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共 8412 条记录，以下是271-280 订阅

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Text Query based Traffic Video Event Retrieval with Global-Local Fusion Embedding

Text Query based Traffic Video Event Retrieval with Global-L...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Thang-Long Nguyen-Ho Minh-Khoi Pham Tien-Phat Nguyen Hai-Dang Nguyen Do, Minh N. Nguyen, Tam, V Minh-Triet Tran Univ Sci Ho Chi Minh City Vietnam Viet Nam Natl Univ Ho Chi Minh City Vietnam John Neumann Inst Ho Chi Minh City Vietnam Univ Illinois Urbana IL USA Univ Dayton Dayton OH 45469 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving event videos based on textual description is a promising research topic in the fast-growing data field. However, traffic data increases every day, so it is essential to need intelligent traffic system management in conjunction with humans to speed up the search. We propose a multi-module system that delivers accurate results that meet objectives, including explainability and scalability at the same time. Our solution considers neighbors entities related to the mentioned object to represent an event by rule-based, which can represent an event by the relationship of multiple objects. In our proposed retrieval method, we add our modified model of Alibaba solution with the post-processing.techniques from HCMUS method in AI City Challenge 2021 to boost the explainability of the obtained results. As the traffic data is vehicle-centric, we apply two language and image modules to analyze the input data and obtain the global properties of the context and the internal attributes of the vehicle. We introduce a one-on-one dual training strategy for each representation vector to optimize the interior features for the query. Finally, a refinement module gathers previous results to enhance the final retrieval result. We benchmarked our approach on the data of the AI City Challenge 2022 and obtained the competitive results at an MMR of 0.3611. We were ranked in the top 4 on 50% of the test set and in the top 5 on the full set.

关键词： Training Visualization Target tracking image color analysis Scalability Urban areas Training data

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A Review on Various image-recognition Types and their Efficiency 7

A Review on Various Image-Recognition Types and their Effici...

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7th International conference on Computing Methodologies and Communication, ICCMC 2023

作者： Prasad, Vurimi Sri Venkata Durga Reddy, Indla Madhusudhan Sasank, Ravi Krishna Vinnakota, Sai Durga Tejaswi Naga Lakshmi, T. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665464086

Now-a-days images and documents play an important role in storing information. image processing.and text extraction are essential to sustain in our digital society. Text extraction and recognition from images and documents is a key step to build efficient indexing methodology for accessing and retrieving of information from any database. Commonly used in online shopping and augmented reality. To put it simple image recognition provides an extensive range of possibilities and services to categorise and validate documents. image recognition is not optimized for extracting text and information from images with complex backgrounds and fonts. Still the elements or characters cannot be extracted due to character misalignment or writing style. The purpose of this document is to collect information about various techniques andalgorithms used for text extraction from images, documents, and their efficiency. This study has reviewed various deep learning methods such as optical character recognition (OCR) and a hybrid technique by combining region-based and texture-based methods for text extraction. © 2023 ieee.

关键词： Optical character recognition

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SC-NAFSSR: Perceptual-Oriented Stereo image Super-Resolution Using Stereo Consistency Guided NAFSSR

SC-NAFSSR: Perceptual-Oriented Stereo Image Super-Resolution...

引用

2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Qiu, Zidian He, Zongyao Zhan, Zhihao Pan, Zilin Xian, Xingyuan Jin, Zhi Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Technology China

