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

共 21063 条记录，以下是4811-4820 订阅

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Learning with Retrospection 35

Learning with Retrospection

引用

35th AAAI conference on artificial Intelligence / 33rd conference on Innovative applications of artificial Intelligence / 11th Symposium on Educational Advances in artificial Intelligence

作者： Deng, Xiang Zhang, Zhongfei SUNY Binghamton Comp Sci Dept Binghamton NY 13902 USA

ISBN: (纸本)9781577358664

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarded information indeed useless? We argue that the discarded information can benefit the subsequent training. In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. LWR is a simple yet effective training framework to improve accuracies, calibration, and robustness of DNNs without introducing any additional network parameters or inference cost, but only with a negligible training overhead. Extensive experiments on several benchmark datasets demonstrate the superiority of LWR for training DNNs.

关键词： Deep neural networks

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Convolutional neural Network Model for the Detection of Diseases and Pests in Coffee Crops 8

Convolutional Neural Network Model for the Detection of Dise...

引用

8th International Engineering, Sciences and Technology conference (IESTEC)

作者： Grimaldo, Gabriel Rodriguez, Humberto Lopez Cabrera, Victor Univ Tecnolog Panama Panama City Panama

ISBN: (纸本)9781665498432

The early detection of diseases and pests in coffee crops by means of artificial vision and pattern recognition brings with it the ease of inspection and the reduction of crop losses in coffee plantations. This work proposes a model based on neural networks that is capable of detecting coffee leaves in an image and also classifies them into the most common diseases in the Panamanian tropics. The coffee leaf disease classification model is able to classify the following diseases: Cercospora leaf spot, leaf rust, leaf miner and phoma. This model obtained an accuracy of 100% and a loss of 1.6 x 10(-5) during the training phase. Subsequent to the training phase, a validation of the model was performed;during this phase, an overall accuracy of 90% was obtained for each of the diseases and pests. In future work, it is desired to implement this architecture in the vision system of the agricultural robot. The vision system will allow farmers to effectively inspect and manage their crops.

关键词： Deep Learning Convolutional neural networks Digital image processing Leaves diseases detection Coffee crops Arabica coffee

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Gaussian noise image recognition based on convolutional neural networks

Gaussian noise image recognition based on convolutional neur...

引用

Computer Vision, image and Deep Learning (CVIDL), International conference on

作者： Chengqiang Huang Yanjun Yang Zunyi Normal University Zunyi China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Gaussian noise is one of the main noise sources of digital images. In this paper, the idea of judging before processing is adopted to improve the efficiency of Gaussian noise removal. First, the Pascal VOC dataset is taken as a clean image, and the Gaussian noise image dataset is obtained by adding Gaussian noise with random density. Then, various conventional and lightweight convolutional neural network models are constructed, and the constructed datasets are imported for training, validating and testing. Experimental results show that Vgg-16 achieves the highest classification accuracy of 0.9929. Due to its high computational complexity, VGG-16 is only applicable to scenarios that do not require mobile applications such as servers and hosts. SqueezeNet achieves the second-highest classification accuracy of 0.9906 with relatively low computational complexity, making it suitable for portable consumer electronics such as smartphones and smartwatches. Gaussian noise image recognition is realized based on conventional and lightweight convolutional neural network models, which provides a technical basis for efficient Gaussian denoising.

关键词： image recognition Accuracy Gaussian noise Digital images Computational modeling Noise reduction Convolutional neural networks

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In Vitro Quantification of Cellular Spheroids in Patterned Petri Dishes

In Vitro Quantification of Cellular Spheroids in Patterned P...

引用

16th International Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Schurr, Jonas Haghofer, Andreas Fürsatz, Marian Janout, Hannah Nürnberger, Sylvia Winkler, Stephan Bioinformatics Research Group University of Applied Sciences Upper Austria Softwarepark 11-13 Hagenberg Austria Department of Computer Science Johannes Kepler University Altenberger Straße 69 Linz Austria Department of Orthopedics and Trauma-Surgery Division ofTrauma-Surgery Medical University of Vienna Austria Ludwig Boltzmann Institute for Traumatology The Research Center in Cooperation with the AUVA Vienna Austria Austrian Cluster for Tissue Regeneration Vienna Austria

