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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Data recovery in two-dimensional read-out of optical tape

Data recovery in two-dimensional read-out of optical tape

引用

Optical Data Storage

作者： A.D. Simone P.A. Mitkas G.A. Betzos Department of Electrical Engineering Colorado State University Fort Collins CO USA

Optical tape offers the greatest volumetric storage density of any commercially available storage medium, and thus is a cost effective alternative to traditional forms of archival storage. An optical tape recorder utilizing linear scanning with a single reel storage capacity of one terabyte has been commercially available for some time. We utilize a two-dimensional read-out based on linear scanning which provides a page oriented format similar to that of a holographic memory. Two-dimensional optical memories promise to provide high storage capacities, making them ideal candidates for archival storage in future computing applications. This extension of channel capability has the potential to increase the read bandwidth of the storage system allowing faster retrieval of a larger file space than traditional archival storage. Data recovery with image processing techniques and with an artificial neural network are discussed.

关键词： Optical network units Holography Holographic optical components Optical recording Optical computing History neural networks Relational databases Intelligent networks Backpropagation algorithms

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TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR ...

引用

IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Rong Li Shijie Li Xieyuanli Chen Teli Ma Juergen Gall Junwei Liang HKUST(GZ) China University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany HKUST China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain, including point-based, range-image-based, polar-coordinate-based, and hybrid strategies. Among these, range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However, they face a significant challenge known as the "many-to-one" problem caused by the range image ’s limited horizontal and vertical angular resolution. As a result, around 20% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the "many-to-one" issue. We evaluated the approach on two benchmarks and demonstrated that the plugin post-processing technique is generic and can be applied to various networks.

关键词： Training Laser radar Three-dimensional displays image resolution Semantic segmentation Face recognition neural networks

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A Review of image Classification and Object Detection on Machine learning and Deep Learning Techniques

A Review of image Classification and Object Detection on Mac...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Ms. R.S. Sandhya Devi V.R. Vijay Kumar P. Sivakumar Kumaraguru College of Technology Coimbatore India Anna University Regional Campus Coimbatore India PSG College of Technology Coimbatore India

ISBN: (纸本)9781665435253

One of the known computer vision tasks is image recognition and classification. Computer vision - a field that automates tasks and works with multi - dimensional data, when combined with the power of artificial intelligence mimics human visual system. image processing and analysis is an integral part of computer vision. image classification, a vital part in image analysis is used to categorize pixels or classes of interest by extracting information classes from a multiband raster image. Automatic sorting of pixels and analogous multispectral reflectance values are widely used in land cover mapping cases where remote sensing and Geographical Information System (GIS) is integrated. Designing of image classification and processing procedure by considering various factors involved, decides the success of the classification approach being used. This paper provides a summarization of various classical machine learning based classification approaches and the major advanced deep learning based classification techniques, issues affecting the success of each classification approach and its applications. Convolutional neural Network (CNN) is the most popular deep network architecture used to analyze visual imagery. This paper discusses waste object detection, a part of the waste segregation process based on CNN hierarchical image classification approach. Here, the system is able to detect the object and provides the relative match percentage of the object being detected. Open source software libraries such as Tensorflow and Spyder is used for this process

关键词： Deep learning Computer vision Machine learning algorithms Object detection Network architecture Classification algorithms Convolutional neural networks

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Advancements in Computer Vision: A Comprehensive Review

Advancements in Computer Vision: A Comprehensive Review

引用

Innovations and Challenges in Emerging Technologies (ICICET), International conference on

作者： Poorva Agrawal Reyon Bose Gopal Kumar Gupta Gagandeep Kaur Samiksha Paliwal Aarya Raut Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350319019

