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Graph of Neural Networks for pattern recognition

Graph of Neural Networks for pattern recognition

引用

16th international conference on pattern recognition (ICPR)

作者： Cardot, H Lezoray, O IUT SRC LUSAC F-50000 St Lo France

ISBN: (纸本)076951695X

This paper presents a new architecture of neural networks designed for pattern recognition. The concept of induction graphs coupled with a divide-and-conquer strategy defines a Graph of Neural Network (GNN). It is based on a set of several little neural networks, each one discriminating only two classes. The principles used to perform the decision of classification are : a branch quality index and a selection by elimination. A significant gain in the global classification rate can be obtained by using a GNN. This is illustrated by tests on databases from the UCl machine learning database repository. The experimental results show that a GNN can achieve an improved performance in classification.

关键词： pattern recognition

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SMARTINDUstRY 2024 - Proceedings of the 1st international conference on Smart Automation and Robotics for Future Industry

SMARTINDUSTRY 2024 - Proceedings of the 1st International Co...

引用

1st international conference on Smart Automation and Robotics for Future Industry, SMARTINDUstRY 2024

The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach with the DIAFS machine learning model;development of an artificial intelligence tool and sensing in informatization systems of mobile robots;PCA-NuSVR framework for predicting local and global indicators of tunneling-induced building damage;design and deployment of data development toolkit in cloud manufacturing environments;research and development of image processing algorithms for effective recognition of various gestures in real time;machine learning models for the recognition of commands in smart home technologies;responsive dehydration: sensor-driven optimisation of production cycles in a solar dehydrator;and formation of the method of description and control of the relative position of the links of the upper limbs of the grip of an anthropomorphic robot.

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Optimally robust, fast-learning, pattern recognizer derived from a noniterative neural network learning theory

Optimally robust, fast-learning, pattern recognizer derived ...

引用

Proceedings of the 1997 1st international conference on Information, Communications and Signal processing, ICICS. Part 3 (of 3)

作者： Hu, Chia-lun John Southern Illinois Univ at Carbondale Carbondale United States

It is proved analytically that, whenever the input-output mapping of a one-layered, hard-limited perceptron satisfies a positive, linear independency (PLI) condition, the connection matrix A to meet this mapping can be obtained noniteratively in one step from an algebraic matrix equation containing an N×M input matrix U. Each column of U is a given standard pattern vector, and there are M standard patterns to be classified. It is also analytically proved that sorting out all nonsingular sub-matrices Uk in U can be used as an automatic feature extraction process in this noniterative-learning system. This paper reports the theory, the design, and the experiments of a superfast-learning, optimally-robust, neural network pattern recognition system derived from this novel noniterative learning theory. An unedited video movie showing the speed of learning and the robustness in recognition of this novel pattern recognition system will be demonstrated in life. Comparison to other neural network pattern recognition and feature extraction systems will be discussed in III.

关键词： pattern recognition systems

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Automatic Licence Plate Detection and recognition System using image processing Techniques 1

Automatic Licence Plate Detection and Recognition System usi...

引用

1st international conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Ganesh, Peddi Shyam Srilekha, B. Sravanthi, Vukanti Siddhartha Institute of Technology & Sciences Department of ECE Telangana India

ISBN: (纸本)9798350361780

The recent exponential surge in the number of vehicles on our roadways has made congestion and violations important problems. By automating traffic management using an ALPR system, we can improve access control systems, streamline traffic flow, and save time. Here, we look at how well algorithms for edge processing and morphological processing perform. The parameters of the neural network, including the regularisation parameter, the count of hidden layer units, and the count of iterations, are optimised with great care. With a focus on real-time implementation and control via a graphical user interface, this case presents a parking security system suitable for usage in office buildings, institutions, malls, etc. Operating on Raspberry Pi 2B and Matlab, the system accomplishes a 97% efficiency rate through the use of image processing techniques and machine learning algorithms. © 2024 IEEE.

关键词： Traffic congestion

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Face recognition of Blurred images Using image Enhancement and Texture Features 1

Face Recognition of Blurred Images Using Image Enhancement a...

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1st international conference on Next Generation Computing Technologies (NGCT)

作者： Kapil, Deeksha Abhilasha Dept Comp Sci GZS PTU Campus Bathinda India CSE Dept Bathinda India

ISBN: (纸本)9781467368094

Face recognition is an existing and one of most prominent technique of biometrics that includes processing of an image and to be matched with different database. In this procedure, the user matches one person identity with several database images. Various approaches like Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Local Ternary pattern (LTP) have been used for the purpose of face recognition. These approaches use different features for face recognition purpose. The features used for face recognition are shape, distance between two traits of face and texture features. Texture features are particularly susceptible to the resolution of images, when the resolution changes the calculated textures are not accurate. Texture features computed for low resolution images does not provide better feature information. So there is a big issue in face recognition for low resolution images. In the proposed work, EULBP (Equalized Uniform Local binary pattern) has been implemented for the purpose of low resolution images, but it does not provide better results up to an extent. To improve the recognition accuracy, the aim of this research is to study various approaches and development of new approach used for recognition purpose on low resolution images using texture features of an image which can provide better results.

关键词： DCT DWT LTP EULBP ORL database face recognition feature extraction

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1st international conference on machine Intelligence and Emerging Technologies, MIET 2022

1st International Conference on Machine Intelligence and Eme...

