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共 1490 条记录，以下是171-180 订阅

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Detection of Fraudulent Alteration of Bank Cheques Using image Processing Techniques 3rd

Detection of Fraudulent Alteration of Bank Cheques Using Ima...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

作者： Raghavendra, S.P. Ahamed, Shoieb Danti, Ajit Rohit, D. Department of Computer Application JNN College of Engineering Shimoga Karnataka India Shimoga Karnataka India Bangalore Karnataka India NMIT Bangalore Karnataka India

ISBN: (纸本)9789811605062

In today‘s world illegal alteration and illegal modifications of authenticated financial documents is increasing rapidly as a fastest growing crimes around the world. The result of this kind of crimes may result in a huge financial loss. In this paper image processing and document image analysis techniques are used to examine such cases in order to identify the fraudulent bank cheques. However, it is very difficult to detect an alteration made on documents once the printing ink of alike color is employed. In this paper, alterations and modifications caused with handwritten ball point pen strokes are considered and proposed a technique for recognition of such types of corrections by employing standard techniques under Digital image processing and pattern recognition. The results are quite promising during the experiments conducted. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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Logic-Based Neural Network for image Compression Applications 3

Logic-Based Neural Network for Image Compression Application...

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3rd IEEE Middle East and North Africa Communications conference (MENACOMM)

作者： Doughan, Ziad Kassem, Rola El-Hajj, Ahmad M. Haidar, Ali M. Beirut Arab Univ Dept Elect & Comp Engn Debbieh Lebanon

ISBN: (纸本)9781665434430

This paper presents a new lossy image compression technique using Logic-based Weightless Neural Networks, which underwrite two novel network architectures. The system endorses three processing phases, image optimization, inflation, and skimming. This research demonstrates an untraditional approach of auto-compression network guided by horizontal and vertical pixel intensity wavering trend. The performance of this new approach aligns with human's perception of singularities in a certain pattern. The potential of trend analysis in image compression incorporates with information storage techniques and knowledge accumulation. The weightless network models generate images underlying enough distinct features that preserve the originality of a particular pattern but give superior levels of compression.

关键词： Logic-Based Intelligence Artificial Neural Networks Weightless Neural Networks image Processing Compression

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Automated Orientation Detection of 3D Head Reconstructions from sMRI Using Multiview Orthographic Projections: An image Classification-Based Approach 11th

Automated Orientation Detection of 3D Head Reconstructions ...

引用

11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Heredia-Lidón, Álvaro González, Alejandro Guerrero-Mosquera, Carlos Gonzàlez-Colom, Rubèn Echeverry, Luis M. Hostalet, Noemí Salvador, Raymond Pomarol-Clotet, Edith Fortea, Juan Martínez-Abadías, Neus Fatjó-Vilas, Mar Sevillano, Xavier HER - Human-Environment Research Group La Salle - Universitat Ramon Llull Barcelona Spain Barcelona Spain FIDMAG Sisters Hospitallers Research Foundation Barcelona Spain Madrid Spain Sant Pau Memory Unit Hospital de Sant Pau i la Santa Creu Barcelona Spain

ISBN: (纸本)9783031366154

Recent studies in neuropsychiatry have highlighted the correlation between facial and brain dysmorphologies. One way of simultaneously analysing the brain and the face of a subject is by reconstructing a whole-head 3D model from structural magnetic resonance imaging (sMRI). However, the use of different reconstruction protocols generates undesired orthogonal rotations of the 3D models. This is a likely situation in multicentric studies that hampers the combination of data from different centers. Although the original sMRI files contain the subject orientation, it is not always possible to access this data. To solve this issue, in this work we propose a novel method to estimate the orientation of 3D heads with rotations of 90 ∘ or multiples thereof around any of the three Cartesian axes as a required step for generating a normalised dataset in terms of orientation. Our proposal creates 2D images from orthogonal projections of the 3D object, transforming orientation estimation into an image classification problem. Experimental results show that our method, using three orthographic views of the 3D head to create the projection image and ResNet50 for classification, achieves an accuracy of 99.7%, which corresponds to 0.15 mean absolute error in rotation, outperforming state-of-the-art point cloud registration methods like DeepBBS and PRNet. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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Computer Vision and image Processing the Challenges and Opportunities for new technologies approach: A paper review 3

Computer Vision and Image Processing the Challenges and Oppo...

