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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Mas-Candela, Enrique Rios-Vila, Antonio Calvo-Zaragoza, Jorge Univ Alicante UI Comp Res Alicante Spain

ISBN: (纸本)9783031048814;9783031048807

Detecting the corresponding editions from just a pair of input-output images represents an interesting task for artificial intelligence. If the possible image transformations are known, the task can be easily solved by enumeration with brute force, yet this becomes an unfeasible solution for long sequences. There are several state-of-the-art approaches, mostly in the field of image forensics, which aim to detect those transformations;however, all related research is focused on detecting single transformations instead of a sequence of them. In this work, we present the image Transformation Sequence Retrieval (ITSR) problem and describe a first attempt to solve it by considering existing technology. Our results demonstrate the huge difficulty of obtaining a good performance-being even worse than a random guess in some cases and the necessity of developing specific solutions for ITSR.

关键词： Computer vision image transformations Deep learning

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Learning Sparse Masks for Diffusion-Based image Inpainting 10th

Learning Sparse Masks for Diffusion-Based Image Inpainting

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Alt, Tobias Peter, Pascal Weickert, Joachim Saarland Univ Fac Math & Comp Sci Math Image Anal Grp Campus E1-7 D-66041 Saarbrucken Germany

ISBN: (纸本)9783031048814;9783031048807

Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial location - the inpainting mask - is challenging. A commonly used tool for this task are stochastic optimisation strategies. However, they are slow as they compute multiple inpainting results. We provide a remedy in terms of a learned mask generation model. By emulating the complete inpainting pipeline with two networks for mask generation and neural surrogate inpainting, we obtain a model for highly efficient adaptive mask generation. Experiments indicate that our model can achieve competitive quality with an acceleration by as much as four orders of magnitude. Our findings serve as a basis for making diffusion-based inpainting more attractive for applications such as image compression, where fast encoding is highly desirable.

关键词： image inpainting Diffusion Partial differential equations Data optimisation Deep learning

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Abbreviating Labelling Cost for Sentinel-2 image Scene Classification Through Active Learning 10th

Abbreviating Labelling Cost for Sentinel-2 Image Scene Class...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Raiyani, Kashyap Goncalves, Teresa Rato, Luis Univ Evora Sch Sci & Technol Dept Informat P-7000671 Evora Portugal

ISBN: (纸本)9783031048814;9783031048807

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become an important part of any problem-solving process. Active Learning gains importance when, given a large amount of freely available data, there's a lack of expert's manual labels. This paper proposes an active learning algorithm for selective choice of training samples in remote sensing image scene classification. Here, the classifier ranks the unlabeled pixels based on predefined heuristics and automatically selects those that are considered the most valuable for improvement;the expert then manually labels the selected pixels and the process is repeated. The system builds the optimal set of samples from a small and non-optimal training set, achieving a predefined classification accuracy. The experimental findings demonstrate that by adopting the proposed methodology, 0.02% of total training samples are required for Sentinel-2 image Scene Classification while still reaching the same level of accuracy reached by complete training data sets. The advantages of the proposed method is highlighted by a comparison with the state-of-the-art active learning method named entropy sampling.

关键词： Active learning Supervised classification Sentinel-2 image scene classification pattern recognition Training data reduction

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A Survey of Some Important Algorithms Used in Military Applications 11

A Survey of Some Important Algorithms Used in Military Appli...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + Systems and Internet of Things (CPS and IoT)

作者： Capuni, I Milutinovic, V Barleti Univ Tirana Albania Univ Indiana Bloomington IN USA

ISBN: (纸本)9781665468282

This is a short survey of ten algorithms that are often used for military purposes, followed by analysis of their potential suitability for dataflow and GaAs, which are a specific architecture and technology for supercomputers on a chip, respectively. Whenever an algorithm or a device is used in military settings, it is natural to assume strict requirements related to speed, reliability, scale, energy, size, and accuracy. The two aforementioned paradigms seem to be promising in fulfilling most of these requirements.

关键词： dataflow GaAs kNN Benders decomposition pattern recognition image registration neural networks perceptron SVM

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A Study of Augmentation Methods for Handwritten Stenography recognition 11th

A Study of Augmentation Methods for Handwritten Stenography ...

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

作者： Heil, Raphaela Breznik, Eva Centre for Image Analysis Department of Information Technology Uppsala University Uppsala Sweden

ISBN: (纸本)9783031366154

One of the factors limiting the performance of handwritten text recognition (HTR) for stenography is the small amount of annotated training data. To alleviate the problem of data scarcity, modern HTR methods often employ data augmentation. However, due to specifics of the stenographic script, such settings may not be directly applicable for stenography recognition. In this work, we study 22 classical augmentation techniques, most of which are commonly used for HTR of other scripts, such as Latin handwriting. Through extensive experiments, we identify a group of augmentations, including for example contained ranges of random rotation, shifts and scaling, that are beneficial to the use case of stenography recognition. Furthermore, a number of augmentation approaches, leading to a decrease in recognition performance, are identified. Our results are supported by statistical hypothesis testing. A link to the source code is provided in the paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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DAT-SegNet: A Medical Multi-Organ Segmentation Network Based on Deformation Attention and Transformer 3

DAT-SegNet: A Medical Multi-Organ Segmentation Network Based...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Huang, Wenxuan Liu, Chunlin Li, Ruiyi Chen, Peizhi College of Computer and Information Engineering Xiamen University Of Technology Fujian Xiamen361024 China

ISBN: (纸本)9781510667563

With the development of Transformer and its derivatives over the last two years, several research have integrated Transformer with CNN or in instead of CNN to advance medical image *** they often create acceptable feature maps for large organs, segmentation accuracy for small organs is less than satisfactory. Transformer excels at global context, but it displays limits in capturing fine-grained features, especially in medical areas, because convolution is unable to describe long-term correlations prevalent in images. This is because local information modeling lacks a spatial inductive bias. Currently available Transformer-based segmentation networks are not often optimized for this issue, we therefore use transformer as the backbone and provide a medical image segmentation network with deformation attention. The model uses attention mechanism to boost the impact of feature maps to address the issue of low accuracy of tiny organ segmentation in multi-organ segmentation tasks. On the Synapse dataset, our model has so far produced SOTA results. © 2023 SPIE.

