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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Deep Learning Utilization in Beamforming Enhancement for Medical Ultrasound

Deep Learning Utilization in Beamforming Enhancement for Med...

引用

IEEE Annual International Computer Software and applications conference (COMPSAC)

作者： Mariam Fouad Yousef Metwally Georg Schmitz Michael Huebner Mohamed A. Abd El Ghany Ruhr University Bochym (RUB) Germany and German University in Cairo (GUC) Egypt German University in Cairo (GUC) Egypt Ruhr University Bochum (RUB) Germany BTU Cottbus - Senftenberg Germany German University in Cairo (GUC) Egypt and TU Darmstadt (TUD) Germany

ISBN: (数字)9781728173030

ISBN: (纸本)9781728173047

Ultrasound imaging offers a low cost, noninvasive and portable system, which allowed it to be an invaluable tool for medical imaging. However, the quality of the reconstructed images depends significantly on the beamforming technique utilized. Although advanced data-adaptive methods of reconstruction such as Minimum Variance (MV) beamforming can recover image quality much higher than conventional techniques, their implementation also entails a heavy computational burden. This dichotomy hinders the ultrasound imaging use as a standalone device in some applications such as early breast cancer detection. Deep neural networks (DNNs) have shown a huge potential when applied to many artificial intelligence (AI) research fields. In this work, the use of Deep learning in improving the quality of the beamforming technique Delay and Sum (DAS) normally used for ultrasound (US) images reconstruction is explored. Three different architectures are implemented: Convolutional AutoEncoder (CAE), Fully Connected network (FC) and U-Net-like architecture. They were trained on datasets simulated using field ii. The dataset consists of input-output pairs where the input is Noisy DAS beamformed scan lines and the output is MV beamformed non-noisy scan lines. The networks show a great ability in predicting the beamformed signals along with significantly reducing noise in the reconstructed images. Additionally, the proposed networks improve other image characteristics such as scatterer size and position along with reducing tail characteristic normally found in DAS beamformed ultrasound images. US images constructed by the networks achieved better quality metrics that surpass conventional DAS beamformed images. The CAE, U-net-like architecture, and FC enhanced the signal to noise ratio (SNR) compared to DAS by 218%, 165% and 136% respectively. Additionally, the networks showed higher Contrast to Noise Ratio (CNR) and Contrast Ratio (CR) metrics than DAS beamformed signals. Finally, the propo

关键词： Array signal processing Ultrasonic imaging image reconstruction Delays Imaging Computer architecture Covariance matrices

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Prediction of Pathogen Causing Rice Plant Disease and Recommendation using Enhanced Machine Learning Technique

Prediction of Pathogen Causing Rice Plant Disease and Recomm...

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Applied artificial Intelligence and Computing (ICAAIC), International conference on

作者： J Suji Priya M Hema Priya M Iyswarya K Kiruthika Department of Master of Computer Applications Sona College of Technology Salem India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Plant diseases pose significant challenges to agricultural productivity, impacting both the quality and quantity of crop yields. Early detection and effective management of these diseases are essential for mitigating their detrimental effects on agricultural output. However, traditional methods of disease monitoring and diagnosis are often costly and require continuous expert intervention. To address these challenges, this study proposes a novel approach that combines image processing techniques and advanced machine learning methodologies for the accurate identification and classification of rice leaf diseases. By utilizing datasets comprising images of diseased rice leaves, environmental data, and historical disease reports, the proposed system utilizes Convolutional neural networks (CNNs) for image analysis and feature extraction. Subsequently, a Support Vector Machine (SVM) classifier is employed for disease detection and classification based on the extracted features. The integration of image processing and machine learning technologies offers a cost-effective and efficient solution for early disease detection and management in agriculture. By automating the process of disease identification, the proposed system facilitates timely interventions, thereby minimizing crop losses and promoting agricultural sustainability.

关键词： Support vector machines Productivity Plant diseases Accuracy image analysis Feature extraction Real-time systems

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A genetic algorithm-neural network approach for Mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue slide images

A genetic algorithm-neural network approach for Mycobacteriu...

