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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

共 16199 条记录，以下是181-190 订阅

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image to Text: Comprehensive Review on Deep Learning Based Unsupervised image Captioning 2

Image to Text: Comprehensive Review on Deep Learning Based U...

引用

2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Shetty, Aishwarya D Shetty, Jyothi Department of Cse Nitte India Visvesveraya Technological University Belagavi590018 India

ISBN: (纸本)9798350308846

The task of image captioning has seen considerable success using deep neural networks. This assessment offers a thorough overview of the most cutting-edge approaches for deep learning-based unsupervised image captioning. In order to bridge the gap between computer vision and natural language processing, the effort of creating meaningful textual captions for images is known as image captioning. Without using annotated image-caption pairings for training, unsupervised approaches try to provide captions. The review examines a variety of techniques, including generative adversarial networks (GANs), pre-training, transfer learning, and convolutional neural networks (CNNs) for extracting visual features, as well as recurrent neural networks (RNNs) or transformer models for language modelling. Researchers and practitioners can benefit from this survey's observations, classification of methodologies, and recommendations for the future of deep learning-based unsupervised image captioning. © 2023 IEEE.

关键词： computer vision deep learning Natural Language processing Unsupervised image captioning

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REVOLUTIONIZING THERMAL IMAGING: GAN-BASED vision TRANSFORMERS FOR image ENHANCEMENT 30

REVOLUTIONIZING THERMAL IMAGING: GAN-BASED VISION TRANSFORME...

引用

30th IEEE international conference on image processing (ICIP)

作者： Marnissi, Mohamed Amine Univ Sfax Ecole Natl Ingn Sfax LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia

ISBN: (纸本)9781728198354

In this paper, we propose a new architecture for thermal image enhancement in which we exploit the strengths of both architecture-based vision transformers and generative adversarial networks. Our approach includes the introduction of a thermal loss function, which is specifically employed to produce high quality images. In addition, we consider fine-tuning based on visible images for thermal image restoration, resulting in an overall improvement in image quality. The performance of our proposed architecture is evaluated using visual quality metrics. The results show significant improvements over the original thermal images and over other established enhancement methods on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin considering different versions of the YOLO detector.

关键词： image enhancement generative adversarial networks vision transformers object detection

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Multi-scale Feature Modification Network for FewShot image Classification 2

Multi-scale Feature Modification Network for FewShot Image C...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Chen, Yuxiang Lu, Zongqing Tsinghua University Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9798350331417

Few-shot image classification aims to learn a model that can adopt to unseen classes with few labeled data. This challenging problem requires to overcome the distribution shift of features due to differences between the novel classes and the base classes. Inspired by transformer style self-attention mechanism and feature pyramid, we propose a multi-scale cross-attention module. This module enables the correction of features by interacting with feature information at different scales and obtains the feature set of the sample. In addition, from the perspective of metric learning, we further propose a distance metric module to measure the distance between sets of features, thus better characterizing the similarity between samples. Extensive experiments on two public benchmarks in both 1-shot and 5-shot setting demonstrate the effectiveness of our method, which is a significant performance improvement over most methods. © 2023 IEEE.

关键词： cross-attention Few-shot image classification metric learning multi-sacle

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NCVPRIPG 2023 Summer Challenge on Writer Verification 8th

NCVPRIPG 2023 Summer Challenge on Writer Verification

引用

8th National conference on computer vision, Pattern Recognition, image processing and Graphics-NCVPRIPG

作者： Prabhat, Gyan Shahid, Abu Gatti, Prajwal Shah, Manan Chutia, Girin Sharma, Arvind Kumar Mishra, Anand Indian Inst Technol Jodhpur Jodhpur Rajasthan India Cashfree Payments India Pvt Ltd Bengaluru India HCL Technol Ltd Chennai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