ISBN: (纸本)9798350319026

Computer vision is a versatile area that allows a computer to understand and analyze images from the environment. This paper focuses on a comprehensive discussion of where computer vision is today in light of its past, most recent accomplishments, and predictions of where it could go next. The evolution of computer vision over the past few decades has been marked by remarkable progress, driven by advancements in three key areas: utilize deep learning, hardware, and image-processing methods. artificial neural networks are now commonly used in deep learning, which has improved the abilities of machines to recognize, classify, and understand visual content. Computationally speaking, parallel with deep learning, other kinds of hardware, like GPU chips, can be used to perform such functions better. This has made it easy to train and deploy complex deep learning algorithms for real-time computer vision applications with expanded reach into many facets. In addition, image processing has been dramatically improved, making it possible to process and enhance incoming visual data. Traditional and most recent techniques for Computational Photography have enabled the dependable and precise computer-vision system.

关键词： Deep learning Computer vision Visualization Technological innovation Reviews image processing Transforms

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Anomaly Based Intrusion Detection for a Biometric Identification System using neural networks

Anomaly Based Intrusion Detection for a Biometric Identifica...

引用

International conference on artificial Intelligence and image processing(ICAiiP’2012);International conference on Innovations in Electrical and Electronics Engineering(ICIEE’2012);International conference on Mechanical,Automobile and Biodiesel Engineering(ICMABE’2012);International conference on History,Literature and Management(ICHLM’2012);International conference on Agriculture,Chemical and Environmental Sciences(ICACES’2012);International conference on Biological and Medical applications(ICBMA’2012)

作者： Tinny Mgabile Ishmael S.Msiza Erick Dube the CSIR Modeling & Digital Science the Faculty of Engineering & the Built Environment University of Johannesburg

This manuscript presents a supervised machine learning approach in the identification of network attacks on a fingerprint biometric *** reduce the problem of malicious acts on a biometric system,this manuscript proposes an intrusion detection technique that analyses the fingerprint biometric network traffic for evidence of *** neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic pattern on a fingerprint biometric *** aim of the study is to observe the ability of the neural network in the detection of known and unknown attacks without using a vast amount of training *** results of the neural network model had a classification rate of 98 %,which translates to a false positive rate of 2%.

关键词： biometric system,neural network,machine learning,intrusion detection.

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A Next-Generation Secure Cloud-Based Deep Learning License Plate Recognition for Smart Cities

A Next-Generation Secure Cloud-Based Deep Learning License P...

引用

International conference on Machine Learning and applications (ICMLA)

作者： Rohith Polishetty Mehdi Roopaei Paul Rad Department of Computer Science The University of Texas San Antonio Department of Electrical and Computer Engineering The University of Texas San Antonio

ISBN: (纸本)9781509061686

License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS is addressed in the context of efficiency where accuracy and speed of processing plays a critical role towards its success. Signature-based features technique as a deep convolutional neural network in a cloud platform is proposed for plate localization, character detection and segmentation. Extracting significant features makes the LPRS to adequately recognize the license plate in a challenging situation such as i) congested traffic with multiple plates in the image ii) plate orientation towards brightness, iii) extra information on the plate, iv) distortion due to wear and tear and v) distortion about captured images in bad weather like as hazy images. Furthermore, the deep learning algorithm computed using bare-metal cloud servers with kernels optimized for NVIDIA GPUs, which speed up the training phase of the CNN LPDS algorithm. The experiments and results show the superiority of the performance in both recall and precision and accuracy in comparison with traditional LP detecting systems.

关键词： Feature extraction Licenses Character recognition image edge detection Kernel neural networks

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A Verilog based Approach for Object Detection using CNN

A Verilog based Approach for Object Detection using CNN

引用

Global conference for Advancement in Technology (GCAT)

作者： Samson Swaraj Quadros S. Adityakrishna Kirti S. Pande Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

A Convolutional neural Network (CNN) are a class of artificial neural networks specifically designed to process data with a grid-like topology, such as images, making them well-suited for tasks like image recognition and classification, object detection, and speech recognition. However, their current software implementations leave much to be desired regarding energy efficiency, speed, performance and scalability. Hardware-based implementation of CNNs has several significant advantages over software, including faster data processing due to the parallel execution of hardware-based FPGAs and ASICs, which are necessary for real-time applications. They are more energy-efficient and have consistent, predictable performance. In this paper, we present the implementation of CNN on Verilog. We implemented a highly optimized CNN regression model architecture for object detection having an accuracy of 97%. The entire design was made on Verilog, allowing easy transferability to both FPGA and ASIC platforms. The proposed work is compared to the current standard for software implementations – Google Colab. The results obtained show considerable speed up and improved performance.