引用

International conference on Intelligent Systems Design and applications (ISDA)

作者： M. K. Osman F. Ahmad Z. Saad M. Y. Mashor H. Jaafar Faculty of Electrical Engineering Universiti Teknologi MARA Permatang Pauh Penang Malaysia Electronic & Biomedical Intelligent Systems (EBItS) Research Group School of Mechatronic Engineering Universiti Malaysia Pedis Arau Perlis Malaysia Department of Pathology School of Medical Science Universiti Sains Islam Malaysia Kelantan Malaysia

This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image segmentation, feature extraction and identification. It uses Ziehl-Neelsen stained tissue slides images which are acquired using a digital camera attached to a light microscope for diagnosis. To separate the tubercle bacilli from its background, moving k-mean clustering that uses C-Y colour information is applied. Then, seven Hu's moment invariants are extracted as features to represent the bacilli. Finally, based on the input features, a GA-NN approach is used to classify into two classes: `true TB' and `possible TB'. In this study, genetic algorithm (GA) is applied to select significant input features for neural network (NN). Experimental results demonstrated that the GA-NN approach able to produce better performance with fewer input features compared to the standard NN approach.

关键词： image color analysis Microscopy artificial neural networks Feature extraction image segmentation Training Gallium

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A UAV-Based Remote Sensing image Automatic Monitoring System Empowered by artificial Intelligence

A UAV-Based Remote Sensing Image Automatic Monitoring System...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International conference on

作者： Tao Liu Bohan Zhang College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing image processing. With the maturity and wide application of remote sensing and UAV technologies, efficient and accurate image analysis methods have become particularly important. We developed this system through in-depth research on multiple stages of data preprocessing, feature extraction and model training. In the experimental stage, through the comparison experiments with Support Vector Machine (SVM) and Random Forest (RF) models, it is found that the CNN model has significant advantages in processing speed, anti-interference ability and accuracy. Especially in the processing of urban remote sensing images, CNN exhibits up to 90% accuracy, showing its wide applicability and excellent cross-domain application capability. Overall, this study not only successfully develops an efficient and accurate automatic monitoring system for remote sensing images, but also provides strong theoretical and experimental support for future optimization of CNN architecture in natural environments, improvement of real-time data processing capability, and extension to practical applications such as disaster monitoring and environmental protection.

关键词： Support vector machines Accuracy Disasters Computer architecture Real-time systems Data models Convolutional neural networks Protection Remote sensing Monitoring

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Can Pixel-Based Approach Achieve Similar Performance to Area-Based Approach in Crop Yield Forecasting Using Sentinel-2 imagery and Deep neural networks? A Probabilistic Analysis

Can Pixel-Based Approach Achieve Similar Performance to Area...

引用

Technology Innovation and Its applications (ICTiiA), International conference on

作者： Khadija Meghraoui Imane Sebari Saloua Bensiali Kenza Ait El Kadi Unit of Geospatial Technologies for a Smart Decision IAV Hassan II Rabat Morocco Department of Applied Statistics and Computer Science IAV Hassan II Rabat Morocco

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

A key solution to addressing food security challenges is the accurate forecasting of crop yields. Satellite imagery and historical remote sensing data play a crucial role in forecasting agricultural yields, particularly at the scale of individual fields. In the context of a small field, selecting between a pixel-based or area-based approach is essential, especially with deep learning (DL) models where having too little data can pose problems and acquiring more data can be difficult and challenging. In this study, we introduce two frameworks for forecasting corn yield: one using a pixel-based method and the other an area-based strategy, both utilizing Sentinel-2 imagery from a public image yield dataset and leveraging deep neural networks. The two methods were compared using key statistical metrics and also probabilistic techniques and analyses including error histogram of the strategies and their cumulative distribution function (CDF) plots considering the shapes of the lines, and the general distribution of the differences. Our findings underscored nuanced distinctions in the predictive capabilities, highlighting that the area-based model leaned towards greater accuracy in predictions, and the pixel-based approach emerged as a feasible substitute, delivering comparable performance despite slightly larger discrepancies.

关键词： Histograms Technological innovation Accuracy Shape artificial neural networks Predictive models Probabilistic logic Forecasting Crop yield Distribution functions

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neural network based handwritten character recognition system without feature extraction

Neural network based handwritten character recognition syste...

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International conference on Computer, Communication and Electrical Technology (ICCCET)

作者： J. Pradeep E. Srinivasan S. Himavathi Department of ECE Pondicherry Engineering College Pondicherry India Department of EEE Pondicherry Engineering College Pondicherry India

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30×20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.