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3rd International Scientific conference of Engineering Sciences and Advances Technologies, IICESAT 2021

作者： Hussien, Reem M. Al-Jubouri, Karrar Q. Gburi, Mohaimen Al Hussein Qahtan, Al Gburi Duaa Jaafar, Al Hamami Department of Civil Engineering Al-Farabi University College Baghdad Iraq Department of Production Engineering and Metallurgy University of Technology Iraq Dep of Electrical and Computer Engineering Altinbaş University Istanbul Turkey Administrative Affairs ID Dep Ministry of Education Baghdad Iraq

Digital image processing has numerous applications in many sectors of the world. It expands from initial information registration into methods and thoughts combining pattern recognition, computer vision, and machine learning. The wide utilization has pulled in many researchers to integrate with a range of related specializations. This work gives a study of the latest development and theoretical ideas clarifying an improvement of computer vision particularly with pattern recognition and image processing, utilizing various regions of their field implementation. Digital image processing causes researchers to analyze images to get important data and comprehend information. It utilized a technique of multi-range implementation and huge information analysis. This work aims to focus on the latest studies related to image processing, pattern recognition, and computer vision. In this paper, Computer vision standard has been categorized into groups. For example, pattern recognition, image processing, and AI. Additionally, we give a short clarification of the forward-thinking data about the methods and their realization. This survey is restricted to computer vision, and more research can include detecting the conduct and properties of the object including people actions. © Published under licence by IOP Publishing Ltd.

关键词： Computer vision

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3rd International Symposium on Material and Electrical Engineering conference, ISMEE 2021

3rd International Symposium on Material and Electrical Engin...

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3rd International Symposium on Material and Electrical Engineering conference, ISMEE 2021

ISBN: (纸本)9781665423625

The proceedings contain 65 papers. The topics discussed include: reinforcement learning based energy storage units scheduling considering point of common coupling constraint;content-based traditional clothes pattern retrieval using convolutional neural network;improved pattern recognition of various traditional clothes with convolutional neural network;redistribution of wealth: an agent-based simulation;a review of equipment and signal processing of the automated auscultation for blood pressure measurement;a case study on improvement of power factor of an industrial load;interaction design of mathematics learning applications for elementary school students;supervisory system based on image processing using library open computer vision in tool management system;design and simulation of wind energy conversion system to electricity on the coast of Kebumen;a microfluidic impedance flow detection platform based on rapid polyjet 3D printing fabrication toward biomedical analysis applications;and design of a novel microchannel-based device for dielectrophoresis separation of cancer cells from red blood cells.

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Proposed CNN Model for the Classification of Knee Osteoarthritis

Proposed CNN Model for the Classification of Knee Osteoarthr...

引用

Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Rahul Singh Neha Sharma Deepak Upadhyay Sarishma Dangi Akira Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Knee osteoarthritis (OA) is a common chronic joint disorder affecting the health and quality of life for millions worldwide, particularly the elderly. This condition is distinguished by the progressive deterioration of cartilage in the knee joint, resulting in pain, stiffness, and limitations in physical function. Traditionally, the diagnosis of knee OA is based on clinical evaluations, patient-reported symptoms, and radiographic imaging. X-ray imaging is the most common modality due to its accessibility and cost-effectiveness. However, manual interpretation of X-ray images is prone to inter-observer variability and necessitates specialised knowledge, which can result in inconsistencies and delays in diagnosis. Deep learning, particularly Convolutional Neural Networks (CNNs), has demonstrated tremendous potential in automating medical image analysis, resulting in a more objective and efficient diagnostic approach. The study proposes a CNN-based model for classifying knee osteoarthritis using X-ray images. The dataset used in this study includes 6000 images divided into three categories: healthy, moderate, and severe. The proposed model achieves an overall accuracy of 96%, improving diagnostic accuracy and consistency by utilising CNNs to recognise complex patterns and features in images. This study’s findings have the potential to revolutionise the clinical management of knee OA by providing a robust, non-invasive diagnostic tool that improves the precision and efficiency of OA detection, thereby improving patient outcomes and lowering healthcare costs.

