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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Fusion of Independent and Interactive Features for Human-Object Interaction Detection

Fusion of Independent and Interactive Features for Human-Obj...

引用

IEEE International conference on image processing

作者： Zehai Wu Lijie Sheng Songnian Zhang Qiguang Miao School of Computer Science and Technology Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Human-Object Interaction (HOI) detection, which aims to identify humans and objects with interactive behaviors in images and predict the behaviors between them, is of great significance for semantic understanding. The existing works primarily focus on exploring the fine-grained semantic features of humans and objects, as well as the spatial relationships between them. However, these methods do not leverage the contextual information within the interaction area, which could potentially be valuable for predicting interaction behavior. To investigate the impact of contextual information on behavior prediction, we propose a novel approach to extract both independent and interactive features and fuse them. Specifically, our method is capable of extracting interaction features from the interaction region. These features are then merged with fine-grained independent features of humans and objects. Finally, the fused features are utilized to predict interaction behavior. In addition, the feature fusion module does not add extra storage and computation costs to our method. Experiments demonstrate the effectiveness of our method, achieving state-of-the-art performance on two benchmark HOI datasets, namely HCO-DET and V-COCO.

关键词： Accuracy Costs Fuses Convolution Semantics Feature extraction Prediction algorithms

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DOMAIN ADAPTATION IN image DEHAZING: EXPLORING THE USAGE OF imageS FROM VIRTUAL SCENARIOS 16

DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF ...

引用

16th International conference on Computer Graphics, Visualization, Computer Vision and image processing, CGVCVIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information systems, MCCSIS 2022

作者： Sappa, Angel D. Suárez, Patricia L. Velesaca, Henry O. Carpio, Darío Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km. 30.5 P.O. Box 09 Vía Perimetral 01-5863 Ecuador Computer Vision Center Campus UAB Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789898704429

This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator;these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way haze removal algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy. © MCCSIS *** rights reserved.

关键词： Deep learning

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Copy-Move image Forgery Detection using Deep Learning Approaches

Copy-Move Image Forgery Detection using Deep Learning Approa...

引用

Software, systems and Information Technology (SSITCON), International conference on

作者： Zainab Shawkat Ibrahim Taha Mohammed Hasan Department of Computer Science College of Science University of Diyala Iraq

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The advancement of digital image processing software has reached a stage where it is effortless to manufacture forgeries by using numerous manipulating approaches on authentic photos. Occupations such as law, healthcare, and education are susceptible to manipulated imagery. Therefore, it is crucial to ascertain the authenticity of an image. Single type of image manipulation is identified as ‘copy move,’ when significant item or objects are concealed by duplicating and inserting them into the original image. It is crucial to determine whether an image is genuine. The current techniques employed to identify image forgeries relied on conventional feature extraction algorithms, such as key point and block-based algorithms. These conventional approaches yield a subpar outcome. Deep learning approaches have established superior performance in image processing applications. This paper presents a highly effective method for identifying instances of copy-move forgery in images using a hybrid approach that combines deep learning (DL) such as (VGG19 and ResNet50) for feature extraction and Machine Learning (ML) algorithms including (Random Forests (RF) and Support Vector Machine (SVM)) to classify digital images. The proposed models (VGG19-RF), (VGG19-SVM), (ResNet50-RF), and (ResNet50-SVM) have been compared using two standard datasets. The results indicate that ((VGG19-RF) achieves higher recall, accuracy, precision and F1-score of 97% on MICC-F220 dataset, On the other hand (VGG19-RF) and (ResNet50-RF) achieve higher recall, precision, accuracy and F1-score of 97% on the MICC-F2000 dataset.

