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A relational distance-based framework for hierarchical image understanding

A relational distance-based framework for hierarchical image...

引用

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

作者： Antanas, Laura Van Otterlo, Martijn Jośe Oramas, M. Tuytelaars, Tinne De Raedt, Luc Katholieke Universiteit Leuven Leuven Belgium

ISBN: (纸本)9789898425980

Understanding images in terms of hierarchical and logical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robot vision. This paper combines compositional hierarchies, qualitative spatial relations, relational instance-based learning and robust feature extraction in one framework. For each layer in the hierarchy, substructures in the images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures, by making use of qualitative spatial relations. The approach is applied to street view images. We employ a four-layer hierarchy in which subsequently corners, windows and doors, and individual houses are detected.

关键词： Edge detection

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Heritage image classification by convolution neural networks 1

Heritage image classification by convolution neural networks

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1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018

作者： Vu, Manh-Tu Beurton-Aimar, Marie Le, Van-Linh LaBRI-CNRS 5800 France PUF - Pôle Universitaire Francophone Ho Chi Minh City Viet Nam LaBRI-CNRS 5800 Universite Bordeaux Talence France LaBRI-CNRS 5800 France ITDLU - Dalat University France

ISBN: (纸本)9781538641804

In this project, we have explored the use of Convolution Neural Networks for semantic classification of heritage images. Recently four architectures, AlexNet, GoogLeNet, ResNet, and SENet have been proposed. We have tested them with two different learning modalities. Besides conventional training from scratch, we have resorted to pre-trained networks that have been fine-tuned on a huge amount of data in order to avoid overfitting problems and to reduce design/computing time. Experiments on a dataset we have built by ourselves from Heritage repository show that with the help of this step of fine-tuning, one of the simplest (and oldest) networks AlexNet is able to produce similar results than more sophisticated ones as ResNet which is pretty 3 times more consuming in computing time, mainly due to the number of the layer it uses. With this model, we have been able to show that our model can classify in the right way more than 98% of the images belonging to the dataset test. From now, this model can be used to classify the full dataset of the Heritage repository. © 2018 IEEE.

关键词： image classification

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Implementation of Enhanced Iris recognition for Secure Biometric Authentication using machine learning 1

Implementation of Enhanced Iris Recognition for Secure Biome...

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1st international conference on Trends in Engineering Systems and Technologies, ICTEst 2024

作者： Duraisamy, Premkumar Rupasri, K.S. Sri K, Rekha Thanishka, T.S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350307795

Developing effective strategies to address significant variations in segmented iris image quality is crucial for accurate iris recognition from remotely acquired facial or eye images. These variations are closely linked to the consistency of iris feature encoding, suggesting that leveraging such discrepancies can improve matching accuracy. A nonlinear approach is recommended for considering both the local consistency of the iris pattern and the overall quality of the weight map. Principal Component Analysis (PCA) and Support Vector machines (SVM) are commonly employed in biometric research for iris recognition, identifying individuals through unique iris features. This research aims to enhance the precision and efficiency of iris recognition systems through methods such as feature extraction, classifier algorithms, integration of multiple biometric modalities, and the development of comprehensive and diverse iris datasets. Iris recognition technology offers significant potential to bolster security and privacy in applications including access control, border security, and financial transactions. © 2024 IEEE.

关键词： Principal component analysis

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Analysis of images Obtained by Unmanned Aerial Vehicle by Deep learning Methods 1

Analysis of Images Obtained by Unmanned Aerial Vehicle by De...