ISBN: (纸本)9798350302493

Stereo image Super-Resolution (SR) has made significant progress since binocular systems are widely accepted in recent years. Most stereo SR methods focus on improving the PSNR performance, while their visual quality is over-smoothing and lack of detail. Perceptual-oriented SR methods are mainly designed for single-view images, thereby their performance decreases on stereo SR due to stereo inconsistency. We propose a perceptual-oriented stereo SR framework that considers both single-view and cross-view information, noted as SC-NAFSSR. With NAF-SSR [3] as our backbone, we combine LPIPS-based perceptual loss and VGG-based perceptual loss for perceptual training. To improve stereo consistency, we perform supervision on each Stereo Cross-Attention Module (SCAM) with stereo consistency loss [27], which calculates photometric loss, smoothness loss, and cycle loss using the cycle-attention maps and valid masks of SCAM. Furthermore, we propose training strategies to fully exploit the performance on perceptual-oriented stereo SR. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed method. In particular, SC-NAFSSR outperforms the SOTA methods on Flickr1024 dataset [30]. In the NTIRE 2023 Stereo image Super-Resolution Challenge Track 2 Perceptual & Bicubic [26], SC-NAFSSR ranked 2nd place on the leaderboard. Our source code is available at https://***/FVL2020/SC-NAFSSR. © 2023 ieee.

关键词： Stereo image processing

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End-to-End Learned image Compression with Augmented Normalizing Flows

End-to-End Learned Image Compression with Augmented Normaliz...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Ho, Yung-Han Chan, Chih-Chun Peng, Wen-Hsiao Hang, Hsueh-Ming Natl Chiao Tung Univ Comp Sci Dept Hsinchu Taiwan Natl Chiao Tung Univ Elect Engn Dept Hsinchu Taiwan Natl Chiao Tung Univ Pervas AI Res PAIR Labs Hsinchu Taiwan

ISBN: (纸本)9781665448994

This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a smoother transformation from the augmented input space to the latent space. Inspired by the fact that ANF can offer greater expressivity by stacking multiple variational autoencoders (VAE), we generalize the popular VAE-based compression framework by the autoencoding transforms of ANF. When evaluated on Kodak dataset, our ANF-based model provides 3.4% higher BD-rate saving as compared with a VAE-based baseline that implements hyper-prior with mean prediction. Interestingly, it benefits even more from the incorporation of a post-processing.network, showing 11.8% rate saving as compared to 6.0% with the baseline plus post-processing.

关键词： computer vision image coding conferences Stacking Low-pass filters Transforms Predictive models

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MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries

MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queri...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Zhang, Tianyuan Chen, Xuanyao Wang, Yue Wang, Yilun Zhao, Hang Carnegie Mellon Univ Pittsburgh PA 15213 USA Fudan Univ Shanghai Peoples R China MIT Cambridge MA 02139 USA Li Auto Stanford CA USA Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate and consistent 3D tracking from multiple cameras is a key component in a vision-based autonomous driving system. It involves modeling 3D dynamic objects in complex scenes across multiple cameras. This problem is inherently challenging due to depth estimation, visual occlusions, appearance ambiguity, etc. Moreover, objects are not consistently associated across time and cameras. To address that, we propose an end-to-end MUlti-camera TRacking framework called MUTR3D. In contrast to prior works, MUTR3D does not explicitly rely on the spatial and appearance similarity of objects. Instead, our method introduces 3D track query to model spatial and appearance coherent track for each object that appears in multiple cameras and multiple frames. We use camera transformations to link 3D trackers with their observations in 2D images. Each tracker is further refined according to the features that are obtained from camera images. MUTR3D uses a set-to-set loss to measure the difference between the predicted tracking results and the ground truths. Therefore, it does not require any post-processing.such as non-maximum suppression and/or bounding box association. MUTR3D outperforms state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code will be released.

关键词： Solid modeling Visualization Three-dimensional displays Tracking image color analysis Fuses Cameras

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Brain Tumor Segmentation using Spatial Fuzzy C-means(SFCM) and CNN Fusion

Brain Tumor Segmentation using Spatial Fuzzy C-means(SFCM) a...

