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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6128 条记录，以下是381-390 订阅

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Padding-Free Convolution Based on Preservation of Differential Characteristics of Kernels 22

Padding-Free Convolution Based on Preservation of Differenti...

引用

22nd IEEE International conference on machine learning and applications, ICMLA 2023

作者： Leng, Kuangdai Thiyagalingam, Jeyan Science and Technology Facilities Council Scientific Computing Department Didcot United Kingdom

ISBN: (纸本)9798350345346

Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundary effects. We present a non-padding-based method for size-keeping convolution based on the preservation of differential characteristics of kernels. The main idea is to make convolution over an incomplete sliding window 'collapse' to a linear differential operator evaluated locally at its central pixel, which no longer requires information from the neighbouring missing pixels. While the underlying theory is rigorous, our final formula turns out to be simple: the convolution over an incomplete window is achieved by convolving its nearest complete window with a transformed kernel. This formula is computationally lightweight, involving neither interpolation or extrapolation nor restrictions on image and kernel sizes. Our method favours data with smooth boundaries, such as high-resolution images and fields from physics. Our experiments include: i) filtering analytical and non-analytical fields from computational physics and, ii) training convolutional neural networks (CNNs) for the tasks of image classification, semantic segmentation and super-resolution reconstruction. In all these experiments, our method has exhibited visible superiority over the compared ones. © 2023 IEEE.

关键词： computer vision convolutional neural network differential operator machine learning padding

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Rice Leaf Disease Detection Using image processing Techniques 4th

Rice Leaf Disease Detection Using Image Processing Technique...

引用

4th International conference on Data, Engineering, and applications, IDEA 2022

作者： Gayathiri, P. Nirmala College for Women Coimbatore641018 India

ISBN: (纸本)9789819724505

One of the factors that have contributed significantly to human civilization's development is agriculture. Automation has started to control every industry in this modern era. However, compared to other industries, farm automation is not as sophisticated. As a result, the need for an automation system in agriculture is pressing. Finding illnesses in crops at an early stage of growth is one of the main problems that farmers confront. This research aims to develop an autonomous drone to identify diseases at the earliest and prevent the crop from diseases. Plant or crop diseases play a significant role in both value and amount in agriculture, it is significantly effective to identify and diagnose these disorders. A significant scientific difficulty is a proper classification with small datasets in machine learning. Disease categorization, image processing, pattern recognition, and disease detection are all accomplished using the neural network whale optimization method. The neural network whale optimization is suggested in this research study as a way to identify rice leaf diseases. This study presented 5932 on-field photos of four different rice leaf diseases, including bacterial blight, blast, brown spot, and tungro. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： neural networks

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ResNet-Based Camera Model Identification with Adaptive Preprocessing Module and Weight Fusion of Global Information

ResNet-Based Camera Model Identification with Adaptive Prepr...

引用

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

作者： Chen, Boru Abdulla, Waleed Univ Auckland Auckland New Zealand

ISBN: (纸本)9798350300673

Identifying camera models used in digital images is an essential component of image forensics, which plays a vital role in preventing image-related crimes. While deep learning techniques, especially Convolutional neural networks (CNNs), are currently the most commonly used approach for Camera Model Identification (CMI) due to their high efficiency and accuracy, the performance of the CNN-based method will be adversely affected by the image content due to its structure. In this study, we introduce a novel approach to CMI, which utilizes the Residual neural Network (ResNet), Adaptive Preprocessing Module (APM), and Weight Fusion of Global Information (WFGI) to overcome this issue. The results of the experiment indicate that our proposed method has achieved an accuracy exceeding 97.5% when tested on a large-scale dataset that consists of 45 different camera models based on the Vision and Dresden database. Furthermore, our approach exhibits robustness under different attack scenarios, thereby highlighting its potential for practical use in image forensics applications.

关键词： Convolutional neural networks

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image Captioner: Captioning for Visual Impact 1

Image Captioner: Captioning for Visual Impact

引用

1st IEEE International conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Aryan, Shabareesh Subrahmanya, S. Jagtap, Pahel Mani, Leander Sowmya, M.R. Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350316469

image captioning is the process of generating a textual description that accurately represents the features of an image. In the realm of deep learning, this task is of utmost significance and has a wide range of uses. image captioning involves converting an image, represented as a sequence of pixels, into a sequence of words that are relevant to the image. It can be seen as an end-to-end and sequence-to-sequence challenge, as both the language and visual aspects need to be processed. In this regard, recurrent neural networks are employed for the language processing, while convolutional neural networks are used to extract feature vectors from the images. The findings of this study highlight the effectiveness of this method and show how diverse applications in image processing and description generation are possible. This work promotes the incorporation of these methods into actual image captioning systems in order to provide more precise and contextually appropriate image descriptions. © 2023 IEEE.

