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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

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An Efficient image Retrieval Model with Convolutional Neural Network based Text/image Identification for Copyright Violation Detection 30

An Efficient Image Retrieval Model with Convolutional Neural...

引用

30th IEEE Signal processing and communications Applications conference (SIU)

作者： Ozden, Harun Tavli, Bulent Demirci, M. Fatih Anadolu Ajansu Iletisim Teknol Direktorlugu Ankara Turkey TOBB Ekon Teknol Univ Elekt Elekt Muhendisligi Ankara Turkey Ankara Yildirim Beyazit Univ Bilgisayar Muhendishgi B Ankara Turkey

ISBN: (纸本)9781665450928

One of the most important problems faced by broadcasters is the unauthorized use of their images by third parties or organizations in a large-scale database, which contains hundreds of thousands of images. For this reason, it is important to perform an efficient and effective image retrieval, whose objective is to find the most similar images to a given test image. In addition, test images often contain text, and the presence of the text together with the visual part complicates the search process. In this paper, we present an image retrieval framework based on a bag of visual words, which has been shown to be effective in the literature. A convolutional neural network model is used to parse the text in the images. Experiments demonstrate the efficacy of this model in a large database.

关键词： Bag of visual Words SIFT Convolutional Neural Network image Retrieval

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Multi-information Aggregation Network for Fundus image Quality Assessment

Multi-information Aggregation Network for Fundus Image Quali...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Yuan Yue, Guanghui Duan, Lvyin Wu, Honglv Wang, Tianfu Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen 518060 Peoples R China

ISBN: (纸本)9781665475921

Fundus image quality assessment (IQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists. Existing fundus IQA methods mainly explore local information to consider local distortions from convolutional neural networks (CNNs), yet ignoring global distortions. In this paper, we propose a novel multi-information aggregation network, termed MA-Net, for fundus IQA by extracting both local and global information. Specifically, MA-Net adopts an asymmetric dual-branch structure. For an input image, it uses the ResNet50 and vision transformer (ViT) to obtain the local and global representations from the upper and lower branches, respectively. In addition, MA-Net separately feed different images into the two branches to rank their quality for supplementing the feature representations. Thanks to the exploration of intra- and inter-class information between images, our MA-Net is competent for the fundus IQA task. Experiment results on the EyeQ dataset show that our MA-Net outperforms the baselines (i.e., ResNet50 and ViT) by 3.06% and 7.61% in Acc, and is superior to the mainstream methods.

关键词： fundus image quality assessment multi-task visual transformer convolutional neural network

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A Secure Network Coding based image communications in Two-Hop Wireless Relay Networks

A Secure Network Coding based Image Communications in Two-Ho...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Vien, Quoc-Tuan Nguyen, Tuan T. Middlesex University Faculty of Science and Technology United Kingdom University of Greenwich School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350359855

This paper investigates the image communications in two-hop wireless relay networks (TH-WRNs) where a source node sends images to a destination node with the assistance of a relay node via two hops, i.e. source-To-relay and relay-To-destination links. Due to the broadcast nature of wireless media, there exists an eavesdropper who tries to overhear and recover the images. Aiming to enhance the security and also to save transmission bandwidth of the image communications, we propose a secure relaying transmission (SRT) protocol by exploiting both random linear network coding (RLNC) and image super-resolution (ISR) techniques. In the proposed protocol, the original high-resolution (HR) images are downscaled at the source node and the RLNC is employed at both the source and relay nodes to conceal the original images from the eavesdropper. The RLNC decoding and ISR are adopted at the destination node to decode and recover the HR images, while the eavesdropper cannot decode the images due to the unawareness of the coefficient matrices and the reference images in the RLNC. It is shown that the proposed SRT protocol achieves a significantly higher performance at the destination than at the eavesdropper. Furthermore, with high-quality relaying hops, the SRT protocol outperforms the secure direct transmission (SDT) protocol with only a direct link between the source and the destination nodes. Finally, simulation results are provided to verify the findings. © 2023 IEEE.

