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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1803 条记录，以下是311-320 订阅

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The Ability to Control the Technical vision of Robots for Effective Object Recognition 2

The Ability to Control the Technical Vision of Robots for Ef...

引用

2nd international conference on Technology Enhanced Learning in Higher Education, TELE 2022

作者： Kramarov, Sergey Olegovich Khramov, Vladimir Viktorovich Grebenyuk, Elena Vladamirovna Southern Federal University. SFU Department of Technology and Professional and Pedagogical Education Rostov-on-Don Russia Department of Computer Science Rostov-on-Don Russia Surgut State University SurSU Department of Automated Information Processing and Management Systems Surgut Russia

ISBN: (纸本)9781665470070

The theoretical and practical issues of the development of a control system for the technical vision of robots, which is based on the specifics of the processed information and the principles of building an integral system for understanding the image of the scene in its study, as well as the principles of building an integral system for recognizing the image of the scene, are considered. The issues of visual representation of objects and processes on rectangular, triangular and hexagonal rasters are considered. The results of these studies make it possible to analyze data presented through visual coding, intelligent processing and use for support and decision-making systems in a wide variety of fields of knowledge. The use of the proposed approach to the description of objects provides rapid analysis of raster images, prepares the initial information for more subtle studies, allows to quickly determine the important characteristics of the object in the image. © 2022 IEEE.

关键词： Object recognition

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Class Distribution Aware Focal Loss for Long-Tailed Object Detection 2

Class Distribution Aware Focal Loss for Long-Tailed Object D...

引用

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

作者： Shao, Yalei Qi, Chongchong Pu, Yuanyuan Lin, Xu Wang, Linfei Tao, Dapeng Yunnan University School of Information Science and Engineering Kunming China Yunnan United Vision Technology Company Limited Technology Center Kunming China

ISBN: (纸本)9798350331417

Object detection tasks have made significant progress on class-balanced, high-quality datasets. However, training data often exhibit a long-tail distribution in real-world scenarios. Existing re-weighting methods have made preliminary explorations into the imbalance issue of positive and negative samples across different categories. However, the additional hyperparameters introduced complicate the network's practicality and performance on the overall dataset. To address this, we propose a class-distribution-aware focal (CDAF) loss. Without the need for meticulous manual parameter tuning, it can dynamically control the contribution of different sample categories and complexity levels to model training using a balancing factor related to sample features. This allows the model to improve performance not only on rare classes but also on difficult samples. Extensive experimental results demonstrate that our approach achieves significant performance improvements on challenging object detection datasets, including Pascal VOC and MS COCO. © 2023 IEEE.

关键词： adaptively class imbalance distribution of class loss re-weighting

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image Denoising Using AI with Entropy as Metric Analysis 1

引用

2nd EAI international conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Venkateshwara Reddy, H. Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

This paper introduces a novel denoising approach image denoising is one of the fundamental challenges in the field of image processing and computer vision. Our main aim of the project is to get a complete noiseless image with high accuracy and less time. So, in our project we are proposing an effective denoising technique using RNN (Recurrent neural network) for fixed pattern noisy images which may reduce the usage of number of auto encoders. Here, we are passing images into the recurrent neural networks as pixel information in the form of a 3D coordinate system. RNN doesn’t migrate the information from one node to another node until it gets its basic requirements. As we are using a single auto encoder, it will reduce noise as well as time complexity. The statistical analysis is going to be observed by using the following metric considerations, namely SNR (Signal to noise ratio), PSNR (Peak signal to noise ratio), MSE (Mean square error) and Entropy. From this research work we are going to get a complete noiseless image. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Recurrent neural networks

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Lip reading using external viseme decoding 12

Lip reading using external viseme decoding

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Peymanfard, Javad Reza Mohammadi, Mohammad Zeinali, Hossein Mozayani, Nasser Iran University of Science and Technology School of Computer Engineering Tehran Iran Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Lip-reading is the operation of recognizing speech from lip movements. This is a difficult task because the movements of the lips when pronouncing the words are similar for some of them. Viseme is used to describe lip movements during a conversation. This paper aims to show how to use external text data (for viseme-to-character mapping) by dividing video-to-character into two stages, namely converting video to viseme and then converting viseme to character by using separate models. Our proposed method improves word error rate by an absolute rate of 4% compared to the typical sequence to sequence lipreading model on the BBC-Oxford Lip Reading dataset (LRS2). © 2022 IEEE.

关键词： Speech recognition

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The Detection of Blastocyst Embryo in Vitro Fertilization (IVF) 12

The Detection of Blastocyst Embryo in Vitro Fertilization (I...

