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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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TPC-NAS: Simple and Effective Neural Architecture Search Based on Total Path Count

TPC-NAS: Simple and Effective Neural Architecture Search Bas...

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6th International conference on AI Circuits and systems (AICAS)

作者： Huang, Ming-Shan Chen, Pi-Chuan Chiueh, Tzi-Dar Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9798350383638;9798350383645

With the explosive growth of neural network (NN) research and application areas, there is a pressing need to automate the NN model search process in order to attain optimal performance. Nevertheless, existing neural architecture search (NAS) algorithms are time-consuming, resource-intensive, and predominantly tailored for image-related applications. This paper presents the Total Path Count (TPC) score, a straightforward yet highly efficient accuracy predictor solely reliant on the architectural information of a model. The effectiveness of the TPC score is underscored by a robust rank correlation of 0.96 between TPC scores and the accuracies of CIFAR100 architectures. We further introduce TPC-NAS, a zero-shot NAS method that can complete a NAS task in under five CPU minutes without training and inference. TPC-NAS has found wide-ranging applications and it outperforms many other NAS solutions. In image classification, TPC-NAS achieves 78.3% imageNet top-1 accuracy with 399M FLOPs, while in object detection, it improves mAP by at least 2% over other NAS-derived models. Moreover, TPC-NAS successfully discovers a super-resolution architecture with < 300K parameters and achieves 32.09dB PSNR. In NLP, TPC-NAS delivers a model that matches tinyBERT's FLOPs but outperforms it by almost 10% in accuracy. These experiments illustrate TPC-NAS's ability to rapidly generate high-performance CNN/transformer architectures for various applications.

关键词： Neural Architecture Search (NAS) image Classification Super-Resolution Object Detection Natural Language processing

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Turkish Document image Classification

Turkish Document Image Classification

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International conference on Advanced Engineering, Technology and Applications, ICAETA 2024

作者： Nar, Meryem Tuğba Durukan, Gürcan Özcan, Abdullah Çakıl, Lütfü Kara, Hüseyin Omurca, Sevinç İlhan GoLive Yazılım Hizmetleri Istanbul Turkey Kocaeli University İzmit Turkey

ISBN: (纸本)9783031709234

Document image classification has gained extensive attention due to the rising number and types of scanned documents. Multi-modal architectures, processing image and text simultaneously, leverage the strengths of each modality. This study explores an efficient neural architecture for classifying scanned documents in a private company. The effectiveness of CNN-based deep learning and OCR algorithms in extracting textual and visual features is investigated. Different feature fusion methods are applied in the next stage to combine these extracted features. A multi-modal document image classifier is developed for companies managing a large number of scanned documents, delivering superior performance even with fewer and faint documents. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Transfer learning

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Robust subspace clustering image segmentation algorithm based on noise suppression

Robust subspace clustering image segmentation algorithm base...

引用

Natural Language processing (ICNLP), International conference on

作者： xiumei Cai Rui Zhang Chenmao Wu School of Automation Xi'an Post and Telecommunications University Xi'an China School of Electronic Engineering Xi'an Post and Telecommunications University Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Separating meaningful visual structures from images is one of the most fundamental tasks addressed by image analysis algorithms. Research on existing subspace clustering segmentation methods reveals that most of them can only handle noise-free or low-noise scenarios. The proposed method aims to address the robustness issues of subspace clustering segmentation algorithms in image segmentation tasks under high noise conditions. image segmentation, as the subspace clustering of image feature vectors, analyzes the impact of noise on subspace clustering segmentation algorithms. Firstly, the concept of non-local means filtering is applied to enhance the precision of image segmentation and to improve its resistance against high-noise segmentation models. Secondly, a novel centroid update robust superpixel segmentation algorithm is employed along with adaptive image denoising theories to reduce the noise influence on superpixel feature extraction, thereby producing more accurate feature vectors. Multiple features are extracted to establish a feature data matrix. This paper integrates these three denoising concepts and multiple superpixel feature extractions for the comprehensive application in subspace clustering segmentation algorithms. This approach aims to enhance the robustness of segmentation algorithms under noise and produce correct segmented images.

关键词： Resistance image segmentation Visualization Filtering Noise Noise reduction Clustering algorithms Feature extraction Vectors Robustness

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A Manager Portrait Method based on Neighborhood Rough Set and Natural Language processing Algorithm 3

A Manager Portrait Method based on Neighborhood Rough Set an...

