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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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Camera model identification using local binary patterns

Camera model identification using local binary patterns

引用

2012 13th IEEE international conference on Multimedia and Expo, ICME 2012

作者： Xu, Guanshuo Shi, Yun Qing New Jersey Institute of Technology Newark NJ United States

ISBN: (纸本)9781467316590

In digital image forensics, camera model identification seeks for the source camera model information from the given images under investigation. To achieve this goal, one of the popular approaches is extracting from the images under investigation certain statistical features that capture the difference caused by camera structure and various in-camera image processing algorithms, followed by machine learning and pattern recognition algorithms for similarity measures of extracted features. In this paper, we propose to use uniform gray-scale invariant local binary patterns (LBP) as statistical features. Considering 8-neighbor binary co-occurrence, three groups of 59 local binary patterns are extracted from the spatial domain of red and green color channels, their corresponding prediction-error arrays, and their 1st-level diagonal wavelet sub bands of each image, respectively. Multi-class support vector machines are built for classification of 18 camera models from âDresden image Database'. Compared with the results reported in literatures, the detection accuracy reported in this paper is higher. © 2012 IEEE.

关键词： Local binary pattern

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Iris Grid image Classification using Naive Bayes for Human Biometric System 1

Iris Grid Image Classification using Naive Bayes for Human B...

引用

1st international conference on Information System and Information Technology, ICISIT 2022

作者： Wibawa, Adhi Dharma Pamungkas, Yuri Perdana, Muhammad Ilham Rachmatika, Ratih Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Biometrics is a measurement of a person's physical and behavioral characteristics. Iris image is one of many biometrics data such as fingerprint, voice, face, and gait that can be used as an identifier. Iris is the colored part of the eye that helps the pupil see clearly and regulates light entry. Iris recognition is one of the important topics in biometric systems because of its unique pattern. Several related studies have been carried out to automatically obtain the most efficient method to understand and recognize the iris for human verification. This study proposes an analysis of iris images for biometrics systems with effective image processing techniques for system recognition. CVBL Iris image dataset was used in this study with 4320 iris images. After converting the iris image into a rectangle form, the Grid iris image experiment was implemented to find the highest accuracy. Several iris image grid-size were simulated to find the best accuracy. Multinomial Naive Bayes is used as a classifier. The Naive Bayes method is a machine learning method that uses probability calculations (rules-based). This algorithm uses probability and statistical methods, which predict future probabilities based on the previous data. The study results indicate that the proposed method can recognize the iris by identifying its fibers and encoding the fibers data using a grid image approach, with a classification accuracy of 92.37%, using an iris grid size of 70x50 pixels. This research can be useful for developing human biometric systems based on iris with a simple preprocessing approach. © 2022 IEEE.

关键词： Biometrics

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An Effective Automatic recognition Approach of Chinese Herbal Medicine by Micro image processing 21

An Effective Automatic Recognition Approach of Chinese Herba...

引用

21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Pang, Guangyao Yu, Zhenming Pan, Honglin Xie, Chengwen Gong, Ping Zhu, Xiaoying Peng, Zizhen Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Guangxi Coll & Univ Key Lab Ind Software Technol Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： image recognition identification of traditional Chinese medicine transfer learning data enhancement microscopic image

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Classification by learning of wavelet and texture features 1st

Classification by learning of wavelet and texture features

引用

1st international conference on Frontiers in Smart Systems Technologies, ICFSst 2019

作者： Bagavathi, C. Saraniya, O. Department of Electronics and Communication Engineering Government College of Technology Coimbatore641013 India

ISBN: (纸本)9789811550287

image classification is one among the significant tasks that can benefit industry through advancement in science and technology. Texture images possess a specific pattern that can be utilized to uniquely identify a texture class. Classification of Texture images can be done through proper selection of parameter that uniquely identifies an image in a group containing images of different classes. machine learning approaches are currently the choice of implementation to provide accuracy and robustness in various fields like automatic image recognition, registration, and analysis. This work addresses machine learning methods to classify learnt texture samples through feature fusion of parameters obtained through wavelet and texture analysis. © Springer Nature Singapore Pte Ltd 2021.

关键词： Support vector machines

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A review for soil nutrient identification using image processing 1

A review for soil nutrient identification using image proces...

引用

1st IEEE international conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sisodia, Ms. Shivani Dhyani, Saurabh Uttaranchal University Uttaranchal School of Computing Sciences Uttarakhand Dehradun India

ISBN: (纸本)9798350356816

In agriculture, environmental science, and land resource management, soil analysis is crucial. The ability to extract detailed data from soil images has recently become possible thanks to the integration of image processing methods with soil analysis. We emphasize on the precision and effectiveness of soil analysis using machine learning. To tackle the issues of sustainable agriculture, soil conservation, and land-use planning, the combination of soil science and image processing provides promising solutions. This work offers a comprehensive examination of the integration of image processing into soil analysis. We look at the entire soil analysis process, and we have also subcategorized the work depending on how images of soil were collected for processing, i.e., remotely, using satellites, or using a digital camera. This study is a useful tool for academicians, engineers, and decision-makers who want to use image processing to its full potential for soil nutrient identification. © 2024 IEEE.

关键词： image processing machine learning nutrients soil

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Estimation of Face Pose from an image Using Eigen Space Method

Estimation of Face Pose from an Image Using Eigen Space Meth...