关键词： Scalability Object detection Speech recognition Software Energy efficiency Topology Convolutional neural networks Hardware design languages Field programmable gate arrays Standards

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Register Transfer Level Model For CNN Tumor Detection on FPGA

引用

International conference on Microelectronics, ICM

作者： Omar M. Elsayed Samar M. Ismail Mohamed A. Abd El Ghany Faculty of Information and Engineering and Technology (IET) German University in Cairo (GUC) Egypt School of Engineering and Computer Science University of Hertfordshire (UH-GAF) Egypt

ISBN: (纸本)9781665408400

With the increasing demand on artificial intelligence in various daily life applications, a hardware design is proposed in this work for a convolutional neural network (CNN), for brain tumor classification. In terms of the type of data each machine learning technique uses or how the training process works, the CNN was chosen in this work because of its architecture, which was created exclusively to have picture input and its efficiency in image processing in general. The training phase is performed based on CNN using a brain tumor dataset in MATLAB. Then, a Register Transfer Level (RTL) inference model is built to use the parameters resultant from the training process (weights and biases), to be implemented on FPGA using Vivado HLS. The proposed model achieved higher accuracy than previous models in literature.

关键词： Training Brain modeling Mathematical models Hardware Registers Microelectronics Convolutional neural networks

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A comparison between ANN and SVM classifier for drowsiness detection based on single EEG channel

A comparison between ANN and SVM classifier for drowsiness d...

引用

International conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Ibtissem Belakhdar Walid Kaaniche Ridha Djmel Bouraoui Ouni National Engineering School of sousse University of sousse Sousse Tunisia Faculty of Sciences University of Monastir Monastir Tunisia King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9781467385275

In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and easy to use by the driver. In this respect, we propose to evaluate the performance of thise two classifiers used for EEG classification in order to select the most appropriate one which can provide higher classification accuracy. The validation process is conducted on EEG signals of the polysomnography database where EEG signals of 10 persons have been recorded from C3-O1 region. The signal read from the dataset mentioned above is segmented into 30 second windows then features are extracted from these segments using Fast Fourier Transform (FFT). These features are fed to ANN and SVM to select the most appropriate one. To evaluate the performance of the classifier we have used two metrics: the accuracy of classifier and the Receiver Operating Characteristic (ROC) curve. Based on this study, we conclude that the ANN classifier is better than SVM for the EEG drowsiness signals when using one EEG channel.

关键词： Support vector machines Electroencephalography artificial neural networks Feature extraction Neurons Kernel Training

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An improved neural-network-based face detection and facial expression classification system

An improved neural-network-based face detection and facial e...

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IEEE International conference on Systems, Man and Cybernetics

作者： I.-O. Stathopoulou G.A. Tsihrintzis Department of Informatics University of Piraeus Piraeus Greece

Automatic facial expression recognition is one of the most difficult and important problems in the scientific areas of cybernetics, pattern recognition and computer vision and their technological applications. New, friendlier human-computer interaction modes and multimedia interactive services require processing of images obtained with the use of multiple cameras to detect the presence and location of computer users' faces and determine their affective state. In order to be fully automated, the system proposed in this paper, consists of two modules: firstly, we use a face detection algorithm to determine whether or not there are any faces in the image and, if so, return the location and extent of each face, and secondly, we use a facial expression classification module to determine the facial expression of a person

关键词： Face detection Psychology Informatics Face recognition Pattern recognition Application software Feature extraction artificial neural networks Humans State estimation

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