关键词： Decoding

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Attention Unveiled: Revolutionizing image Captioning through visual Attention

Attention Unveiled: Revolutionizing Image Captioning through...

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2023 IEEE Global conference on Information Technologies and communications, GCITC 2023

作者： Kolla, Teja Vashisth, Harsh Kumar Kaur, Manpreet Manav Rachna University Computer Science Department Faridabad India

ISBN: (纸本)9798350308167

image captioning models are a type of "Natural Language processing"(NLP) models that are designed to generate textual descriptions of images. These models are trained on large datasets of images and captions, and use a combination of deep learning models and natural language processing techniques to generate accurate and informative captions. These models have proven to be effective in improving the quality of the generated captions. In recent years, researchers have also been exploring the use of reinforcement learning, adversarial training, and other techniques to improve the performance of image captioning models. Specifically, techniques like Reinforcement Learning from Human Feedback (RLHF), where human-provided captions guide model training, Generative Adversarial Networks (GANs), which generate captions through a competition between a generator and discriminator network, and Self-Critical Sequence Training (SCST), which optimizes model performance based on its own generated captions, have gained attention. These approaches aim to enhance the quality and relevance of captions generated by image captioning models. visual attention models use a transformer and attention models typically consist of two main components: an image encoder that extracts features from the image and a language decoder that generates the textual description. This paper focuses on techniques that can be used for image Captioning using visual attention models. © 2023 IEEE.

关键词： CNN GRU image captioning NLP Transformers

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ADAPTIVE CAMOUFLAGE PATTERN GENERATION TO DIFFERENT ENVIRONMENTS VIA CONTENT-AWARE STYLE TRANSFER 30

ADAPTIVE CAMOUFLAGE PATTERN GENERATION TO DIFFERENT ENVIRONM...

引用

30th IEEE International conference on image processing (ICIP)

作者： Kim, Min-jae Kwon, Subin Ahn, Byung-hyun Ha, Euntaek Choi, Yeonhee Paik, Joonki Chung Ang Univ Dept Image Seoul 06974 South Korea Chung Ang Univ Dept Artificial Intelligence Seoul 06974 South Korea Chung Ang Univ Def AI Core Talent Program Seoul 06974 South Korea Republ Korea Army Gyeryong South Korea Republ Korea Navy Gyeryong South Korea

ISBN: (纸本)9781728198354

visual camouflage is an effective means of protecting valuable assets that are vulnerable to theft, espionage, or other forms of malicious activity. To overcome the limitation of standardized camouflage patterns in certain environments, we need an innovative approach that adapts the camouflage pattern to the specific surroundings of the asset to be concealed. In this paper, we present a novel camouflage image generation method whose results change by the circumstances around the asset to be concealed using the style transfer. In order to remove the influence of stuff and objects that are not advantageous for camouflage in the surrounding, we propose a novel mechanism that introduces the contents-aware information into the calculation of style representation. Experimental results in various situations, including the snowy natural scene, show that the proposed method provides excellent adaptive camouflage outcomes while effectively suppressing conspicuous elements in the surrounding.

关键词： Camouflage Style transfer Convolutional neural networks (CNNs) Panoptic segmentation

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Accuracy Improvement of Depth Map Estimation from Multi-View images Using NeRF

Accuracy Improvement of Depth Map Estimation from Multi-View...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Ito, Shintaro Miura, Kanta Ito, Koichi Aoki, Takafumi Tohoku University Graduate School of Information Sciences Sendai9808579 Japan

ISBN: (纸本)9798350359855

In this paper, we propose a method to improve the accuracy of depth map estimation from multi-view images using Neural Radiance Fields (NeRF). A depth map can be estimated from multi-view images using Multi-View Stereo (MVS), which can estimates the depths inside objects with high accuracy, while NeRF can estimate the depths at object boundaries with high accuracy. We consider using both advantages to improve the accuracy of depth map estimation from multi-view images and making NeRF as a refinement module. Through a set of experiments using a public MVS dataset, we demonstrate the effectiveness of the proposed method compared to conventional depth map estimation methods. © 2023 IEEE.