This report presents the outcomes of the Summer Challenge on Writer Verification, hosted as part of the Eighth National conference on computer vision, Pattern Recognition, image processing, and Graphics (NCVPRIPG) held at IIT Jodhpur on July 21-23, 2023. This challenge introduces a novel dataset comprising images of handwritten text contributed by 1,352 unique writers. Predominantly, these images feature handwritten Hindi text, but they also encompass a variety of elements such as numbers, mathematical symbols, and English text. Participants were tasked with developing a model capable of automatically determining whether the text in a given pair of images is authored by the same writer or different writers. The primary objective of this challenge was to advance research in the realm of handwritten text recognition. Throughout the competition, we registered 108 teams, with 18 teams submitting results for the validation dataset. Out of these, 13 teams provided submissions for the semi-final dataset. The top six teams from the semi-finals were subsequently invited to compete in the finals. Additional details about the challenge can be found at https://***/challenges/wv2023/.

关键词： Writer verification Handwritten text Indic language Document analysis Handwritten text recognition

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Contrastive Feature Masking Open-Vocabulary vision Transformer

Contrastive Feature Masking Open-Vocabulary Vision Transform...

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IEEE/CVF international conference on computer vision (ICCV)

作者： Kim, Dahun Angelova, Anelia Kuo, Weicheng Google DeepMind London England

ISBN: (纸本)9798350307184

We present Contrastive Feature Masking vision Transformer (CFM-ViT) - an image-text pretraining methodology that achieves simultaneous learning of image- and region-level representation for open-vocabulary object detection (OVD). Our approach combines the masked autoencoder (MAE) objective into the contrastive learning objective to improve the representation for localization tasks. Unlike standard MAE, we perform reconstruction in the joint image-text embedding space, rather than the pixel space as is customary with the classical MAE method, which causes the model to better learn region-level semantics. Moreover, we introduce Positional Embedding Dropout (PED) to address scale variation between image-text pretraining and detection finetuning by randomly dropping out the positional embeddings during pretraining. PED improves detection performance and enables the use of a frozen ViT backbone as a region classifier, preventing the forgetting of open-vocabulary knowledge during detection finetuning. On LVIS open-vocabulary detection benchmark, CFM-ViT achieves a state-of-the-art 33.9 APr, surpassing the best approach by 7.6 points and achieves better zero-shot detection transfer. Finally, CFM-ViT acquires strong image-level representation, outperforming the state of the art on 8 out of 12 metrics on zero-shot image-text retrieval benchmarks.

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Object Detection in Adverse Weather Conditions using Machine Learning

Object Detection in Adverse Weather Conditions using Machine...

引用

2023 international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Kumar, Manish Yadav, Anup Lal Arora, Abhilaksh Deb, Arnab Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350315455

Object detection in challenging weather conditions is a formidable hurdle in the realm of computer vision. Unfavorable weather circumstances, encompassing rain, snow, fog, and low light conditions, substantially impede the effectiveness of conventional computer vision algorithms. This research paper introduces an innovative approach to image-to-image translation by harnessing the power of CycleGAN, a deep learning architecture renowned for its proficiency in transmuting images from one domain to another. This study is dedicated to tailoring the CycleGAN framework to the specific task of enhancing images captured in adverse weather conditions, such as rain, fog, and snow. The proposed methodology capitalizes on the deployment of two distinctive generators, trained cyclically, for the transformation of images between a source domain (representing adverse weather) and a target domain (signifying clear weather). These generators are bolstered by discriminator networks, enabling adversarial training to enhance the quality of image translation. © 2023 IEEE.

关键词： Adversarial Training Autonomous Driving bdd100k computer vision CycleGAN Data Augmentation Dataset Deep Learning Generative Adversarial Networks image Degradation image Enhancement image processing image Quality Improvement image-to-image Translation Neural Networks Synthetic Data Weather Conditions

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Reversible Data Hiding in Encrypted image based on Adaptive Difference Prediction and Block Subdivision

Reversible Data Hiding in Encrypted Image based on Adaptive ...

