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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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Authentic Gate Entry System (AuthGES) by Using LBPH for Smart Home Security

Authentic Gate Entry System (AuthGES) by Using LBPH for Smar...

引用

international conference on Networking and Network Applications (NaNA)

作者： Bhattarai, Krishna Prasad Sato, Kazuhiko Gautam, Bishnu Prasad Muroran Inst Technol Div Informat & Elect Engn Muroran Hokkaido Japan Muroran Inst Technol Coll Informat & Syst Muroran Hokkaido Japan Wakkanai Hokusei Gauen Univ Depeartment Integrated Media Wakabadai 1-2290-28 Wakkanai Hokkaido Japan

ISBN: (纸本)9781538683033

In this paper, a smart home security system by using local binary pattern histograms (LBPH) face detection algorithm is proposed to enhance the security level of entry-system. Face recognition is an interesting but challenging in machine learning field and impacts important applications in many areas such as remote sensing, machine/robot vision, pattern recognition, medical field, banking and security system access, and authentication in personal electronics gadget. In this research paper, we proposed the door lock security system using image processing instead of traditional key and digital lock system. The image processing mainly consists of three parts, namely face representation, feature extraction and identification of face. Face representation represents how to model a face with LBPH algorithms of detection and recognition. The most useful and unique features of the face image are extracted in the feature extraction phase. In the identification of face the new face image is compared with the images which are already extracted and saved on database. Face detection and recognition method was applied to allow the authorized dwellers and the guest and prevent unwanted person to enter inside the house.

关键词： Face Detection LBPH Algorithm machine learning Feature Extraction Authentic Gate Entry System

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Using Adaptive Multiwavelet Transform Based on Evidence Theory for image processing

Using Adaptive Multiwavelet Transform Based on Evidence Theo...

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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： Akhavan, Saeid Nezafat, Reza DEPFI UNAM Mexico D. F. Mexico Tehran University Tehran Iran

In this paper we propose an evidencial measure for Multiwavelet best basis selection. The main idea is like Wavelet Packet, WP with the best basis selection where the filter applied to the signal varies adaptically with time. Here instead of entropy, we use the plausibility state(the degree of beliefs in the merit) in the image, because we can gain other measure that is more sophisticated. Finally we approve the efficiency of using Multiwavelets with adaptive tree structure with aoolying to image processing.

关键词： image processing

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Research on intelligent decision support system based on the machine learning 2018

Research on intelligent decision support system based on the...

引用

1st international conference on Information Hiding and image processing, IHIP 2018

作者： Liu, Yisu Zhao, Yingjie Northeastern University at Qinhuangdao Control Engineering Institute Qinhuangdao Hebei066004 China

ISBN: (纸本)9781450365468

The rapid development of machine learning technology provides a foundation for the construction of the new generation of intelligent decision support system. In this paper, the latest development of intelligent decision system and machine learning technology is studied. Some problems in intelligent decision system based on machine learning are discussed. The concrete architecture and the problems are discussed. © 2018 Association for Computing machinery.

关键词： machine learning

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Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays 1

Small-Scale CNN-N model for Covid-19 Anomaly Detection and L...

引用

1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Marusani, Jagadeesh Sudha, B.G. Darapaneni, Narayana Pes University Data Science and Machine Learning Bengaluru India Data Science and Machine Learning Great Learning Bengaluru India Artificial Intelligence and Machine Learning Great Learning Bengaluru India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection. © 2022 IEEE.

关键词： Object detection

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Local information based overlaid text detection by classifier fusion

Local information based overlaid text detection by classifie...

引用

31st IEEE international conference on Acoustics, Speech and Signal processing

作者： Ekin, Ahmet Philips Res Labs Video Proc Grp NL-5600 JA Eindhoven Netherlands

ISBN: (纸本)9781424404681

When implemented in hardware, image-processing algorithms should be robust to memory limitations because some hardware architectures may not have memory size as large as the whole frame size. Although this is not generally a problem for low-level processing, higher-level understanding, such as object detection, demands novel solutions because the available information may, in some cases, be very local, e.g., only a partial view of the object could fit in the available memory size. In this paper, we propose a novel hardware-oriented overlaid text detection algorithm that can detect text with height as large as five times the memory size. The algorithm integrates a connected component (CC)-based algorithm with a texture-based machine learning approach. The CC-based algorithm uses character-level features in the horizontal direction whereas the texture-based algorithm extracts block-based features to integrate information from all directions. Furthermore, the texture-based algorithm employs a support vector machine (SVM) to benefit from the strength of machine learning tools. In order to detect text of large font size, we also propose a novel hardware-oriented, height-preserving multi-resolution analysis. Finally, the results of the two classifiers as well as color and edge cues are used for the final pixel-based text/non-text decision.