Cell Spheroids are of high interest for clinical cell applications and cell screening. To allow the extraction of early readout parameters a high amount of image data of petri dishes is created. To support automated analyses of spheroids in petri dish images we present a method for analysing and quantification of spheroids in its development stages. The algorithm is based on multiple image processing algorithms and neural networks. With an evolutionary strategy, engraved grid cells on petri dish are extracted and on top a Unet is used for the segmentation and quantification of different cell compartment states. The measured f 1 -scores for the different states are 0.77 for monolayer grid cells, 0.86 for starting formation grid cells and 0.85 for spheroids. As we describe in this study we can provide thorough analyses of cell spheroid in petri dishes, by automating the quantification process. © 2023 by SCITEPRESS - Science and Technology Publications, Lda

关键词： neural networks

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Investigating Brain Connectivity with Graph neural networks and GNNExplainer 26

Investigating Brain Connectivity with Graph Neural Networks ...

引用

26th International conference on Pattern Recognition / 8th International Workshop on image Mining - Theory and applications (IMTA)

作者： Zhdanov, Maksim Steinmann, Saskia Hoffmann, Nico Tech Univ Dresden HZDR Dresden Germany UKE Hamburg Germany HZDR Dresden Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain's functional and structural aspects, including mechanisms behind multiple pathologies. One such pathology is schizophrenia which is often followed by auditory verbal hallucinations. The latter is commonly studied by observing functional connectivity during speech processing. In this work, we have made a step toward an in-depth examination of functional connectivity during a dichotic listening task via deep learning for three groups of people: schizophrenia patients with and without auditory verbal hallucinations and healthy controls. We propose a graph neural network-based framework within which we represent EEG data as signals in the graph domain. The framework allows one to 1) predict a brain mental disorder based on EEG recording, 2) differentiate the listening state from the resting state for each group and 3) recognize characteristic task-depending connectivity. Experimental results show that the proposed model can differentiate between the above groups with state-of-the-art performance. Besides, it provides a researcher with meaningful information regarding each group's functional connectivity, which we validated on the current domain knowledge.

关键词： Deep learning Pathology Neuroscience Mental disorders Electroencephalography Graph neural networks Recording

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Automatic Recognition of Pictograms with Convolutional neural Network Approach for Literacy 2nd

Automatic Recognition of Pictograms with Convolutional Neura...

引用

2nd International conference on Smart Technologies, Systems and applications (SmartTech-IC)

作者： Reyes, Ayleen Vallejo-Huanga, Diego Univ Politecn Salesiana Dept Comp Sci Quito Ecuador Univ Politecn Salesiana IDEIAGEOCA Res Grp Quito Ecuador

ISBN: (纸本)9783030991708;9783030991692

Literacy is a fundamental and intrinsic right for every person;therefore, government agencies, NGOs, and private donors seek to implement sustainable and long-term plans that allow citizens to access quality, inclusive, and equitable education. But despite their efforts, illiteracy has not yet been fully eradicated, so educational programs and tools have been created to work to solve this problem. In this research, a web application is developed that takes the resources of the Google QuickDraw API to generate an interactive image interpretation tool, which aims to support the learning of reading and writing in people with illiteracy. The tool allows an illiterate person to draw pictograms that are interpreted by the software and through convolutional neural networks with deep learning, a prediction of the image is generated. The result is presented in a graphical user interface, which displays a real image corresponding to the prediction together with text and audio assistance, essential for user learning. The results show a performance of 75% accuracy in the training and 56% in the test. The performance tests yielded indicate that the tool meets the minimum software requirements for its future implementation in institutions that work in the education of people with illiteracy.

关键词： Illiteracy QuickDraw LeNet CNN architecture Deep learning image processing

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On-the-fly image Compression Using Overfitted Shallow CNN Autoencoders

On-the-fly Image Compression Using Overfitted Shallow CNN Au...

引用

International image processing, applications and Systems conference (IPAS)

作者： John El Gallab Abdelwahab Ganna Abdallah Kassem Hend Makram Martina Muawad Mina Mikhael Tamer ElBatt Computer Science and Engineering The American University in Cairo Cairo Egypt

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In the era of pervasive Internet use, managing large volumes of image data becomes crucial. To mitigate storage and bandwidth costs, image compression plays a pivotal role. Traditional image compression techniques like JPEG and PNG, while widely used, may suffer from limited compression rates. In this work, we propose a novel approach using shallow Convolutional neural Network (CNN) autoencoders for on-the-fly image compression. Our model aims to achieve high compression rates with improved image quality compared to classical methods. Additionally, it supports decompression on the CPU in realtime, making no assumptions about client-side computational resources. We present a comprehensive methodology, including architecture design, experiments, and performance metrics. Our results demonstrate the effectiveness of the proposed approach, providing a competitive alternative for online content compression and decompression that outperforms JPEG in image quality assessment metrics at 33% compression rate. We also outperform other Learned image Compression (LIC) techniques in both the decompression time and number of trainable parameters with an improvement in the order of 10-times.