关键词： Support vector machines Deep learning Radio frequency Accuracy Digital images Feature extraction Software systems Forgery Standards Residual neural networks

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Video Mosaic of Arbitrary Viewing Direction Oriented on das

Video Mosaic of Arbitrary Viewing Direction Oriented on das

引用

2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Liu, Jinwei Wei, Baoguo Zhou, Jiaming Cai, Mingzhi Northwestern Polytechnical University School of Electronic Information Xi'an China

ISBN: (纸本)9781665429184

DAS (Distributed Apertures Sensors) system can acquire panoramic images from a distributed number of sensors, whose performance is mainly determined by the stitching algorithm. In DAS, the direction angle between sensors is large. Usually, rather than the whole panorama, vision with arbitrary viewing direction is required. In addition, low power consumption and high real-Time stitching are expected in DAS. In this paper, we propose a stitching method for images with large capturing angle intervals, which can provide panorama with arbitrary observation direction. First, we project the captured image using cylinder model to deduce the feature difference between large capturing directions. Then the feature points are matched, projected back to original image, and screened out. Finally the distance parameter of stitching is estimated using the matched points, and the panorama of arbitrary observe direction is stitched. Additionally, we implemented the algorithm using FPGA, which is suitable for deployment in DAS systems with less computational resources. Experiment results suggest that the proposed algorithm improves the frame rate significantly (43.4fps) with no significant difference between the stitching effect of this paper and the comparison algorithm. © 2021 IEEE.

关键词： image sensors image registration Power demand Fitting Signal processing algorithms Signal processing Surface fitting

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FPGA-based Improved Canny Edge Detection System

FPGA-based Improved Canny Edge Detection System

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Zhang Huan Wang Yaxin Wang Shuang Liu Zhihao Chen Panyu Wang Fangjuan School of Electronic Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In order to address the issues associated with the classic Canny operator, such as high computational load, incomplete edge information capture, and poor real-time performance, an improved multi-directional Canny edge detection method is proposed for using in service robot navigation systems. This method collects image information through the OV5640 camera, preprocesses the image after grayscale processing combined with mixed multiple filtering, and then performs eight directional adaptive threshold edge detection based on the classical Canny operator. After image enhancement, it runs on the FPGA chip. It can be seen from the experimental results that compared with the classic edge detection algorithm, the improved Canny operator has significantly improved the detection effect and can efficiently process the edges of various complex images in real time, providing a feasible solution for real-time image processing in the FPGA field.

关键词： Thresholding (Imaging) Service robots Navigation image edge detection Robot vision systems Noise Filtering algorithms Real-time systems Field programmable gate arrays image enhancement

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A Simple and Effective Deep Registration Algorithm for SAR images

A Simple and Effective Deep Registration Algorithm for SAR I...

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IEEE International conference on Information Communication and Signal processing (ICICSP)

作者： Hui Fang Qingping Meng Guisheng Liao Yongjun Liu Cao Zeng Zhuochen Chen National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Synthetic aperture radar (SAR) image registration contributes significantly to various applications of SAR images. Conventional scale invariant feature transform (SIFT) alignment methods possess abysmal robustness for SAR image registration resulting from speckle noise in SAR images. Attributing to the excellent feature representation ability of convolutional neural networks (CNN), a simple and effective deep registration algorithm for SAR images is proposed in this paper. Specifically, we construct a CNN to extract the features of SAR images and to accomplish the feature matching, while outputting the transformation matrix for SAR image registration directly. Furthermore, a data generation strategy is adopted to solve the problem of insufficient training data during the training process. Experiment results on the real data verify the effectiveness of the proposed method.

关键词： Training Training data Signal processing algorithms Transforms Data collection Speckle Feature extraction Radar polarimetry Convolutional neural networks Synthetic aperture radar

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Semantic Traffic Element Removal by Learning to Follow Linguistic Instructions

Semantic Traffic Element Removal by Learning to Follow Lingu...

引用

IEEE International conference on Unmanned systems (ICUS)

作者： xiaodan Feng Yuanqi Su Yuehu Liu Chi Zhang School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China

We propose a novel task of semantic object removal in traffic scenes through human-guided image editing. Unlike traditional methods, our model does not require image masks; instead, it effectively removes semantic elements from a traffic scene image based solely on an input image and linguistic instruction. To achieve this objective, we use large-scale pre-trained models to construct a traffic scene image dataset suitable for human-guided image editing. We train a text-guided image translation model on this dataset. During inference, our model quickly generalizes to photorealistic traffic scene images and user-written instructions, obviating the need for fine-tuning and enabling rapid removal of semantic object elements from images within seconds. Experimental results demonstrate that our model achieves satisfying removal performance in the traffic scene semantic object removal task, showing the potential advantages of text guidance in image processing tasks.