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Dehkordi, Kimiya Samie Moghaddam, Mohsen Ebrahimi Shahid Beheshti University Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

One of the most important stages in the fate of the embryo in In vitro fertilization (IVF) is the blastocyst stage. There is currently no way to diagnose blastocyst. In this study, using Resnet and Unet networks, the embryo was detected in the blastocyst state. The proposed method is trained on a set of data consisting of 40392 data, which is 24365 data for training and 5814 data for validation, and is tested on 10263 data obtained from various sources. The results show an accuracy of 92.9% and a precision of 93.7% and recall of 92 92.1% which confirm that the proposed method was well able to detect the states in which the fetus is in the blastocyst state. © 2022 IEEE.

关键词： Deep learning

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A face detection method via ensemble of four versions of YOLOs 12

A face detection method via ensemble of four versions of YOL...

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Khalili, Sanaz Shakiba, Ali Vali-e-Asr University of Rafsanjan Department of Computer Sciences Rafsanjan Iran

ISBN: (纸本)9781665412162

We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). The experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. These numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble. © 2022 IEEE.

关键词： Face recognition

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Sign Language Detection using Action Recognition 2

Sign Language Detection using Action Recognition

引用

2nd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Iyer, Vishwa Hariharan Prakash, U.M Vijay, Aashrut Sathishkumar, P. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Pgp College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665437899

Sign Language Detection has become crucial and effective for humans and research in this area is in progress and is one of the applications of computer vision. Earlier works included detection using static signs with the help of a simple deep learning-based Convolutional Neural Network. This proposal is based on continuous detection of image frames in real-time using action detection so as to detect the action performed by the user. The model uses LSTM neural network model after identifying keypoints using mediapipe holistic which includes face, pose and hand features. The proposed work is done by collecting key value points for training and testing, pre-processing the data, and creating labels and features. It saves the weights and evaluates the model using confusion matrix accuracy. © 2022 IEEE.

关键词： computer vision

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Underwater image Enhancement using a Light Convolutional Neural Network and 2D Histogram Equalization 12

Underwater Image Enhancement using a Light Convolutional Neu...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Khandouzi, Ali Ezoji, Mehdi Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran

ISBN: (纸本)9781665412162

Underwater images usually have low contrast, blurring, and extreme color distortion because the light is refracted, scattered, and absorbed as it passes through the water. These features can lead to challenges in image-based processing and analysis. In this paper, a two-step method based on a deep convolution network is proposed for solving these problems and enhancing underwater images. First, through a light global-local structure, the initial image enhancement is performed and the color distortion and degradation of the images are partially covered. Then, two-dimensional histogram equalization is used as an appropriate complement to the network. Two-dimensional histogram equalizing is able to produce clear results and prevents excessive contrast. The results show that the proposed method performs better than other methods in this field in terms of qualitative and quantitative criteria. © 2022 IEEE.

关键词： image enhancement

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MUWS'2023: The 2nd international Workshop on Multimodal Human Understanding for the Web and Social Media 23

MUWS'2023: The 2nd International Workshop on Multimodal Huma...

引用

32nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Cheema, Gullal S. Hakimov, Sherzod Kastner, Marc A. Garcia, Noa Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Kyoto Univ Kyoto Japan Osaka Univ Osaka Japan

ISBN: (纸本)9798400701245

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like computer vision (CV), Natural Language processing (NLP), Speech processing, Human-computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop will be an interactive event and include keynotes by relevant experts, poster and demo sessions, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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Deep Siamese Multi-level Feature Network for VIS and NIR image Matching 2

Deep Siamese Multi-level Feature Network for VIS and NIR Ima...

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2nd international conference on Signal processing, computer Networks and Communications, SPCNC 2023

作者： Ma, Xinyi Wang, Fan School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798400716430

Infrared (NIR) and visible light (VIS) images matching is a critical issue in the field of computer vision, aiming to align and correlate images from different spectral ranges. The structural deformation between NIR and VIS light images, alongside nonlinear radiation differences, results in lower generality and efficiency of existing image matching methods. Addressing the inadequacy of existing methods in providing the necessary high-level feature information differences for matching cross-modal images, we propose a deep Siamese multi-feature network for matching VIS and NIR images. Firstly, we propose multi-tier Siamese sub-network components for recursive inference, focusing on understanding high-level image features to obtain more spatially rich semantic information. Subsequently, we employ Transformer encoders to attend to multi-scale Siamese CNN's multi-level feature maps to decode complex nonlinear feature differences between cross-modal images caused by different physical processes. Finally, experimental results on multimodal datasets demonstrate that our method outperforms the SOTA methods in matching metrics. © 2023 ACM.

关键词： image matching

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