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3rd International conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Liu, Youping Shen, Jiangang Xue, Junshuai China North Talent Research Institute Beijing China Beijing Mafumatou Technology Co. Ltd. Beijing China Xi’an Electronic Engineering Research Institute Xi’an China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

The management of enterprises has long included manager management as a key responsibility. Recently, manager portrait has attracted attention as a research focus in the field of manager management. This paper aims to model the problem of manager portrait using neighborhood Rough Set theory. Personality characteristics of managers are extracted from document materials using Natural Language processing algorithms and integrated into the rough set model. In addition, the distance formula of the KFHARA attribute reduction algorithm is improved. A label attributes construction method and a portrait method suitable for manager portrait are proposed based on the rough set model and the improved KFHARA algorithm. Finally, the practicability of the method is illustrated by an example of an enterprise manager selection and appointment business scenario. © 2023 SPIE.

关键词： Natural language processing systems

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Application of Database Technology in Video Surveillance System

Application of Database Technology in Video Surveillance Sys...

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2022 IEEE International conference on Knowledge Engineering and Communication systems, ICKES 2022

作者： Zhong, Chongli School of Information Engineering Xi'an Mingde Institute of Technology Shaanxi Xi'an710100 China

ISBN: (纸本)9781665456371

With the development of technology, the technological informationization of the security network video surveillance service industry has become the demand of the times. How to improve the functions of the video surveillance system and build an open security integrated monitoring management platform has become the research point of this article. This article intends to build a video surveillance system based on database technology to meet the multi-functional requirements of the surveillance system. This article mainly uses experimental methods to test the data of the monitoring system designed in this article, and then uses the comparative method to compare the speed of the three methods to calculate the data, and the data results are obtained. According to the experiment, the data processing time of the binary algorithm in the video surveillance system is within 15s. image detection in database technology uses binary algorithms to operate and analyze it more quickly. © 2022 IEEE.

关键词： Informatization

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Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play image Reconstruction 38

Provably Robust Score-Based Diffusion Posterior Sampling for...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Xu, xingyu Chi, Yuejie Carnegie Mellon University United States

In a great number of applications, the goal is to infer an unknown image from a small number of noisy measurements collected from a known and possibly nonlinear forward model describing certain sensing or imaging modality, which is often ill-posed. Score-based diffusion models, thanks to their impressive empirical success, have emerged as an appealing candidate of an expressive prior in image reconstruction. In order to accommodate diverse tasks at once, it is of great interest to develop efficient, consistent and robust algorithms that incorporate unconditional score functions of an image prior distribution in conjunction with flexible choices of forward models. This work develops an algorithmic framework for employing score-based diffusion models as an expressive data prior in nonlinear inverse problems with general forward models. Motivated by the plug-and-play framework in the imaging community, we introduce a diffusion plug-and-play method (DPnP) that alternatively calls two samplers, a proximal consistency sampler based solely on the likelihood function of the forward model, and a denoising diffusion sampler based solely on the score functions of the image prior. The key insight is that denoising under white Gaussian noise can be solved rigorously via both stochastic (i.e., DDPM-type) and deterministic (i.e., DDIM-type) samplers using the same set of score functions trained for generation. We establish both asymptotic and non-asymptotic performance guarantees of DPnP, and provide numerical experiments to illustrate its promise in various tasks. To the best of our knowledge, DPnP is the first provably-robust posterior sampling method for nonlinear inverse problems using unconditional diffusion priors. © 2024 Neural information processing systems foundation. All rights reserved.

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Reactive Power Optimization of Photovoltaic Distribution Network Based on Particle Swarm Optimization Algorithm 2

Reactive Power Optimization of Photovoltaic Distribution Net...

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2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Ouyang, Huawei xiang, Haoyuan School of Energy and Electrical Engineering Chang'an University Xi'an China

ISBN: (纸本)9798350360240

In the case of distribution systems characterized by a significant share of grid-connected photovoltaic installations, voltage issues can arise, primarily manifested as voltage fluctuations and elevations. When the output of PV generation is higher or lower than the demand of the distribution network, it can cause voltage fluctuations in the grid, especially when the PV generation is high and the electricity load is low, potentially elevating the grid voltage beyond the normal operating range. Furthermore, the intermittency and uncertainty of PV generation add complexity to the operation of the grid, posing challenges to its stability and safety. Therefore, effective management and regulation of PV integration into the distribution network are crucial for ensuring stable grid operation. The present paper establishes a mathematical model for optimizing reactive power compensation in the distribution network of photovoltaic power stations. It aims to minimize network losses as the primary objective, taking into account limitations related to active power, reactive power, and voltage. The model accounts for the reactive power regulation capability of grid-connected PV station inverters and employs the Particle Swarm Optimization (PSO) algorithm to solve the proposed reactive power optimization model. Finally, a typical IEEE-33 bus test case verifies the effectiveness of the proposed reactive power optimization model. © 2024 IEEE.

关键词： Power distribution networks

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Transformer-based Lossless Compression of Aurora Spectral images: A Spatial-Temporal-Spectral Joint Approach

Transformer-based Lossless Compression of Aurora Spectral Im...