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2024 International conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Narava, Sailaja Aduru, Veera Reddy Pujitha, Atluri Gemini, T Rakesh Sumanth, Bapatla Velagapudi Ramakrishna Siddhartha Engineering College Electrical and Electronics Engineering Vijayawada India

ISBN: (纸本)9798350381887

In today's medical field, prompt and precise detection of brain tumors is essential for optimal treatment planning and enhanced patient prognosis. One of the most common methods for diagnosing brain tumors is through SFCM (Spatial Fuzzy C Means) and CNN. SFCM is a clustering algorithm that utilizes fuzzy set theory to partition images into different regions based on similarity characteristics. This algorithm has been frequently utilized in diverse implementations, such as image processing.and pattern recognition. Conversely, CNN is an advanced deep-learning technique that has demonstrated impressive efficacy in peculiar computer vision tasks, such as image segmentation. Although both SFCM and CNN have been used individually for brain tumor segmentation, there is a need for an integrated approach that combines the strengths of both methods to enhance segmentation accuracy. To achieve more precise and efficient brain tumor detection, the combination of SFCM and CNN holds great potential. By integrating the feature extraction capabilities of CNN with the segmentation abilities of SFCM, medical imaging can be transformed. This integration eliminates the need for hand-crafted features and allows for processing.images of arbitrary sizes, thus enhancing the accuracy and adaptability of the segmentationprocess. © 2024 ieee.

关键词： image segmentation

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Optimizing Data Ingestion for Big Data: A Cloud-Based Design pattern Approach

Optimizing Data Ingestion for Big Data: A Cloud-Based Design...

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2024 ieee International conference on Big Data, BigData 2024

作者： Rucco, Chiara Longo, Antonella Saad, Motaz University of Salento Department of Engineering Innovation Lecce Italy

ISBN: (纸本)9798350362480

The rapid growth of data has created significant challenges in managing and leveraging data effectively. Data engineering has emerged as a crucial discipline to address these challenges, providing frameworks for efficient data management. Data Engineering patterns (DEP) and Data Engineering Design patterns (DEDP) offer standardized practices and best practice solutions for data engineering tasks like ETL. While various DEPs and DEDPs exist, the issue of high-volume data ingestion remains insufficiently addressed. This paper focuses on defining design patterns specifically for data ingestion techniques within cloud-based architectures, covering both incremental and full refresh methods. The proposed approach utilizes a flexible, metadata-driven framework to enhance adaptability and ease of use, allowing for seamless changes to the ingestion type, schema updates, table additions, and incorporation of new data sources. Validated on the Azure cloud platform, the experiments demonstrate that the proposed design patterns significantly reduce data ingestion time, contributing to the field of data management by addressing key challenges in high-volume data processing. © 2024 ieee.

关键词： Cloud Data Ingestion Design patterns

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Lightweight dual scale angle transformation network for accurate detection of power components

Lightweight dual scale angle transformation network for accu...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Liu, Huiting Wang, Huimin Xu, Yaqian Du, Rui College of Computer and Electrical Engineering Hunan University of Arts and Sciences Changde China College of Automation and Electronic Information Xiangtan University Xiangtan China

ISBN: (纸本)9781510666351

The detection technology of infrared thermal imaging is becoming more and more mature. With the wide application of deep learning, the accurate detection method of infrared image power components based on object detection occupies the mainstream of the market. The existing accurate detection methods of power components mainly have problems such as difficulty in taking into account the speed and accuracy, and low recall rate of rotating targets. This paper proposes a Lightweight Dual Scale Angle Transform Network (LDS-ATN). Based on the lightweight backbone network, an angle transform module is introduced to enhance the angle correction. Finally, this paper achieves an average accuracy of 89.89% and a detection speed of 127.27 FPS on the task of accurate detection of electrical components. It proves the practicability and generalizability of this network in power detection. © 2023 SPIE.

关键词： Object detection

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 ieee.

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A Method of Constructing and Automatically Labeling Radio Frequency Signal Training Dataset for UAV 48

A Method of Constructing and Automatically Labeling Radio Fr...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Liu, Chao Ma, Ruipeng Si, Zheng Chi, Mingmin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Zhengzhou University School of Cyber Science and Engineering China Zhengzhou University Henan Institute of Advanced Technology China

ISBN: (纸本)9781728163277

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%. © 2023 ieee.

关键词： Object detection

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