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Global video object segmentation with spatial constraint module

Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Diverse Co-saliency Feature Learning for Text-Based Person Retrieval

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5465-5477页

作者： You, Shuai Chen, Cuiqun Feng, Yujian Liu, Hai Ji, Yimu Ye, Mang School of Internet of Things Nanjing China Anhui University School of Computer Science and Technology Hefei China South China Normal University school of computer Guangdong China NJUPT School of Computer Science Nanjing China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan430072 China

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning Network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly. © 2005-2012 IEEE.

关键词： Feature extraction

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LSRN-AED: lightweight super-resolution network based on asymmetric encoder–decoder

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Soft Computing 2024年第13-14期28卷 8513-8525页

作者： Huang, Shuying Li, Wei Yang, Yong Wan, Weiguo Lai, Houzeng School of Software Tiangong University Tianjin300387 China School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang330032 China School of Computer Science and Technology Tiangong University Tianjin300387 China

Due to limited memory and computing resources, the application of deep neural networks on embedded and mobile devices is still a great challenge. To tackle this problem, this paper proposes a lightweight super-resolution network based on asymmetric encoder–decoder (LSRN-AED), which achieves better performance while reducing model parameters and computation. On the basis of rethinking the roles of encoder and decoder, an asymmetric encoder–decoder (AED) composed of complex encoders and simple decoders is designed to achieve feature extraction and reconstruction. Here, the decoder only adopts one inverted residual block, which can reduce the computational cost of the model and the redundancy of mapping features. For the encoder, inspired by the Transformer structure, an epiphany encoder is designed to realize the feature extraction and representation. In the encoder, a multi-way epiphany attention module (MEAM) is constructed, in which inverted residual blocks are used to replace traditional residual blocks to extract features and reduce model complexity. To realize the selection and enhancement of spatial features, an epiphany attention block (EAB) is designed by exploiting depth-wise convolutions which can learn the significant spatial information of the feature maps. Experimental results demonstrate that the proposed LSRN-AED can achieve better performance at lower parameter cost and outperform some existing state-of-the-art lightweight models. For example, compared to the advanced SMSR method, the proposed LSRN-AED has better evaluation metrics while reducing the number of parameters by 45%, 44%, and 44%, and FLOPs by 44%, 42%, and 41% on the × 2/3/4 SR tasks, respectively. The code will be published on GitHub after our paper is accepted for publication. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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Exploring Accelerometer Sensors for Optimized Smart Shoe-Based Fall Detection 14

Exploring Accelerometer Sensors for Optimized Smart Shoe-Bas...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Rahman, Md Shohanoor Nikoukar, Ali Gunes, Mesut Dezfouli, Behnam Faculty of Computer Science Magdeburg Germany Santa Clara University Internet of Things Research Lab Department of Computer Engineering United States

ISBN: (纸本)9798350304138

The need of immediate fall detection, particularly within the geriatric population, is emphasized by its pivotal role in curtailing consequences post-incident. In this paper we introduce an algorithm that harnesses the subject's acceleration, angular velocity, and orientation (pitch and roll) to ascertain fall incidents. The evaluation is based on systematic extraction and comparison of features delineated over varied temporal intervals. The presented algorithm uses data extracted from a singular sensor, amalgamating a triaxial accelerometer and a gyroscope, embedded in footwear possessing integrated computational modules. The resilience of the algorithm and efficacy are empirically validated through rigorous assessments, involving five healthy participants simulating 160 fall events and 650 activities indicative of routine locomotion. © 2023 IEEE.