关键词： Character recognition Handwriting recognition artificial neural networks image segmentation Pixel Accuracy Feature extraction

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Voice Activity Detection Based on Time-Delay neural networks

Voice Activity Detection Based on Time-Delay Neural Networks

引用

Asia-Pacific Signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Ye Bai Jiangyan Yi Jianhua Tao Zhengqi Wen Bin Liu School of Artificial Intelligence University of Chinese Academy of Sciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Voice activity detection (VAD) is an important preprocessing part of many speech applications. Context information is important for VAD. Time-delay neural networks (TDNNs) capture long context information with a few parameters. This paper investigates a TDNN based VAD framework. A simple chunk based decision method is proposed to smooth raw posteriors and decide border points of utterances. To evaluate decision performance, a metric intersection-over-union (IoU) is introduced from image object detection. The experiment results are evaluated on Wall Street Journal (WSJ0) corpus. Frame classification performance is measured by area under the curve (AUC) and equal error rate (EER). Compared with long short-term memory baseline, the TDNN based system achieves a 41.26% EER relative reduction on average in matched noise condition, and relative improvement of average AUC is 3.82%. Proposed decision method achieves an 18.74% IoU relative improvement on average compared with moving average method on average.

关键词： Measurement neural networks Computer architecture Hidden Markov models Voice activity detection Speech recognition Object detection

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Effect of feature extraction and classification method on hyperspectral image classification accuracy

Effect of feature extraction and classification method on hy...

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IEEE Signal processing and Communications applications (SIU)

作者： Ahmed Alraimi Sarp Ertürk Elektronik ve Haberlesme Muhendisligi Bolumu Kocaeli Universitesi Kocaeli Turkiye

In this paper, the effect of different hyperspectral images feature extraction techniques in ANN and SVM classifiers is investigated. While a high accuracy and efficiency for HLFE method was shown in ANN classifier in the literature, in this study it is shown that using the RBF kernel in SVM provides increased accuracy for PCA and KPCA meanwhile poor classification accuracy is achieved for HLFE. Therefore it is shown in this paper that it is important to take feature extraction and classification methods together into account, as one feature extraction method might outperform the other for a certain classification approach but might provide poor performance for another. This is an important point, usually neglected in the literature.

关键词： artificial neural networks Support vector machines Feature extraction Hyperspectral imaging Principal component analysis Kernel Training

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An approach to recognition of human patterns in movement

An approach to recognition of human patterns in movement

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An International Meeting on Engineering in Medicine and Biology Society,and conference of the Biomedical Engineering Society of India

作者： A. Pazos A. Rivas-Feal Department of Computer Science Faculty of Informatics of La Coruña Group of Applications of Artificial Intelligence Spain

The system the authors propose here aims to establish an automatization of the data acquisition process from video sequences in sporting environments. All of this is devoted to perform an analysis of human movement in an affordable way. One has to take into account that if one is working with very narrow environments (e.g. sport movements and specifically cyclic movements), the main problem, that is shape recognition and interpretation from images, reduces the complexity and thus the analysis can be treated in an easier way. Under this condition, a database for the storage of graphic patterns aids the system in situations with a nonclear solution, and at the same time allows one to save the environmental conditions of the image sequence. These conditions can change a lot of times and can make the recognition process difficult. The authors have also used artificial neural networks for pattern association between patterns from the image, in some cases incomplete, and those stored in a database. This process will be preceded, of course, by filtering operations and typical techniques of image processing for improving the features of each frame from the sequence to be recognized.

关键词： Pattern recognition Humans image recognition image databases image storage Data acquisition Video sequences Performance analysis Shape image analysis

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Training a neural Network on Virtual Reality Devices: Challenges and Limitations

Training a Neural Network on Virtual Reality Devices: Challe...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Francisco Díaz-Barrancas Daniel Flores-Martin Javier Berrocal Justus-Liebig-University Germany CénitS - COMPUTAEX Spain University of Extremadura Spain

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

The processing power of Virtual Reality (VR) devices is constantly growing. However, few applications still take advantage of these capabilities. Machine learning algorithms have shown promise in enabling an immersive and personalized experience for VR device users. Therefore, it is interesting that these algorithms are processed directly on the devices themselves, without needing other external resources. In this work, a neural Network (NN) is trained for real-time image classification using different VR devices. The results show the feasibility of incorporating VR devices for NN training without compromising the quality of the interaction, simply and saving external resources.

关键词： Training Machine learning algorithms Three-dimensional displays Virtual reality artificial neural networks User interfaces Real-time systems

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