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image Captioning based on Deep Convolutional Neural Networks and LSTM 2

Image Captioning based on Deep Convolutional Neural Networks...

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2nd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2022

作者： Srivastava, Swati Sharma, Himanshu Dixit, Pragati GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665432153

image captioning is a challenging task that needs the knowledge from both computer vision algorithms and language processing techniques. The model must be able to understand an image and then apply language generation techniques to describe an image in a natural language such as English. In this paper, we have presented an image captioning model which uses VGG16 for visual feature extraction and LSTM model to generate sentences corresponding to extracted visual features. We have performed experiments on Flickr8k and Flickr30k datasets. Bilingual Evaluation Understudy (BLEU) metric is used to measure the accuracy of the proposed model. The proposed model can be further extended to wide range of applications related to IOT based applications and smart control systems. © 2022 IEEE.

关键词： Computer vision

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A NOVEL GROUND-TRACK FMCW SAR IMAGING METHOD BASED ON MODIFIED WAVENUMBER DOMAIN ALGORITHM

A NOVEL GROUND-TRACK FMCW SAR IMAGING METHOD BASED ON MODIFI...

引用

IET International Radar conference 2023, IRC 2023

作者： Ling, Qing Ma, Jingtao Huang, Penghui xi, Peili Zhao, Chanjuan Lu, Qing Jiang, Yan Wang, Haitao Liu, xingzhao School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Innovation Academy for Microsatellites of CAS Shanghai China Shanghai Institute of Satellite Engineering Shanghai China Shanghai Aerospace Electronics Technology Institute Shanghai China

ISBN: (纸本)9781839539954

The frequency modulated continuous wave synthetic aperture radar (FMCW SAR) is widely utilised on miniaturized platforms due to its light weight and cost-effectiveness. However, the imaging algorithms for pulse radars may not function well for the FMCW SAR. To deal with this issue, this paper proposes a novel FMCW SAR imaging method based on the modified wavenumber domain algorithm, where the Doppler frequency shift caused by radar motion during the frequency sweep period is compensated firstly. Then, a modified range wavenumber axis scaling transformation is performed, which can effectively reserve the non-stationary phase information, so as to improve the subsequent azimuth focusing performance. Finally, a well-focused SAR image can be obtained after azimuth matched filtering. The simulation results of point targets and the real-measured ground-track FMCW SAR data simultaneously verify the effectiveness and feasibility of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Multimodal Fusion of Beidou Navigation Signal in Urban Navigation Services

Multimodal Fusion of Beidou Navigation Signal in Urban Navig...

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IEEE International conference on Power, Intelligent Computing and systems (ICPICS)

作者： Juan Wang Li Zhang Yifeng He Xi'an Railway Vocational & Technical Institute Xi'an China China Academy of Space Technology Xi'an China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Navigation systems play an important role in urban navigation services, but traditional Beidou navigation signals still face problems such as low positioning accuracy and poor navigation stability in multimodal fusion. This article aimed to promote the improvement of positioning accuracy by exploring the multimodal fusion of Beidou navigation signals in urban navigation services, in order to better meet the needs of urban navigation services. Firstly, the signal data of Beidou Navigation Satellite was collected in the article; then, through steps such as data preprocessing, error correction, and data editing, the Beidou navigation signal data processing was achieved, improving the accuracy of navigation; afterwards, the Beidou navigation signal was combined with a one-dimensional structure, and multimodal fusion of the navigation signal was achieved through concatenation, resulting in the final classification result; finally, to verify the effectiveness of Beidou navigation using the algorithm proposed in this article for signal fusion in urban navigation services, it was compared with traditional algorithms. Through experiments, it was found that the average positioning accuracy of the Beidou navigation system for 15 positions after signal fusion using the algorithm proposed in this article could reach 94.25%, while the traditional algorithm only had 82.52%. The study has demonstrated the effectiveness of multimodal fusion of Beidou navigation signals, providing more ideas for promoting the construction of urban smart transportation and urban management.

关键词： Accuracy Satellites Urban areas Data preprocessing Switches Satellite navigation systems Stability analysis User experience Classification algorithms Smart transportation

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