引用

1st international conference on machine Intelligence and Emerging Technologies, MIET 2022

ISBN: (纸本)9783031346217

The proceedings contain 104 papers. The special focus in this conference is on machine Intelligence and Emerging Technologies. The topics include: A Reliable and Efficient Transfer learning Approach for Identifying COVID-19 Pneumonia from Chest X-ray;Infection Segmentation from COVID-19 Chest CT Scans with Dilated CBAM U-Net;Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-Ray images;classification of Tumor Cell Using a Naive Convolutional Neural Network Model;Tumor-TL: A Transfer learning Approach for Classifying Brain Tumors from MRI images;deep Convolutional Comparison Architecture for Breast Cancer Binary Classification;lung Cancer Detection from Histopathological images Using Deep learning;brain Tumor Detection Using Deep Network EfficientNet-B0;cancer Diseases Diagnosis Using Deep Transfer learning Architectures;false Smut Disease Detection in Paddy Using Convolutional Neural Network;transfer learning Based Skin Cancer Classification Using GoogLeNet;Assessing the Risks of COVID-19 on the Health Conditions of Alzheimer’s Patients Using machine learning Techniques;MRI Based Automated Detection of Brain Tumor Using DWT, GLCM, PCA, Ensemble of SVM and PNN in Sequence;Performance Analysis of ASUS Tinker and MobileNetV2 in Face Mask Detection on Different Datasets;fake Profile Detection Using image processing and machine learning;A Novel Texture Descriptor Evaluation Window Based Adjacent Distance Local Binary pattern (EADLBP) for image Classification;bornomala: A Deep learning-Based Bangla image Captioning Technique;traffic Sign Detection and recognition Using Deep learning Approach;a Novel Bangla Spoken Numerals recognition System Using Convolutional Neural Network;Bangla Speech-Based Person Identification Using LstM Networks;gabor Wavelet Based Fused Texture Features for Identification of Mungbean Leaf Diseases;VADER vs. BERT: A Comparative Performance Analysis for Sentiment on Coronavirus Outbreak.

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pattern recognition Using Neighborhood Coding

Pattern Recognition Using Neighborhood Coding

引用

4th international conference on Computer Science and Informatics, JCIS 1998, 1st international Workshop on High Performance, 1st international Workshop on Computer Vision, pattern recognition and image processing Volume 4

作者： Tsang, I.R. Tsang, I.J. Scheunders, P. Van Dyck, D. Vision Lab Department of Physics University of Antwerp Groenenborgerlaan 171 Antwerp B-2020 Belgium

We propose a new coding algorithm for binary images based on neighborhood relations. The shape is transformed into a set of representative code vectors (position invariant) by coding each pixel according to the number of neighbors in the four directions (north, east, south, west). These neighborhood vectors are then transformed into a set of codes satisfying the boundary condition given by the size of the image where the shape is imbedded. A code reduction scheme is proposed for the purpose of information reduction and generalization of the shape image. Using the digits 1 and 0 of the NIst handwritten segmented characters set we show a preliminary application for pattern recognition.

关键词： pattern recognition

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1st international conference on machine Intelligence and Emerging Technologies, MIET 2022

1st International Conference on Machine Intelligence and Eme...

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1st international conference on machine Intelligence and Emerging Technologies, MIET 2022

ISBN: (纸本)9783031346187

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image Pre-processing Detection: Evaluation of Benford's Law, Spatial and Frequency Domain Feature Performance 1

Image Pre-Processing Detection: Evaluation of Benford's Law,...

引用

1st international Workshop on Sensing, processing and learning for Intelligent machines (SPLINE)

作者： Neubert, Tom Hildebrandt, Mario Dittmann, Jana Otto von Guericke Univ Dept Comp Sci Univ Pl 2 D-39106 Magdeburg Germany Otto von Guericke Univ Dept Comp Sci Res Grp Multimedia & Secur POB 4120 D-39016 Magdeburg Germany

ISBN: (纸本)9781467389174

This Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of the trained models indicates average accuracies between 82.50 and 94.53%. The investigation of image data from 8 sensors leads to the detection of credible pre-processing filters. Those results adumbrate that our method might be suitable to prove the authenticity of the data origin and the integrity of image data based on the detected preprocessing techniques. The preliminary evaluation for manually applied filters yields recognition accuracies between 39.09% (14 classes) and 53.33% (7 classes).

关键词： Frequency domain analysis

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Estimating planar structure in single images by learning from examples

Estimating planar structure in single images by learning fro...

引用

1st international conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Haines, Osian Calway, Andrew University of Bristol Bristol United Kingdom

ISBN: (纸本)9789898425980

Outdoor urban scenes typically contain many planar surfaces, which are useful for tasks such as scene reconstruction, object recognition, and navigation, especially when only a single image is available. In such situations the lack of 3D information makes finding planes difficult;but motivated by how humans use their prior knowledge to interpret new scenes with ease, we develop a method which learns from a set of training examples, in order to identify planar image regions and estimate their orientation. Because it does not rely explicitly on rectangular structures or the assumption of a'Manhattan world', our method can generalise to a variety of outdoor environments. From only one image, our method reliably distinguishes planes from non-planes, and estimates their orientation accurately;this is fast and efficient, with application to a real-time system in mind.

关键词： Real time systems

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