关键词： Deep learning Analytical models Accuracy image recognition image analysis pattern recognition Convolutional neural networks Osteoarthritis X-ray imaging Pragmatics

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image Processing-Based Switching Matrix for PV Array Under Partial Shading Conditions 1

引用

3rd International conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Manimegalai, D. Karthikeyan, M. Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai India

ISBN: (数字)9789811688621

ISBN: (纸本)9789811688614

The photovoltaic (PV) system will have the impact of decreasing its efficiency because of partial shadowing, and it can be overcome by using reconfiguration method. In this paper, an analysis of shadow moving and the Sudoku pattern-based reconfiguration method are proposed. The proposed approach gives a new configuration with radiation values incorporated in to PV system. This method had two sections: flexible and static, which are arranged with the help of switching matrices. The optimization of PV arrays is done by using image processing, to acquire the information of shadows. Also, in this paper, both the Total-Cross-Tied (TCT) and Sudoku pattern connections are used in the test. The camera is used to monitor the shadows on the PV system, and the reconfiguration is applied according to a predefined time threshold. The tests are performed in real-time, and the proposed system gives the increase in average energy extraction for TCT and Sudoku pattern configuration. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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Local Roughness Binary pattern for Texture Classification 3rd

Local Roughness Binary Pattern for Texture Classification

引用

3rd International conference on Data, Engineering, and Applications, IDEA 2021

作者： Gupta, Sumit Kumar Yadav, Susheel Singh, Dhirendra Pratap Choudhary, Jaytrilok Department of Computer Science and Engineering MANIT MP Bhopal India

ISBN: (纸本)9789811946868

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Application analysis of Face recognition Technology Based on Computer Vision 3

Application Analysis of Face Recognition Technology Based on...

引用

3rd International Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Liu, Chenran Liu, Junting Anhui University School of Electrical Engineering and Automation Hefei China Tongling People's Hospital Tongling China

ISBN: (纸本)9781665402675

With the development of society, all parties are facing increasingly urgent requirements for fast and effective automatic identity verification. Since biological characteristics are intrinsic properties of humans, they have strong stability and individual differences, so they are an ideal basis for identity verification. Compared with biometrics such as fingerprints and iris, face recognition is more direct, friendly, and convenient to be accepted by users. Therefore, face recognition has become a current research hotspot in the field of pattern recognition and artificial intelligence. As an important part of human-computer interaction, the research of face detection and recognition technology has great theoretical significance and application value. In this paper, the face detection method based on skin color information and the face recognition algorithm based on the embedded hidden Markov model are deeply studied, and the development of the development of the face detection and recognition system, performance analysis and specific applications are done. Introduced. In this paper, the detection and positioning of the face and the extraction of facial features are carried out on the frontal color image: the feature vector extraction of the detected face region using the two-dimensional discrete cosine transform and the method based on the embedded hidden Markov model are proposed. Face recognition method. Experiments show that the method of using the embedded hidden Markov model based on two-dimensional discrete cosine transform for face recognition can make better use of the two-dimensional statistical characteristics of face images, and has high recognition efficiency and very high recognition efficiency. Good robustness;researched on the recognition of hand-drawn portraits of human faces, and proposed a novel algorithm for converting photo images into portraits;applied the face detection and recognition system to specific practices, and established An intelligen

关键词： Face recognition

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Infrared and visible image fusion method based on LatLRR and ICA 21

Infrared and visible image fusion method based on LatLRR and...

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3rd International conference on pattern recognition and Intelligent Systems, PRIS 2021

作者： Huang, Ying Zhang, Zongyu Wen, Xilin Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9781450390392

To solve the problem of missing lots of texture details in the fusion image, we propose a new fusion method of infrared and visible images based on latent low-rank representation(LatLRR) and independent component analysis(ICA) in this paper. Firstly, the source image is decomposed into low-rank components, sparse components, and noise components by LatLRR. Secondly, ICA is utilized for the low-rank part of infrared image and visible image to obtain the main difference between two source images. Then, the image containing more information is determined by comparing the entropy of two source images and it is employed as a benchmark. Finally, the fused image is accomplished by connecting the benchmark result, the low-rank components, and the sparse components of another image according to the result obtained by ICA. Compared with other fusion methods, experimental results demonstrate that the proposed method has better visual effects and evaluation indicators. © 2021 ACM.

关键词： image fusion

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