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共 327 条记录，以下是141-150 订阅

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Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

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

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 IEEE.

关键词： deep learning image pattern recognition MobileNetV3-small Wheat imperfect grains

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Regularized Loose Coupled Deep Non-negative Basis Matrix Factorization for Low-Resolution Face image recognition 1

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3rd International conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Li, Jianhua Wang, Jinxin Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Low-resolution face image recognition is a research focus in computer vision and pattern recognition. In this paper, the potential relationship between matched high-resolution face and low-resolution face images is considered to propose the regularized loose coupled deep non-negative basis matrix factorization (RLCDNBMF) architecture, which uses regularized deep non-negative basis matrix factorization for high-resolution images and couples its coefficient matrix with the coefficient matrix obtained from low-resolution non-negative factorization, so that the discriminative information of high-resolution face images can be fully utilized to guide the non-negative matrix factorization of low-resolution face images. Comparative experiments are conducted on three mainstream face databases, and the experimental results show that the method can improve the accuracy of low-resolution face recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Non-negative matrix factorization

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Vehicle image Classification Method Using vision Transformer 1

引用

2nd international conference Artificial Intelligence and Industrial Applications, A2IA 2023

作者： Taki, Youssef Zemmouri, Elmoukhtar ENSAM Meknes Moulay Ismail University Meknes Morocco

ISBN: (数字)9783031435201

ISBN: (纸本)9783031435195

Vehicle detection and classification has become a critical task of the Advanced Driver Assistance System (ADAS) and Intelligent Transportation System (ITS), with the goal of increasing road safety and saving lives. Advances in image processing, pattern recognition, and deep learning techniques have overcome many obstacles to achieve this goal and still do, especially convolutional neural network. However, most object detection models cannot achieve satisfactory performance under some conditions as low-quality images, nighttime, and other insufficient illumination conditions. In this paper, we propose to increase the effectiveness of ADAS using the vision Transformer Pre-Trained Model, the latest model for computer vision tasks, for vehicle classification problem. The dataset we used is a small dataset of tiny, low-resolution vehicle images. The proposed model is based on the attention mechanism and pre-training on imageNet-21k dataset. The proposed model achieves good performance compared with state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Advanced driver assistance systems

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Correction of Two Human Key-Points Estimations for Medical Applications 6

Correction of Two Human Key-Points Estimations for Medical A...

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6th IEEE International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Luo, Hao Rashid, Said Ramdhan Nie, Yuru Chen, Ancheng Li, Chuan Wu, Sophiann Zheng, Zhiwen Cheng, Zhuzhong Castaneda, Benjamin Peng, Bo Tian, Chao Wu, Zhe Southwest Petroleum University School of Computer Science Chengdu China University of Electronic Science and Technology of China School of Life Science and Technology Chengdu China Sichuan Cancer Hospital Department of Women's Health Chengdu China Canyon Crest Academy San Diego United States Pontificia Universidad Catolica Del Peru Institute of Omics and Applied Biotechnology Lima Peru

ISBN: (纸本)9798350325485

Anatomical key-points recognition is essential in many medical image analyses and clinical healthcare applications. Successfully identifying these anatomical key points provides multiple advantages, such as assisting medical experts in making treatment adjustments and offering information that helps to position surgical instruments at the appropriate locations. However, manual anatomical key-point recognition is subjective, slow, and time-consuming, especially when processing many medical images in clinical institutions. To overcome these limitations, this study aims to establish the correlation between human anatomical key points based on OpenPose and Baidu AI key-point detection techniques and the truth ground anatomical key-points marked by therapists in human medical images. This relationship will help to optimize the detection performance, reduce cost, decrease human error, and accelerate the process. The Sichuan Cancer Hospital provided five whole-body scan images obtained from a clinical CT scanner. A medical expert subsequently identified 14 anatomical key points from each scan. Finally, the datasets were reconstructed into 3-dimensional volume models to visualize whole-body skin models and the skeletons. The human-Annotated 14 key points were then used as ground truth compared to the computer vision techniques: OpenPose and Baidu AI. Both OpenPose and Baidu AI were found to have systematic offsets from the ground true reference points. These findings are reported in this work and can be used as a correction method. © 2023 IEEE.

关键词： computer vision

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Analysis of Automated Skin Disease Classification Exploiting Different Machine Learning Techniques 13

Analysis of Automated Skin Disease Classification Exploiting...

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IEEE 13th Annual Computing and Communication Workshop and conference (CCWC)

作者： Anik, Tanvir Rahman Talukder, Purnendu Faruki, Ishmam Ibn Rahman, Ifti Sam Hossain, Emam Ahsanullah Univ Sci & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9798350332865

One of the most serious and prevalent diseases in the world is skin disease. Due to the intricacy of humans' tone and texture and the visible proximity impact of the disorders, it can be quite difficult to pinpoint the exact type of condition at times. Because of this, it's crucial to recognize skin illnesses at the earliest indication of them. Therefore, it is essential to have a system that can identify skin conditions regardless of these restrictions. With their major advancements, artificial intelligence, computer vision, and machine learning are now crucial to the area of medical research, particularly when it comes to the analysis of medical pictures. In this research, we introduced an automated system based on machine learning for classifying three distinct forms of skin illnesses, including melanoma, basal cell carcinoma, and eczema. For this, we have used different machine learning algorithms like Support Vector Machine, Naive Bayes, Logistic Regression and deep learning models like CNN, LSTM, Bi-LSTM, Inception V3, VGG-16, and Xception. We achieved impressive accuracy of above 90% with every model on a bespoke dataset containing 9665 photos, and the maximum accuracy was 99.33% with the pre-trained deep learning model Xception.