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1st international Informatics and Software Engineering conference, IISEC 2019

作者： Kutlu, Ozgur Demir, Onder Dogan, Baris Marmara Üniversitesi Fen Bilimleri Enstitüsü Istanbul Turkey Marmara Üniversitesi Teknoloji Fakültesi Bilgisayar Mühendisliǧi Istanbul Turkey Marmara Üniversitesi Teknoloji Fakültesi Mekatronik Mühendisliǧi Istanbul Turkey

ISBN: (纸本)9781728139920

In artificial intelligence applications, many sub-methods such as machine learning, artificial neural networks, classification, clustering algorithms are used. One of these methods is deep learning. Deep learning is an advanced machine learning class. Using the Deep learning method, video analysis, image classification, speech recognition and natural language processing are very successful. The data and experiences to be provided by the projects covering Deep learning and Unmanned Aerial Vehicles will increase the number of qualified studies on these issues and contribute to the development of high value-added products in these technologies. In this study, a control software that evaluates image data from unmanned aerial vehicles and makes various inferences (classification, positioning, marking) is created. By using the method of retraining the last layers of the pre-trained artificial neural network models with our data set, it has been tried to reduce the training time and increase the success. In these studies, 2 pre-educated models were used and as a result of training of these models, as a result of 190 thousand steps of training, 25.39 and 27.87 mAP values were reached. © 2019 IEEE.

关键词： Antennas

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A comparative study of three shape normalization algorithms

A comparative study of three shape normalization algorithms

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5th international conference on Wavelet Analysis and pattern recognition

作者： Zhang, Wen-Liang Zhang, Tao Song, Jing-Yan Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781424410651

Shape is one of the fundamental visual features in pattern recognition and target tracking. And shape normalization is a very important pre-processing step in image Understanding. In general, there are four basic forms of planar shape distortions caused by changes in viewer's location: translation. rotation, scaling and skewing. A good shape descriptor should be invariant to these distortions. In this paper, we study and compare three shape normalization algorithms: J.G. Len's shape compacting algorithm and its two modified versions: Wang's image ellipse algorithm and Liang g J.J. 's principal avis algorithm. Experiments on a set of images show that all of these algorithms have some drawbacks and we give some advices for modification.

关键词： shape normalization compact image principal axis shape compacting

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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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A Privacy-preserving Crowd Movement Analysis by k-member Clustering of Face images 4

A Privacy-preserving Crowd Movement Analysis by <i>k</i>-mem...

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4th international conference on Informatics, Electronics & Vision (lCIEV)

作者： Honda, Katsuhiro Omori, Masahiro Ubukata, Seiki Notsu, Akira Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

ISBN: (纸本)9781467369022

Crowd movement analysis is an important issue in social design. This paper studies an machine learning approach to crowd movement estimation through face image recognition. Although high performance face recognition is a powerful tool in individual authentication with surveillance camera images in public spaces, utilization of personal information is often hesitated under fear of privacy violation. In this paper, a privacy preserving framework for crowd movement analysis is proposed considering k-anonymization of face image features. k-anonymity is a quantitative measure of secureness in data mining and is expected to enhance the utility of personal information. An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.

关键词： cameras data mining data privacy face recognition learning (artificial intelligence) parallel processing high performance face image recognition k-anonymization k-member clustering machine learning approach privacy violation privacy-preserving crowd movement analysis surveillance camera image Cameras Data privacy Face Face recognition image recognition Logic gates pattern matching Data privacy Facial recognition Cameras data mining image recognition Face Face pattern matching Logic gates PARALLEL processing (COMPUTERS) Movements

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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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Research and Prospect of image recognition Based on Convolutional Neural Network 1

Research and Prospect of Image Recognition Based on Convolut...

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1st international conference on Computer Applied Science and Information Technology, ICCASIT 2020

作者： Hu, Hanqing Lyu, Jin Yin, Xiaolin Beijing Information Science and Technology University Haidian100192 China Beijing Guozhixue Culture Co. Ltd Chaoyang100101 China BeiJing GuoXue Times Culture Co. Ltd Fengtai100070 China

This paper compares common image recognition technology, introduces three image recognition and classification technologies, and explains the most popular deep learning image recognition algorithm based on CNN in detail. As a new method of ANN, CNN combines ANN and deep learning technology. In the area of image recognition, the advantage of CNN is particularly prominent. Compared with the traditional image processing algorithm, CNN has higher recognition accuracy and speed, and can directly process the original image to avoid the complicated pre-processing image data. CNN is the first choice for image recognition. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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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...

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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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