关键词： BLEU: Bilingual Evaluation Understudy convolutional neural networks image captioning long short-term memory pooling recurrent neural networks unsupervised learning

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EyeRis: Visual image Recognition using machine learning for the Visually-Impaired

EyeRis: Visual Image Recognition using Machine Learning for ...

引用

2023 International conference on Electronics, Information, and Communication, ICEIC 2023

作者： Eugenio, Alliah May Patulot, Marian Jowie Seguiro, Lykha Jane Tuazon, Angelica Nicole Huyo-A, Shekinah Lor Abisado, Mideth Sampedro, Gabriel Avelino Technological University of the Philippines Department of Computer Science Manila Philippines Research and Development Center Philippine Coding Camp Manila Philippines National University College of Computing and Information Technologies Manila Philippines University of the Philippines Open University Faculty of Information and Communication Studies Laguna Philippines

ISBN: (纸本)9798350320213

Visually impaired people struggle daily and have difficulty recognizing and distinguishing objects around them. Thus, they mainly depend on supervision from other people to assist them. Since smartphones have become a necessity in this modern world, the researchers formulated a solution to help the visually impaired through a machine learning-based mobile application for object recognition. Nowadays, software applications can provide accurate findings in picture classification and processing processes thanks to machine learning techniques and algorithms. In this study, the researchers use a convolutional neural network (CNN)-based system on TensorFlow Lite to create a mobile version of a visual information system employing a machine learning strategy and deep learning framework. The main objectives of the smartphone application, EyeRis, is to recognize and categorize items in real-time and to separate photographs from the user-selected scenarios. Results were analyzed and contrasted based on the app's obtained recognition accuracy data. It demonstrated the utility of CNN as a model for image recognition algorithms. © 2023 IEEE.

关键词： Convolutional neural networks machine learning Visual image Recognition

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Maintaining original color when applying super-resolution to individual bands

Maintaining original color when applying super-resolution to...

引用

conference on applications of machine learning

作者： Zhang, Bingcai Luu, Yan BAE Syst 10920 Technol Pl San Diego CA 92127 USA

ISBN: (纸本)9781510665651;9781510665644

We have developed a system that applies deep learning application super-resolution (SR) to multispectral and hyperspectral geospatial satellite imagery to deduce higher resolution images from lower resolution images while maintaining the original color of the lower resolution pixels. A super-resolution model, which uses Deep Convolution neural networks (DCNNs), is trained using individual image bands, a large crop size or tile size of 512 x 512 pixels, and a de-noise algorithm. Applying our algorithms to maintain the original color of the image bands improves the quality metrics of the super-resolution images as measured by peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of super-resolution images. One of the most important applications of satellite images is to automatically detect small objects such as vehicles and small boats. With super-resolution images generated by our system, the object detection accuracy (recall and precision) has improved by 20% with Planet (R) multispectral satellite images.

关键词： Super-resolution satellite image convolutional neural network image transformation object detection.

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Decoupling Feature Extraction and Classification Layers for Calibrated neural networks 41

Decoupling Feature Extraction and Classification Layers for ...

引用

41st International conference on machine learning, ICML 2024

作者： Jordahn, Mikkel Olmos, Pablo M. Cognitive Systems Technical University of Denmark Kongens Lyngby Denmark Universidad Carlos III de Madrid Madrid Spain

Deep neural networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized. Improving DNN calibration without comprising on model accuracy is of extreme importance and interest in safety critical applications such as in the healthcare sector. In this work, we show that decoupling the training of feature extraction layers and classification layers in over-parametrized DNN architectures such as Wide Residual networks (WRN) and Vision Transformers (ViT) significantly improves model calibration whilst retaining accuracy, and at a low training cost. In addition, we show that placing a Gaussian prior on the last hidden layer outputs of a DNN, and training the model variationally in the classification training stage, even further improves calibration. We illustrate these methods improve calibration across ViT and WRN architectures for several image classification benchmark datasets. Copyright 2024 by the author(s)

关键词： Deep neural networks

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Enhancing the Accuracy in the Classification of images using Convolutional neural Network Algorithm over Support Vector machine Algorithm

Enhancing the Accuracy in the Classification of Images using...