引用

2023 international conference on Algorithm, Imaging processing, and Machine vision, AIPMV 2023

作者： Zhang, Xuesheng Wang, Jing School of Computer and Information Security Guilin University of Electronic Science and Technology Guilin541004 China

ISBN: (纸本)9781510672444

Reversible Data Hiding in Encrypted images (RDHEI) embeds information while protecting the content of images from being leaked, allowing users to decrypt image content, extract embedded information, and losslessly recover the original content based on the key types they possess. It is a recent hot research area at the intersection of information hiding and encrypted computation, aiming to ensure both data security and the ability to hide information within images. However, inadequate utilization of image blocks in RDHEI results in a low embedding capacity of additional data. For this reason, this paper proposes a RDHEI based on adaptive difference prediction and block subdivision. At first, divide the image into equally sized blocks, and these blocks are encrypted to conceal the content of the image. For data hider, using adaptive the most significant bit(MSB) prediction to classify the available and unavailable blocks. Based on adaptive MSB prediction(AMP), adaptive difference prediction(ADP) is used to subdivide the unavailable blocks to vacate more room for data embedding. When receiver receives the embedded encrypted image, the embedded data or image can be decrypted according to its own key possession. Experimental results show that the proposed method has a significant effect on improving the embedding capacity. © 2024 SPIE.

关键词： Forecasting

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Automated Template Matching for External Thread Surface Defects with image processing 5

Automated Template Matching for External Thread Surface Defe...

引用

5th IEEE international conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Nirmala, V. Joseph Abraham Sundar, K. Aiswarya, G. Sai Sidhartha, N. Prabu, S. SASTRA Deemed to Be University India

ISBN: (纸本)9781665493604

Artificial Intelligence is a fast-growing domain that facilitates the innovation in various fields of business and manufacturing industries. This field of Machine learning provides the automatic inspection of the manufactured products for the detection of dimensionality measurement of the products. Human visual inspection on finding imperfect dimensions at micro level scratches, dents and quantification variations of engine valve are prone to errors. Here, we propose complete automation to find conventional dimension variations of the product with the traditional machine vision algorithms. Two approaches are adopted here for Edge detection for dimension measurement from the pixel processing and template matching for the identification of deviations for the anomaly detection in the product. As a result, a new automation technique is obtained, that will be used at various levels of quality inspections for the dimensions measurement. © 2023 IEEE.

关键词： computer vision

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Geometric shape recognition based on FAST feature points and hough transform 4

Geometric shape recognition based on FAST feature points and...

引用

4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Kang, Jinchuan Lu, Jun Zhang, Xu Cao, Yuan Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the rapid development of computer vision and image processing, geometric shape recognition has become a highly regarded research area. This study aims to explore a method that combines the FAST feature point recognition algorithm with the Hough transform for fast and accurate recognition of geometric shapes in images, including lines, circles, and rectangles. The FAST feature point recognition algorithm is known for its speed and robustness in effectively detecting key points in images, while the Hough transform allows mapping these key points into parameter space, enabling us to detect and recognize specific geometric shapes. This research focuses on explaining the principles of the method, presenting experimental results, performance evaluations, and discussing future research directions. © 2024 SPIE.

关键词： computer vision

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Defect Detection of Wood Grain images with image Gradient Applied to AE-based generation 6

Defect Detection of Wood Grain Images with Image Gradient Ap...

引用

6th IEEE international conference on Knowledge Innovation and Invention, ICKII 2023

作者： Si, Jongwook Kim, Sungyoung Kumoh National Institute of Technology Department of Computer Ai Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Department of Computer Engineering Gumi Korea Republic of

ISBN: (纸本)9798350323535

Detecting and managing various types of defects that occur in the manufacturing process is important for product quality control. Detecting flaws in product presentation is an ongoing research topic in computer vision. Recently, deep learning technology has been used to improve defect detection performance compared to traditional computer vision methods. In this study, we proposed a new method for detecting defects on the surface of wooden boards using image reconstruction with an autoencoder-based model and image gradient filters in image processing using edge detection. Although the reconstruction performance of the autoencoder is significantly inferior to the newest technologies, this characteristic is used to remove noise from input images. We used the publicly available anomaly detection dataset, MV-Tec AD, for training and evaluation and presented experimental results focusing on the "Wood"category. The proposed method in this study demonstrated better detection performance with image processing. © 2023 IEEE.

关键词： image reconstruction

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