关键词： Fall detection

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Decision Support System for Predicting Ventricular Arrhythmias Using Non-linear Features of ECG Signals

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SN computer science 2024年第4期5卷 357页

作者： Mohanty, Monalisa Dash, Pratyusa Sabut, Sukant Department of Center of Internet of Things ITER SOA University Bhubaneswar India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India School of Electronics Engineering KIIT Deemed to be University Bhubaneswar India

Automated methods using computer-aided decision-making process are effectively used for timely detection of VAs which are the most life-threatening conditions. In this work, we have proposed a decision support system for detection of ventricular arrhythmias (VAs) with a low computational complexity using hybrid features derived from three different transform techniques i.e. DWT, EEMD, and VMD. The methods mainly consist of a windowing technique, signal decomposition, feature extraction, and classification. About 24 time–frequency based features were extracted with 22,721 × 24 and ranked for selection of higher ranked features having maximum information of disease. The reduced feature set consists of only 6 number of highly ranked features which are then classified with SVM and decision tree classifier for efficient recognition of VAs. Aim of the reduction in data size is to reduce the computational time. Our proposed method achieves high classification accuracy in hybrid-based features and by reducing the feature dimension, it reduces the computational complexity significantly. The accuracy of 99.83% and computational time of 4.82 s is achieved when considering all 24 features. In reduced feature set, an accuracy of 99.62% with a very less computational time of 2.71 s was obtained for decision tree classifier which indicates the importance of selecting the important features for classification of VAS. With superior classification accuracy and low computation complexity, this system can be utilized in clinical practice for the recognition of ventricular arrhythmias. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Classifiers Decision support system ECG signals Hybrid features Sudden cardiac arrest

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Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

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computers, Materials & Continua 2024年第9期80卷 4749-4765页

作者： Dennies Tsietso Abid Yahya Ravi Samikannu Basit Qureshi Muhammad Babar Department of Electrical Computer and Telecommunications EngineeringBotswana International University of Science and TechnologyPalapyePrivate Bag 16Botswana College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11586Saudi Arabia

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems *** computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this ***,accurately segmenting the Region of Interest(ROI)fromthermograms remains *** paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree *** proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground *** features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)*** findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of *** consistency of the method across both sides of the breast suggests its viability as an auto-segmentation ***,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

关键词： Breast cancer CAD machine learning ROI segmentation thermography

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Homomorphic Encryption and Prim's Process for Multi-Keyword Exploration in Encrypted Cloud Data for Trades 1

Homomorphic Encryption and Prim's Process for Multi-Keyword ...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Prakash, A. Vignesh, C. Chandru Suresh, M. Pavan, Jampanisriram Ahamad, S.K. Shamshad Rama, M. QIS College of Engineering and Technology AP Ongole India School of Computer Science and Engineering Vellore Institute of Technology Department of Internet of Things Tamilnadu Vellore India

ISBN: (纸本)9798350345957

Due to its low administration costs and convenient accessibility, cloud storage is quickly becoming the norm in today's online infrastructure. By letting cloud users save their massive data in the cloud, the cloud storage system offers crucial advantages. Improving cloud storage's performance at data retrieval requires addressing concerns about data privacy and security. This study presents a new algorithm for bolstering security, called Modified Ring-based Fully Homomorphic Encryption and Prim's (MRFHE-P). The ranking function determines how the index is built for keywords and files. Prim's algorithm generates the dynamic tree based on the ordered keywords. The document is encrypted using a variant of the RSA technique called the Modified Ring-Based Fully Homomorphic Encryption Algorithm (MRFHEP). The encrypted files are indexed in a Two-way Hash Table so that users can quickly get the necessary data. Afterwards, the cloud user uses a shared symmetric key and private key to decrypt the encrypted data. The experimental results show that the suggested technique significantly reduces the time needed to encrypt and decrypt data compared to the current standard. The proposed technique also speeds up indexing and searches for a variety of documents. There was also a decrease in the amount of time spent searching and indexing, and an increase in the efficiency with which keywords could be extracted. © 2023 IEEE.

