检索结果-内蒙古大学图书馆

2021 International symposium of Asian Control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Murugadoss, Bhuvanashree Karna, Sai Nikhilesh Reddy Kode, Jai Surya Subramani, R. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9781665433235

Copyright protection of digital images has become vital in the modern era where multimedia content is the dominant form of traffic in networked multimedia communication systems. Hence, there is a need for building technologies around robustness and computational efficiency for watermark embedding and extraction through state-of-the-art cryptographic algorithms. In this paper, we have proposed a blind image watermarking system that applies Henon chaotic map for watermark embedding and Elliptic Curve Cryptography (ECC) for securely sending the algorithmic parameters to the receiver. Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) are utilised for hybrid decomposition of the host image. The metrices used for the evaluation of the robustness and imperceptibility of this watermarking system are the Peak signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NC) and the Central processing Unit (CPU) runtime was recorded in order to assess the system's execution speed. © 2021 ieee.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition

引用

BIOMEDICAL signal processing AND CONTROL 2023年第1期84卷

作者： Kouka, Najwa Fourati, Rahma Fdhila, Raja Siarry, Patrick Alimi, Adel M. Natl Engn Sch Sfax ENIS Univ Sfax Res Grp Intelligent Machines BP 1173 1173 Sfax 3038 Tunisia Univ Paris Est Lab Image Signaux & Syst Intelligents LISSI F-94400 Creteil France Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

Electroencephalography (EEG) signals can demonstrate the activities of the human brain and recognize different emotional states. Emotion recognition based on full EEG channels leads to the use of redundant data and increases the hardware complexity. This paper presents a channel selection method based on a new Binary Many-Objective Particle Swarm Optimization with Cooperative Agents (BMaOPSO-CA). More specifically, we perform unsupervised feature learning with recurrent convolutional layers based on autoencoder architecture directly from clean EEG signals. Extensive validation on three public effective benchmarks, i.e. DASPS, DEAP, and SEED which are different in channel number used stimuli, and participant ratings, was carried out with subject independent scheme. As result, the experimental study highlights the optimal electrode locations related to emotions, leading to analyze the relationship between specific brain regions and emotions.

关键词： Unsupervised EEG feature learning Convolutional LSTM Channel selection Binary particle swarm optimization Local learning strategy

来源：评论

学校读者我要写书评

暂无评论

Recognition of Handwritten Digit Using Neural Networks

Recognition of Handwritten Digit Using Neural Networks

引用

Sustainable Energy, signal processing and Cyber Security (iSSSC), ieee International symposium on

作者： Kancherla Santoshi Chunduru Anilkumar Suvvari Sravanthirani Bhardwaj Department of Information Technology GMR Institute of Technology Rajam Srikakulam (dist.) AP India

ISBN: (纸本)9781665490573

Because of its practical uses in our daily lives, manuscript number identification is gradually relevant in the present times. Numerous identification techniques such as calligraphy number identification are deployed in many areas where great classification efficiency is required in recent years. In addition pattern recognition is a key component of any computer vision or artificial intelligence system. Machine Learning as well as Computer Vision researchers have used it extensively to build practical applications such as reading computerized bank draft numbers. In this project, we utilized an ANN to develop several surfaces attached development of a thinking computer system with two invisible surfaces to recognize manuscript numbers. The backpropagation method searches for the weight value that delivers the lowest overall error in the network while data processing between layers. Invisible layer neurons employ the ReLU activation function, whereas output neurons use SoftMax. The testing was done using the MNIST handwritten database, which is freely available. Finally retrieved a complete 60,000 digit pictures from the MNIST database for training, validation and testing.

关键词： Training Handwriting recognition Computer vision image recognition Databases Neurons Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Efficient iris segmentation and pupil detection for visagisme applications 14

Efficient iris segmentation and pupil detection for visagism...

引用

ieee 14th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Tudor-Alexandru, Ileni Babes Bolyai Univ Comp Sci Dept Cluj Napoca Romania

ISBN: (纸本)9781728173764

This paper explores the problem of automated iris segmentation and pupil detection. The motivation to develop such a system is that an accurate detection of the eye, iris and pupil provides useful information for many applications from various domains, such as medicine, biometrics, and fashion industry. The paper features the following contributions: training of a fast eye landmarks detector, an original and precise algorithm for an estimating the center of the pupil and the iris margin, especially where the eyelashes cover it, and the manual annotation of a new benchmark dataset for testing purpose. The proposed method is proved to be very accurate: for all detected points, the error is smaller than an approximation of the diameter of the pupil.

