CMU-CD dataset shows that with minimal parameter adjustments, ChangeSAM achieves accuracy on par with fully fine-tuned models. This work contributes to the ongoing development of foundation models in practical applications, illustrating the viability and potential of adaptable, efficient models in scenarios with limited computational resources, such as intelligent vehicles. © 2024 IEEE.

关键词： Semantics

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