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International conference on Signal processing Proceedings (ICSP)

作者： xinlong Cao Jiaojiao Li Yuanqing Li Jiapeng Yan Zhenxing Zheng School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts & Telecommunications Xi'an China Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi'an China International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province Xi'an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Aurora spectral image lossless compression has seen significant advancements in recent years. However, most compression algorithms are based on traditional image compression techniques, focusing solely on spectral and spatial correlations without considering temporal correlations. To further enhance compression performance, this paper proposes a Transformer-based point-by-point prediction algorithm for auroral spectral images, which simultaneously uses spatial, spectral and temporal contexts for prediction. Our prediction network consists of two parts: an encoder and a decoder. The encoder is composed of the encoding unit of the original Transformer and is used to extract features from spatial, spectral and temporal contexts. The decoder consists of fully connected layers and is used for prediction. Experimental results show that the average bitrate of this method is reduced by 0.179 bpp compared with the JPEG2000 algorithm, and the average bitrate is reduced by 0.054 bpp compared with the online DPCM algorithm.

关键词： Ion radiation effects image coding Correlation Signal processing algorithms Transformers Prediction algorithms Feature extraction Decoding Magnetosphere Compression algorithms

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Hardware-aware, deep-learning approaches for image denoising and star detection for star tracker sensor 10

Hardware-aware, deep-learning approaches for image denoising...

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10th International conference on Control, Decision and Information Technologies (CoDIT)

作者： Guesmi, Besma Moloney, David Ubot Technol Dublin Ireland

ISBN: (纸本)9798350373981;9798350373974

In recent years, Deep Neural Networks (DNNs) approaches have outperformed traditional techniques for several computer vision problems. This has been made possible by the increase of computational resources represented by Graphical processing Units (GPU) that allow training using large datasets and the availability of deep learning accelerators for inference. On the other hand, the attitude determination accuracy requirements for spacecraft are increasing. The most accurate attitude determination sensor for spacecraft is the so-called star sensor or star tracker. With the increase in lowcost satellite platforms such as CubeSats, research into the improvement of star sensor accuracy for low-power and low-cost sensor architectures remains a relevant subject. In this context, we examine several methods for noise reduction and star detection for improving centroiding performance. More specifically, an efficient and robust denoising method for star images using an Auto-Encoder (AE) is proposed. This method enhances the image quality for systems sensitive to noise. Furthermore, an accurate and lightweight algorithm based on an existing YOLO (You Only Look Once) architecture is proposed to detect the location of stars in the image. In this work, the YOLO bounding boxes are used to describe the space region around the stars. Subsequently, the star centroid within the bounding box is computed using the COG (Center Of Gravity) method. This method removes the need for centroiding algorithms sliding over the entire image area. An extensive comparison of the proposed denoising technique with other traditional filters confirms that the proposed method resists all noise models and reconstructs well the corrupted images. Experiments show that the proposed YOLO-based star detector achieves high accuracy with a lightweight architecture without any extra latency.

关键词： star tracker attitude determination deep learning image preprocessing denoising detection centroiding YOLOv5 autoencoder low-power

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An Enhanced Deep Learning algorithms for image Recognition and Plant Leaf Disease Detection 2

An Enhanced Deep Learning Algorithms for Image Recognition a...

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350325799

Object recognition in photographs has only been possible with the advent of modern deep learning (DL) techniques. Following its success in other fields, DL techniques are increasingly being used to a broad variety of agricultural operations. Due to its intrinsic complexity, image-based plant disease detection is difficult to solve. Targeted management relies on more than just detection and requires accurate identification of each species. An automated disease detection system aids the farmer by providing a speedy and accurate diagnosis of plant illness. Accelerating agricultural diagnostics requires the development of an automated system for identifying plant leaf diseases. Using machine learning and image processing, the authors of this study outline a strategy for detecting leaf blight. A photo of a leaf may be fed into this framework. In a first processing stage, noise is eliminated from the leaf images. The mean filter may be used to get rid of noise. Histogram equalisation is a method for enhancing image quality. Segmentation is a technique used in photography to break a large image down into more manageable sections. This helps to establish the picture's limits. The image is broken up using the BIRCH (Balance Iterative Reducing and Clustering utilising Hierarchies) technique. For this purpose of feature extraction, fine grained classification model, a convolutional neural network is used. After that, the pictures are categorized using a variety of methods, one of which is RCNN (Region based Convolutional neural network). Using this approach, this study discovered the split ratio, computational time, and as a result RCNN has resulted in a high accuracy of 0.98 when detecting the leaf compared to other algorithms. © 2023 IEEE.

关键词： Iterative methods

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