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： Watanabe, Akihiro Saito, Hideo Ozawa, Shinji Department of Electrical Engineering Keio University 3-14-1 Hiyoshi Kouhoku-ku Yokohama 223 Japan

In this paper, we propose a new model-based method for estimating face pose from a facial image. In this method, computer generated models are used for reducing the cost of collecting the images of various models. By the use of computer generated models, it is very easy to change the acquisition conditions of the model images, such as illumination and pose of the object. Additionally, the eigen space technique is employed for reducing the huge capacity of the prepared models and huge computation for matching input images with model images. We show the results of pose estimation from facial images of varying illumination for demonstrating the effectiveness of the proposed method.

关键词： pattern recognition

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image-based Fall Detection with Human Posture Sequence Modeling

Image-based Fall Detection with Human Posture Sequence Model...

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1st IEEE international conference on Healthcare Informatics (ICHI)

作者： Dai, Xiaoxiao Wu, Meng Davidson, Bradley Mahoor, Mohammad Zhang, Jun Univ Denver Dept Elect & Comp Engn Denver CO 80210 USA Univ Denver Dept Mech & Mat Engn Denver CO 80210 USA

ISBN: (纸本)9780769550893

In this paper, an image-based method is presented for fall detection using statistical human posture sequence modeling. Specifically, a series of laboratory simulated falls and activities of daily living (ADLs) are performed and recorded by a Kinect sensor as training video data. The skeleton view of a human body in these video recordings is extracted using the Kinect for Windows SDK. Hidden Markov Models are used for modeling the fall posture sequences and distinguishing different fall activities and ADLs. Our experimental results demonstrate an average fall recognition rate above 80% and the capability of early warning for falls.

关键词： Fall detection image processing machine learning kinect hidden Markov model

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learning Representations through Ensemble of Fuzzy C-Means for Identification of Retinal Pathologies 17

Learning Representations through Ensemble of Fuzzy C-Means f...

引用

international conference on Internet of Things and machine learning (IML)

作者： Karri, S. P. K. Chakraborty, Debjani Ray, Ajoy K. Chatterjee, Jyotirmoy IIT Kharagpur Kharagpur W Bengal India IIEST Sibpur Howrah India

ISBN: (纸本)9781450352437

Retinal image analysis is employed to automate screening process through low-level feature extraction and classification. Supervised classification approaches are dependent on kernels or distance metrics to handle complex manifolds as they warp feature space for effective classification with less complex boundaries between classes. Proposed approach identifies control points (Voronoi diagram) by exploring the structures of class specific manifolds which constructs complex boundaries with piecewise linear nature. Such a framework has less number of hyperparameters to tweak resulting easy control and understanding of the system. The learning characteristics of the proposed algorithm has been depicted on toy and optical coherence tomography data set. It has illustrated effective performance in identification of retinal pathologies and compared against off-the-shelf classifiers with various parameters. Proposed algorithm is capable of accommodating unsupervised approaches other than Fuzzy C-Means reflecting its adaptability.

关键词： pattern recognition Fuzzy Logic Fractals Voronoi

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The application of BEMD to multi-spectral image fusion

The application of BEMD to multi-spectral image fusion

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Xu, Xiangnan Li, Hua Wang, Anna Air Force Aviation University of PLA Collage of Information Science and Engineering Northeastern University

ISBN: (纸本)9781424410651

Multi-spectral linage Fusion is a new research field in image processing. In this paper, for achieving a better fusion result, a image fusion method based on the intensity;-hue-saturation(IHS) Transform and Bidimensional Empirical Mode Decomposition(BEMD) is proposed. Firstly, the multi-spectral image is transformed into IHS component. Secondly, the histogram-matched panchromatic image and intensity component are decomposed into a set of BIMFs respectively by means of BEMD. Thirdly, the new intensity component can be obtained by merging the BIMFs of histogrant-matched panchromatic image and intensity component. Finally, the new intensity, hue, and saturation components are transformed back to RGB. Experimental results show that this new method can preserve the spectral information it? the fusion image;meanwhile, it can merge the spatial details of the panchromatic image.

关键词： image fusion BEMD IHS

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Malware image Classification Using Deep learning Approach 1

Malware Image Classification Using Deep Learning Approach

引用

1st international conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Yaddala, Sai Harshith Mounika, P. Jothikumar, C. Ashokkumar, Ashokkumar SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350376517

The project aims to address the escalating challenge of malware, a critical threat in the cybersecurity domain. Traditional detection methods are struggling to keep pace with sophisticated, evolving malware attacks. Deep learning offers an innovative approach, leveraging its prowess in pattern recognition to detect and classify malware more effectively. This research involves applying various deep learning algorithms to a substantial dataset of known malware samples, aiming to achieve higher accuracy and faster detection rates compared to traditional methods. The anticipated outcomes include a robust, scalable model capable of adapting to new malware threats, significantly enhancing cybersecurity defenses. This abstract explores the transformative impact of deep learning on malware classification, a critical aspect of cybersecurity. We delve into prevalent deep learning approaches, including the utilization of CNNs for image-based classification and the synergy between deep learning and traditional feature engineering techniques. Additionally, we discuss the potential of hybrid models and the emerging field of dynamic malware analysis utilizing deep learning, highlighting its potential for real-time threat detection. Finally, we emphasize the continuous evolution of the field and the potential for further advancements through the integration of deep learning with other cybersecurity measures. © 2024 IEEE.

关键词： Adversarial machine learning

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