关键词： Machine Learning Deep Learning computer vision pattern recognition Skin Diseases image processing

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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Hazardous Behavior recognition Based on Multi-Model Fusion

Hazardous Behavior Recognition Based on Multi-Model Fusion

引用

asia Pacific computer Systems conference, APCS 2021

作者： Zhang, Bingyi Li, Bincheng Zhu, Yuhan Dalian Maritime University Dalian China

ISBN: (纸本)9789811979033

Distracted driving has become a serious traffic problem. This study proposes an image processing and multi-model fusion scheme to maximize the discrimination accuracy of distracted driving. First, the training dataset and the test dataset are processed to specific specifications by translation and clipping. Second, we set VGG16 as the benchmark model for evaluation and train ResNet50, InceptionV3, and Xception model input images. Finally, considering that each model has its own advantages, we use frozen part of the network layer to fine-tune the model, remove the weights of each single-model fine-tune from the output before full connection, connect them in series, and then calculate each model weights through neural network training. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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SpectralTracker: Jointly High and Low-Frequency Modeling for Tracking 6th

SpectralTracker: Jointly High and Low-Frequency Modeling for...

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6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Rong, Yimin Liang, Qihua Li, Ning Mo, Zhiyi Zhong, Bineng Guangxi Normal Univ Minist Educ Key Lab Educ Blockchain & Intelligent Technol Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China

ISBN: (纸本)9789819985548;9789819985555

Recently, a considerable number of top-performing Transformer based trackers have been proposed. However, most of them mainly focus on utilizing low-frequency information from a spatial-spectral analysis perspective, limiting their performance in complicated scenes. To address this problem, we propose a spectral tracker that explores how to capture high and low-frequency information for robust tracking jointly. Specifically, we design a novel dual-spectral information extraction and aggregation module (DSM) consisting of a high and low-frequency branch to capture and combine complementary frequency information of a Transformer effectively. Firstly, we divide the local window in the high-frequency branch to focus on more fine-grained high-frequency information. Then, in the low-frequency branch, we apply AvgPooling with a low-pass effect on a Transformer to amplify its low-frequency information. Furthermore, we design a shared MLP strategy to polarize the dual-frequency branching to high and low-frequency information attention. Finally, we utilize an MLP to complementarily fuse high and low-frequency information for frequency domain modeling. Comprehensive experiments on five tracking benchmarks (i.e., GOT-10k, TrackingNet, LaSOT, UAV123 and TNL2K) show that our spectral tracker achieves better performance than the state-of-the-art trackers.

关键词： Object Tracking vision Transformer Spectral Domain Single-stage Backbone

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image Generation Method Based on Variable Scale Pyramid Attention with Text Data Enhancement 6

Image Generation Method Based on Variable Scale Pyramid Atte...

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6th International conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Qiao, Pingan Lei, Ziying Zhang, Xiaofan School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798400707674

In recent years, research on methods for generating images from text based on generative adversarial networks has made some progress. The study of text-generated images is at the intersection of natural language processing and computer vision, a task that requires computers to be able to generate images that are consistent with the semantics of the text on the basis of understanding the semantics of the input text. Aiming at the problems of text-image matching and poor performance on image details in traditional text-generated image methods, a VT-AttnGAN (Variable Scale Pyramid Attention with Text Data Augmentation for Generative Adversarial Networks) text-based image generation method. A Vector Data Enhancement Network (TDEN) is designed to reduce the text dimension to enhance the model's ability to learn the probability distribution of text features, and optimize the alignment of image and text. In addition, to enhance the detailed features of the image, a Variable Scale Pyramid Attention Module (VSPAM) is proposed, which extracts the features at different scales of the initial image, and efficiently utilizes the useful information between the feature channels through the attention approach. We train and test on two datasets, CUB-200-2011 and Oxford-102, and the experimental results show that compared with AttnGAN, the model proposed in this paper solves the problems of image blurring and distortion, and has the image generation effect of text image alignment enhancement and picture integrity improvement. © 2023 ACM.

关键词： Generative adversarial networks

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MambaVesselNet: A Hybrid CNN-Mamba Architecture for 3D Cerebrovascular Segmentation 6

MambaVesselNet: A Hybrid CNN-Mamba Architecture for 3D Cereb...

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6th ACM International conference on Multimedia in asia, MMasia 2024

作者： Chen, Yanming Liu, Ziyu He, Xiangjian School of Computer Science University of Nottingham Ningbo China Ningbo China

ISBN: (纸本)9798400712739

Segmenting vessels in magnetic resonance imaging (MRI) stands as a mainstream approach for evaluating cerebrovascular conditions. Due to the complex semantics and topology of cerebrovascular structures, existing CNN-based segmentation methods often fail to correlate the topological structure and branch vessels, resulting in incomplete segmentation. To address the challenge of global dependencies modelling, transformer architectures have been employed due to their capability of capturing long-range dependencies, and they have shown promise in 3D medical image segmentation. However, the transformer architecture greatly increases the computational burden when processing high-dimensional 3D MRI images. In light of this, a selective state space model (SSM) Mamba has gained recognition for its adeptness in handling long-range dependencies in sequential data, particularly noted for its efficiency and speed in natural language processing applications. Mamba is now widely applied in various computer vision tasks. Based on these findings, in this study, we propose MambaVesselNet, a Hybrid CNN-Mamba network for 3D cerebrovascular segmentation. MambaVesselNet leverages CNNs to capture local features and incorporates the Mamba block at the bottleneck to model long-range dependencies within the whole-volume features. The effectiveness of MambaVesselNet is validated on a public cerebrovascular dataset, and our benchmark demonstrates new state-of-the-art performance. © 2024 Copyright held by the owner/author(s).

关键词： Magnetic resonance imaging

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