关键词： iris segmentation pupil detection machine learning image processing

来源：评论

学校读者我要写书评

暂无评论

FST-Net: Facial Soft Tissue Landmark Localization on 3dMD Scans Using Feature Fusion and Local Coordinate Regression

FST-Net: Facial Soft Tissue Landmark Localization on 3dMD Sc...

引用

ieee International symposium on Biomedical Imaging

作者： Zhidong He Han Bao Mingzhang Chen Jiasong Wu Luwei Liu Lotfi Senhadji Huazhong Shu Bin Yan Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education) Southeast University Nanjing China Department of Orthodontics The Affiliated Stomatological Hospital of Nanjing Medical University Nanjing China Centre de Recherche en Information Biomédicale Sino-Français Rennes France INSERM LTSI-UMR 1099 Univ-Rennes Rennes France

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Landmark localization of facial soft tissue (FST) is a basic step in 3D morphometric analysis of human face. However, there are few studies on landmark localization of 3D scan images based on deep learning. The methods based on 2D images cannot be directly applied due to the non-Euclidean data structure. In this paper, we propose an end-to-end learning framework to automatically localize 28 landmarks on 3dMD scans, called FST-Net. Our method extracts features from texture images and mesh models. New texture mappings for 3dMD scans are generated by projection to fuse texture and structure features. A dual-branch network integrating transformers is applied to predict the landmark heatmaps from coarse to fine. A local coordinate regression module based on probabilistic distance and heatmap predictions is proposed to compute the landmark coordinates. We collect and annotate 297 3dMD face scans from the clinic to evaluate our model. Experiments show that the average localization error of the model is 1.204mm (the clinically acceptable precision range is 1.5 mm), and the correct landmark detection rate equals to 70.89%. Our model outperforms the current state-of-the-art deep learning methods for landmark localization on the mesh model.

关键词： Location awareness Deep learning Heating systems Three-dimensional displays Accuracy computational modeling Biological tissues

来源：评论

学校读者我要写书评

暂无评论

A Multi-target Edge Service Approach to Real-time image Object Detection 13

A Multi-target Edge Service Approach to Real-time Image Obje...

引用

13th International symposium on computational intelligence and Design (ISCID)

作者： Xin, Tinglin Li, Shuo Zhao, Ting Xia, Weishang Zhao, Lijiao State Grid Jibei Informat & Telecommun Co Beijing 100054 Peoples R China Global Energy Interconnect Res Inst Co Ltd Beijing 102200 Peoples R China

ISBN: (纸本)9781728184463

The networked devices over the IoT environments have accumulated a large volume of data, and therefore it becomes challenging for many typical surveillance applications to handle and analyze these massive real-time data. In this paper, we propose a multi-target approach to real-time object detection for surveillance applications that can be used as an edge service for handling and analyzing the real-time massive data acquired by a huge number of terminal devices. A lightweight multi-layered Convolutional Neural Network based on Darnet-53 structure is adopted to learn the multi-target features in training dataset for classifying and detecting image objects in unseen image instances, which is. A pre-processing approach including image screening, image enhancement and manual category annotation is presented for building the training dataset of surveillance applications. At last, extensive experiments were made to validate the effectiveness and efficiency of handling and analyzing the real-time massive data based on the proposed approach.

关键词： Edge computing edge service surveilance applications multi-target object detection

来源：评论

学校读者我要写书评

暂无评论

Reversible Data Hiding Using Convolutional Neural Network and Digital signal processing Techniques

Reversible Data Hiding Using Convolutional Neural Network an...

引用

ieee International symposium on Information (IT) in Medicine and Education, ITME

作者： Peng Qingmei Li Shaozi Lin Yaojin Yu Xiehua Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province MinNan Science and Technology University Quanzhou China School of Informatics Xiamen University Xiamen China School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. Based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development.