引用

2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Dhanya Lakshmi, S. Vimal, V.R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350373783

To classify novel images using the Convolutional neural Network (CNN) algorithm and assess how accurate it is in comparison to the Support Vector machines algorithm. Convolutional neural networks (CNNs) are a subset of artificial neural networks with a distinct design that are based on machine learning. Convolutional neural networks have achieved cutting edge results in computer vision tasks by using certain characteristics of the visual brain. The dataset needed for Novel image classification is acquired from the Tensorflow website. The applications for cloud computing and the cloud framework's infrastructures are planned, developed, and put into use. The data sets are imported, and the Convolutional neural Network Algorithm and Support Vector machines Algorithm are run through their paces. For two algorithms, there are two categories. There are 547 samples in each category. Accuracy for the supplied inputs is the outcome that is obtained. The results are obtained using the IBM SPSS program. According to the findings, there is a statistically significant difference between the Convolutional neural Network Algorithm and its accuracy of 91.10 percent. The value is less accurate than Support Vector machines Algorithm 86.48%, p=0.001 ( © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning in food category recognition 3

Deep learning in food category recognition

引用

2024 3rd International conference on image processing, Object Detection, and Tracking, IPODT 2024

作者： Zhou, Jie Master of Software Engineering The University of Melbourne Melbourne Australia

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

This paper proposes a deep learning-based food image recognition system to enhance checkout efficiency in university cafeterias. Addressing the bottlenecks of traditional manual checkout methods, a food image classification model has been designed and implemented, combining Convolutional neural networks (CNN) and Fully Connected networks (FCN). The system extracts image features through convolutional layers, reduces feature map dimensions using pooling layers, and makes classification decisions through fully connected layers. Additionally, data augmentation techniques were utilized to expand the training data, and optimization methods such as regularization and Dropout were introduced to improve the model's generalization ability. The main structure of the paper includes sections on system architecture design, data preparation and processing, experimental results, and analysis. The system architecture design section details the construction methods of CNN and FCN and their application in this system. The data preparation and processing section describes the process of obtaining raw data, data augmentation, and preprocessing. The experimental results and analysis section demonstrates the impact of different parameter settings on model performance and proves the effectiveness of the proposed method through experimental data. The system is characterized by its efficient food image classification ability and good generalization performance. Extensive experiments on the dataset revealed that increasing the sample size and setting hyperparameters appropriately can significantly improve the model's accuracy and stability. With a sample size of 10,000, the model achieved an average accuracy of 86.88%, and the low standard deviation indicates stable performance in practical applications. © 2024 SPIE.

关键词： Convolutional neural networks

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Automatic Optimization-Based Methods in machine learning: A Systematic Review

Automatic Optimization-Based Methods in Machine Learning: A ...

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Intelligent Systems conference (IntelliSys)

作者： Shahrabadi, Somayeh Adao, Telmo Alves, Victor Magalhaes, Luis G. Ctr Comp Graph CCG Campus AzuremEdificio 14 P-4800058 Guimaraes Portugal Univ Minho ALGORITMI Res Ctr Guimaraes Portugal

ISBN: (纸本)9783031477232;9783031477249

machine learning (ML) is a subfield of Artificial Intelligence (AI) that has been applied to various fields ranging from industrial to medical sectors, to perform miscellaneous Computer Vision tasks such as image classification, image segmentation, object detection, and language modeling. Notwithstanding, having a suitable model with practical applicability requires performing appropriate structural operations upon datasets, building adequate CNN architectures from the scratch or resorting to the ones available in the state-of-the-art, and, either way, parameterizing them to improve machine learning skills, usually, in a trial-and-error fashion. Aligned with this context, despite the several semi-/fully automatic approaches that can be found in the literature, (e.g., grid search for hyperparameter fine-tuning, auto-machine learning for self-configurable model development, and automatic methods for data arrangement and augmentation), which are often integrated with combination to establish automatic pipelines for the effective implementation of solutions powered by AI, surveys documenting such topic seem to be scarce. Therefore, the main goal of this work is to present an updated yet extensive literature review focusing on this class of approaches, considering the importance of their role in the perspective of ML Optimization.

关键词： Deep learning machine learning Artificial Intelligence Computer Vision Dataset learning Optimization Auto ML Automatic Data Augmentation CNN Architecture Hyperparameter Optimization neural Architecture Search

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