关键词： image enhancement

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Deep Neural Networks for Efficient image Caption Generation 3rd

Deep Neural Networks for Efficient Image Caption Generation

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3rd International conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Rai, Riddhi Guruprasad, Navya Shimoga Tumuluru, Shreya Sindhu Ramaiah Inst Technol Bangalore Karnataka India

ISBN: (纸本)9783031640636;9783031640643

In the era of rapidly advancing technology, the integration of computer vision and natural language processing has emerged as a pivotal area of research, with deep learning playing a central role. The task of generating descriptive textual captions for images is known as image captioning. It is necessary for enhancing accessibility, aiding visually impaired individuals, and improving human-computer interaction by providing meaningful context to visual content. Generating relevant descriptions for high-level image semantics involves not just recognizing objects and scenes but also analyzing the state, attributes, and relationships among them. This research paper investigates the synergy of Convolutional Neural Networks (CNNs) for effective image feature extraction and Long Short-Term Memory (LSTM) networks for capturing sequential dependencies in generating descriptive and coherent textual captions. It has been demonstrated that it can produce precise and contextually relevant descriptions for a variety of images.

关键词： image Captioning Deep Learning CNN LSTM

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Effect of latency on social presence in traditional video conference and VR conference: A comparative study

Effect of latency on social presence in traditional video co...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Wang, Shengnan Song, Jiarun Trioux, Anthony Wang, Miao Gao, Yusong Yang, Fuzheng Xidian University School of Telecommunications Engineering Xi'an China

ISBN: (纸本)9798350359855

Virtual reality (VR) conference, as a typical social VR application, has gained popularity in recent years. It offers users located at different locations a fully immersive experience and a sense of togetherness. However, the remote communication also introduces inevitable latencies, which may adversely affect the so-called social presence. There is still a lack of research on the effect of latency on social presence. To fill the gap, this paper aims to examine the impact of latency on social presence of VR conference and contrast it with that of traditional video conference. Here, the social presence is measured using the Networked Minds Social Presence Inventory (NMSPI). We design and conduct two conversation-based subjective tests for both types of conference and compare the impact of the latency based on the test results. The conclusions of these studies can be used as guidelines for VR service providers to optimize their conference systems. © 2023 IEEE.

关键词： Virtual reality

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhu, Xun Xu, Mengqiu Wu, Ming Zhang, Chuang Zhang, Bin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665475921

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18%, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69%, respectively.

关键词： sea fog recognition weakly supervised learning point annotation semantic segmentation remote sensing image

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Multi-Direction Strategy with Honey Badger Algorithm and Convolutional Neural Network for Industrial Robot visual Servo image processing

Multi-Direction Strategy with Honey Badger Algorithm and Con...

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2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Chen, Hao Jilin Communications Polytechnic Deppartment of Engineering & Mechanics Changchun China

ISBN: (纸本)9798350360660

The Industrial robot visual servo image processing requires highly autonomous and intelligence robotic manipulators, with goal of performing manipulation tasks independently without human interventions. However, limit efficiency for large scale, sensitive to noise in input data which affect classification accuracy. This paper, proposed Multi-Direction Strategy with Honey Badger Algorithm (MHBA) and Convolutional Neural Network (CNN) for classification is effectively explore the hyper parameter space of CNN technique is help achieve classification accuracy. The MHBA ability to adapt and explore multiple directions in the parameter space makes CNN maximum efficient to variation in input data. The MHBA model is applied to large scale CNN model is efficiently narrowing down the search space and balances exploration and exploitation allowing for search space. The Pre-processing using linear transformation such as translation or rotation help in adjusting the image dimension while maintaining the essential content. The proposed MHBA-CNN technique for industrial robot is achieving better outcomes such as Mean Absolute Error (MAE) of 2.03, Mean Absolute Scaled Error (MASE) of 3.45, R2 score of 2.45 on raw dataset. The existing technique such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) are evaluated of proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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