关键词： pattern recognition

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Fusion of multispectral and panchromatic satellite images based on ihs and curvelet transformations

Fusion of multispectral and panchromatic satellite images ba...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Wang, Man Zhang, Jie-Lin Cao, Dai-Yong China Univ Min & Technol Sch Resource & Safety Engn Beijing 100083 Peoples R China Beijing Res Inst Uranium Geol Natl Lab Remote Sensing Beijing 100029 Peoples R China

ISBN: (纸本)9781424410651

image fusion is an important tool for remote sensing data processing technology, as many Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. This paper presents a new image fusion method that combines IHS transform and curvelet transform. Experiments carried out on a Enhanced Thematic Mapper Plus image show that the proposed method quantitatively outperforms state-of-the art image fusion methods, in terms of geometric, radiometric, and spectral fidelity.

关键词： curvelet transform IHS transform image fusion landsat enhanced thematic mapper plus(ETM plus ) image

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High Speed Multiplier using Embedded Approximate 4-2 Compressor for image Multiplication 1

High Speed Multiplier using Embedded Approximate 4-2 Compres...

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1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Teja, Thogiti Sai Aditya Sai Teja, G. Ravindra, Jvr Maddisetti, Lavanya Vardhaman College of Engineering Department of Ece India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Multiplication is the most widely used operation in a variety of applications such as digital filters and neural networks. In certain applications (image processing), precise computation is not required and, even low-accuracy computing can yield meaningful results. Since approximate computing is less complex and consumes less resources, it can be used in image processing and multimedia applications. Regular adders have a longer critical delay than compressor adders, making them suitable for use in multiplier architectures. Compressors can be employed in multiplication operations to reduce number of partial products, delay and power dissipation. We presented a 8×8 Dadda multiplier with an approximated 4:2 compressor in this research. On a pixel-by-pixel basis, this multiplier is used to multiply two images. © 2022 IEEE.

关键词： Adders

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A New Facial image Deviation Estimation and image Selection Algorithm (Fide-Isa) for Facial image recognition Systems: The Mathematical Models 1

A New Facial Image Deviation Estimation and Image Selection ...

引用

1st international conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021

作者： Siyaka, Hassan Opotu Owolabi, Olumide Bisallah, I. Hashim University of Abuja Department of Computer Science Nigeria

ISBN: (纸本)9781665434935

Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial image Deviation Estimation and image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above. © 2021 IEEE.

关键词： Estimation

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Digital Right Management Control for Joint Ownership of Digital images using Biometric Features

Digital Right Management Control for Joint Ownership of Digi...

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1st international conference on Signal processing and Integrated Networks (SPIN)

作者： Singh, Anushikha Dutta, Malay Kishore Travieso, Carlos M. Soni, K. M. Amity Univ Dept Elect & Commun Engn Noida India

ISBN: (纸本)9781479928668

This paper proposes a method to establish joint ownership of digital images by embedding imperceptible digital pattern in the image. This digital pattern is generated from biometric features of more than one subject in a strategic matter so that the identification of individual subject can be done and the multiple ownership of the digital images can be established. This digital pattern was embedded and extracted from the image and the experiments were also carried out when the image was subjected to signal processing attacks. Coefficients of mid frequency band discrete cosine transform was used for embedding as these coefficients do not adversely affect the perceptual transparency and is also significantly robust to normal signal processing attacks. Experimental results indicate that the insertion of this digital pattern does not change the perceptual properties of the image and the pattern survives signal processing attacks which can be extracted for unique identification.

关键词： Multiple biometric features Iris pattern recognition Robustness Perceptual Transparency Signal processing Attacks Digital watermarking

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recognition Of Off-Line Chinese Characters Via structural Method

Recognition Of Off-Line Chinese Characters Via Structural Me...

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作者： Liu, Ping Lin, Zhen Yong Sch. of Elec./Electronic Engineering Nanyang Technological University Singapore 639798 Singapore

In this paper, a structural representation and fuzzy matching scheme is proposed for off-line multi-font Chinese character recognition. Firstly, a Chinese character is decomposed into eight stroke types. Secondly, a complete structural attribute feature codes among different type of strokes are defined and extracted. Lastly, a fuzzy matching scheme and dynamic programming algorithm is used for detailed match between an input character and candidate characters. Experiment on about 5140 daily used characters shows that our method can achieve 96.23% recognition accuracy.

关键词： pattern recognition

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