关键词： Training System testing signal processing algorithms Digital signal processing Reliability Cryptography Time factors

来源：评论

学校读者我要写书评

暂无评论

An End-to-end Computer Vision System Architecture

An End-to-end Computer Vision System Architecture

引用

ieee International symposium on Circuits and Systems

作者： Ling Zhang Wei Zhou Xiangyu Zhang Xin Lou Schoo of Inforion Science nd Technoogy ShnghiTech UniversiySchoo of Inforion Science nd Technoogy ShnghiTech UniversiySchoo of Inforion Science nd Technoogy ShnghiTech UniversiySchoo of Inforion Science nd Technoogy ShnghiTech Universiy

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

To overcome the data movement bottleneck, near-sensor and in-sensor computing are becoming more and more popular. However, in the existing near-/in-sensor computing architectures for vision tasks, the effect of the image signal processing (ISP) pipeline, which is of great importance to the final vision performance [1], is always ignored. In this work, we propose a synthesized RAW image-based end-to-end computer vision paradigm, taking the effect of ISP pipeline into account. In the proposed approach, a generative adversarial network (GAN)-based tool is used to convert the fully processed color images to their corresponding RAW Bayer versions, generating the training data for end-to-end vision models. In the inference stage, RAW images from the sensor are directly fed to the end-to-end model, bypassing the entire ISP pipeline. Experimental results show that by training/tuning the CNN models using synthesized RAW images, it is possible to design an end-to-end (from RAW image to vision task) vision system that directly consumes RAW image data from the sensor with negligible vision performance degradation. By skipping the ISP pipeline, an image sensor can be directly integrated with the back-end vision processor without a complex image processor in the middle, making near-/in-sensor computing a practical approach.

关键词： image sensors Computer vision Machine vision computational modeling Pipelines Training data signal processing

来源：评论

学校读者我要写书评

暂无评论

Fuzzy-Based Atrous Convolution for Brain Tumor Detection Using MRI

Fuzzy-Based Atrous Convolution for Brain Tumor Detection Usi...

引用

symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Muhammad Irfan Abdulhamit Subasi Hassan Mehdi Tomi Westerlund Wei Chen Fudan University Shanghai University of Turku Finland Turku Finland Information Sciences and Technology University at Albany New York USA University of Jyväskylä Finland Jyväskylä Finland University of Turku Finland Turku Finland University of Sydney Australia Sydney Australia

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Early detection and diagnosis of brain tumors are crucial for improving patient outcomes. While convolutional neural networks (CNNs) have demonstrated promise in detecting tumors from magnetic resonance images (MRI), traditional meth-ods often suffer from overparameterization, resulting in lower accuracy improvements despite higher computational demands. To address this, we propose an efficient approach, Fuzzy Atrous Convolution (FAC), with two additional modules: Top of the funnel and middle of the funnel. In this approach, fewer trainable parameters are required while maintaining high classification accuracy. A novel convolutional operator is introduced in the FAC model that dilates the receptive field while preserving input data, enabling efficient feature map reduction and accurate tumor detection. In the propose model, fuzzy logic improves adaptability and robustness. Experiments on three datasets confirmed the efficiency of proposed approach (FAC) and it achieved accuracies of 98.83%, 99.67%, and 99.56% on Datasets I, II, and III, respectively. Compared with large transfer learning models, Proposed approach has over 300 times fewer parameters, making them efficient for early brain tumor detection.

关键词： Adaptation models Accuracy Convolution Magnetic resonance imaging computational modeling Transfer learning Brain tumors Brain modeling Robustness Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

EXPLAINABLE AI (XAI) IN BIOMEDICAL signal AND image processing: PROMISES AND CHALLENGES

arXiv

引用

arXiv 2022年

作者： Yang, Guang Rao, Arvind Fernandez-Maloigne, Christine Calhoun, Vince Menegaz, Gloria National Heart and Lung Institute Imperial College London United Kingdom Dept. of Computational Medicine and Bioinformatics University of Michigan Ann Arbor United States I3M Laboratory University of Poitiers CNRS France TReNDS Center Georgia State Georgia Tech Emory AtlantaGA United States Dept. of Computer Science University of Verona Italy

Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical image and signal processing of the ieee signal processing Magazine that is going to appear in March 2022. © 2022, CC BY-NC-ND.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：