关键词： Measurement image quality image coding Autoencoders Transform coding Bandwidth Internet Convolutional neural networks Time factors image reconstruction

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Classification of Plastic Waste Products using Deep Learning 1

Classification of Plastic Waste Products using Deep Learning

引用

1st International conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Asha, V. Govindaraj, M. Kolambkar, Mayur L Mithuna, P. Narendra Gautham, V. Prasad, Arpana New Horizon College of Engineering Department of MCA Bengaluru India

ISBN: (纸本)9798350361780

Plastic waste has become a significant environmental concern, necessitating innovative approaches to enhance recycling processes. Traditional methods of sorting plastic waste are labor intensive and error prone, leading to suboptimal recycling rates. This study proposes a novel solution by utilizing deep learning methods to the automated classification of plastic waste based on its composition. The proposed system employs Convolutional-neural-networks to analyze images regarding plastic waste, providing a precise and effective way to classify various kinds of plastics. The dataset that was utilized to train the model includes plastic items of a diverse range, making sure that the model's ability of generalizing, across the various shapes, colors, and textures. Transfer learning is implemented to leverage pre trained models, facilitating the adaptation to the specific task of plastic waste classification. The implementation of deep learning based plastic waste classification contributes to the growth of sustainable waste management practices. By automating the sorting process, the recycling industry can significantly reduce processing time, minimize errors, and increase overall recycling efficiency. This research aligns with the global goal of mitigating plastic pollution and promotes a circular economy by optimizing the reuse of plastic materials. The study's conclusions encourage further exploration of deep learning applications in waste management to address environmental challenges and promote a more sustainable future. © 2024 IEEE.

关键词： image analysis

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Training and Algorithm Optimization of Stable Pendulum Motion of Cart Based on BP neural Network and Gradient Descent Method

Training and Algorithm Optimization of Stable Pendulum Motio...

引用

image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Yikun Chen College of Engineering China Agricultural University Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

BP neural network is using gradient descent method to continuously adjust the weights and thresholds between the input layer and the hidden layer, so that the error decreases along the direction of the gradient, and the training stops when the variance between the actual output layer and the desired output layer reaches the minimum, and obtains the threshold of the best training performance. However, the drawbacks of the gradient descent method are reflected in the control training of the steady pendulum motion of the cart based on BP neural network and gradient descent method, that is, although the direction of the gradient descent is the direction of the fastest reduction of the loss function, this is not necessarily the direction of the fastest convergence. And at the same time, since the gradient descent method utilizes the local nature of the objective function, it is very likely to fall into the local minimum in the iteration. To address the above drawbacks, we compare the training results using gradient descent, Newton's method, and conjugate gradient method, and find that Newton's method as well as conjugate gradient method have faster convergence, higher recognition rate, and better training results.

关键词： Training Gradient methods image recognition Accuracy neural networks Computer applications Linear programming Newton method Optimization Convergence

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A Lightweight Skin Cancer Detection Model Based on Convolutional neural Network 2

A Lightweight Skin Cancer Detection Model Based on Convoluti...

引用

2022 2nd International conference on artificial Intelligence, Big Data and Algorithms, CAIBDA 2022

作者： Li, Yizhou Mao, Hongxi Wang, Zhiran School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Computer Science Wuhan University Wuhan430072 China Qiushi Honors College Tianjin University Tianjin300072 China

ISBN: (纸本)9783800760268

With the development of artificial Intelligence (AI), deep neural networks have been used in a variety of fields such as digital signal processing, object detection as well as medical use. It acts as a vital aid to people, especially when it comes to huge workloads. Skin cancer has been considered the most common cancer with universality and detectability. We aim at constructing a lightweight detection model which can be adopted on FPGA or other SCMs to function as a more convenient family doctor with high accuracy of classification. In this paper, we solved the problem of data imbalance and proposed a new model based on the structure of the fire module and CNN to achieve the goal of categorizing skin cancers. Furthermore, we compare our model with prevalent image classifiers and the result indicates that our model has a better performance in both accuracy and capacity. Finally, the trained model is lightweight but efficient and we got a high accuracy of 97.8%. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Object detection

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