关键词： Cryptography

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Optimizing Medical Imaging: High-Performance Hardware for Image Processing, Machine Learning 24

Optimizing Medical Imaging: High-Performance Hardware for Im...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Kalyan Chakravarthy, N.S. Balapanur, Mouli Chandra Pandian, Arun Nambi Pulikanti, Jyothi Prasad, D. Syed Masood, Jafar Ali Ibrahim High-Performance Computing Research Laboratory QIS College of Engineering and Technology Andhra Pradesh Ongole India Department of Internet of Things School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

The demand for high-performance hardware solutions for machine learning tasks is growing as medical imaging evolves. In this paper, we will focus on the latest hardware advanced technologies: GPUs, TPUs and FPGAs that can be used for image processing optimization in medical imaging such as brain tumor segmentation or chest X-ray classification. We test these devices on the BraTS and ChestX-ray14 datasets to show how running models quicker increases diagnostic performance. The study offers a comprehensive performance and energy efficiency evaluation of all the considered hardware solutions when training deep learning models for medical imaging, providing clinical guidelines to choose appropriate new-generation accelerators before engaging in expensive leasing contracts. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： GPUS

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DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain 38

DAT: Improving Adversarial Robustness via Generative Amplitu...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Fengpeng Li, Kemou Wu, Haiwei Tian, Jinyu Zhou, Jiantao State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer Science City University of Hong Kong Hong Kong Faculty of Innovation Engineering Macau University of Science and Technology China

To protect deep neural networks (DNNs) from adversarial attacks, adversarial training (AT) is developed by incorporating adversarial examples (AEs) into model training. Recent studies show that adversarial attacks disproportionately impact the patterns within the phase of the sample's frequency spectrum-typically containing crucial semantic information-more than those in the amplitude, resulting in the model's erroneous categorization of AEs. We find that, by mixing the amplitude of training samples' frequency spectrum with those of distractor images for AT, the model can be guided to focus on phase patterns unaffected by adversarial perturbations. As a result, the model's robustness can be improved. Unfortunately, it is still challenging to select appropriate distractor images, which should mix the amplitude without affecting the phase patterns. To this end, in this paper, we propose an optimized Adversarial Amplitude Generator (AAG) to achieve a better tradeoff between improving the model's robustness and retaining phase patterns. Based on this generator, together with an efficient AE production procedure, we design a new Dual Adversarial Training (DAT) strategy. Experiments on various datasets show that our proposed DAT leads to significantly improved robustness against diverse adversarial attacks. The source code is available at https://***/Feng-peng-Li/DAT. © 2024 Neural information processing systems foundation. All rights reserved.

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Learning Fine-Grained Information with Capsule-Wise Attention for Salient Object Detection

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IEEE Transactions on Multimedia 2023年 1-14页

作者： Zhao, Sanyuan Wen, Zongzheng Qi, Qi Lam, Kin-Man Shen, Jianbing School of Computer Science & Technology Beijing Institute of Technology China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macau China

With the popularity of convolutional neural networks being used for salient object detection (SOD), the performance has been significantly improved. However, how to integrate crucial features for modeling salient objects needs further exploration. In this work, we propose an effective feature selection scheme to solve this task. Firstly, we provide a Simplified Atrous Spatial Pyramid Pooling (SASPP) module to lightweight the multi-scale features. Dealing with the SASSP features, we design a pixel-level local feature selection scheme named Multi-Scale Capsule-wise Attention (MSCA). It aggregates features from multi-scales by dynamic routing and helps the network to generate fine-grained prediction maps. In addition, we exploit holistic features by the Spatial-wise Attention and Channel-wise Attention (SA/CA) mechanisms, which adaptively extracts spatial or channel information. We also propose a Multi-crossed Layer Connections (MLC) structure in the upsampling stage, to fuse features from not only different levels but also different scales. The salient object prediction is performed in a coarse-to-fine manner. By conducting comprehensive experiments on five benchmark datasets, our method achieves the best performance when compared to existing state-of-the-art approaches. IEEE

关键词： Object detection

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