关键词： image segmentation

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Improved clothing segmentation algorithm based on Deeplabv3 + network 3

Improved clothing segmentation algorithm based on Deeplabv3 ...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Shi, Xiujin Wu, Danxin Donghua University Songjiang Shanghai China

ISBN: (纸本)9781510667563

In recent years, the fashion industry has developed rapidly, and the image is one of the main carriers for people to obtain clothing information. Therefore, the analysis and processing of clothing image through the technology in the field of artificial intelligence has become a major research content in the field of clothing development. image segmentation technology can be used to segment different clothing regions from the clothing image in order to facilitate the subsequent analysis and processing of clothing image, which is one of the basic research directions of clothing image analysis. At present, most of the methods belong to the traditional segmentation methods, or are based on the deep convolutional neural network (DCNN). Especially for Deeplab, FCN and their improved network segmentation models, although these network models have different degrees of improvement compared with the traditional methods, there are still some problems such as the fitting degree of clothing region segmentation is not high and the effect of clothing edge segmentation is poor. To solve these problems, this paper adds object context information extraction module and edge optimization module based on Deeplab v3+ network. The object context information extraction module focuses on the context information aggregation method in semantic segmentation, and enhances the feature representation of pixel points with the help of the feature information of the corresponding clothing region, so as to improve the label classification effect of pixel points in the clothing image and improve the fitting degree of clothing region segmentation. The edge optimization module makes the pixels in the edge area directly use the label prediction of the pixels in the same category area to greatly improve the edge segmentation effect. Our method performs well on the DeepFashion-MultiModal dataset, and experiments show that the improved Deeplabv3 + network outperforms the original Deeplabv3 + network and oth

关键词： Pixels

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DCP-YOLOv7: Dark Channel Prior image-Dehazing YOLOv7 For Vehicle Detection in Foggy Scene 3

DCP-YOLOv7: Dark Channel Prior Image-Dehazing YOLOv7 For Veh...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Zhao, Shuaiyin Fang, Yujing HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781510667563

Aiming at the problem that the vehicle detection process in the foggy scene in automatic driving is vulnerable to the influence of fog, haze and other weather conditions, which causes a decrease in the quality of images captured by sensors, resulting in low accuracy of vehicle detection and positioning. This paper proposed a joint optimization scheme by combining DCP and YOLOV7 backbone detection networks called DCP-YOLOv7. This network consists of two modules: the image dehazing module DCP and the YOLOv7 detection module. Specifically, the DCP module performs image dehazing and YOLOv7 module performs vehicle detection. Experiments were conducted on the synthetic foggy VisDrone dataset to test the detection performance of DCP-YOLOv7. The results show that the mean Average Prediction (IoU=0.5) is 36.6% and 16.0% higher than that of SSD and Faster RCNN algorithm, reaching 86.7%, which can effectively detect vehicle object in foggy scenes. © 2023 SPIE.

关键词： Automobile drivers

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Cloud Feature Extraction and Fluctuation pattern recognition Based Ultrashort-Term Regional PV Power Forecasting

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第5期58卷 6752-6767页

作者： Wang, Fei Li, Jianan Zhen, Zhao Wang, Chao Ren, Hui Ma, Hui Zhang, Wei Huang, Lang North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microg Beijing 102206 Peoples R China Inner Mongolia Power Grp Co Ltd Dispatch & Control Ctr Hohhot 010000 Peoples R China Beijing Goldwind Smart Energy Technol Co Ltd Beijing 100176 Peoples R China TBEA Xinjiang Sunoasis Co Ltd Xian 710119 Peoples R China

Regional photovoltaic(PV) power forecasting provides a foundation for grid management and trading in the power markets. To tackle the deficiency of conventional regional PV power modeling methods, such as the problem of challenging to select useful meteorological information and the problem that a single model cannot fully learn the complex and diverse fluctuation characteristics of power curves, an ultrashort-term regional PV power forecasting framework assembled by fusing fluctuation pattern recognition (FPR) and deep learning modeling under a fluctuation pattern prediction (FPP) model was proposed. First of all, the fluctuation characteristics of the regional PV power curves were extracted, including sampling loss area, mean value, standard deviation, and third derivative. Then, a K-means algorithm based FPR model was established to obtained three kinds of patterns. FPP model based on convolutional neural network is used to predict future PV power fluctuation patterns with historical satellite images as input. Finally, the convolutional autoencoder is used to extract cloud distribution features, and a long- and short-term memory network based on the combination of cloud distribution features and historical output is proposed to construct prediction models of the three models. Through the analysis of the simulation data, the prediction method proposed in this article has higher prediction accuracy than the traditional prediction model.

关键词： Predictive models Fluctuations Data models Photovoltaic systems Clouds Satellites Meteorology Fluctuation pattern prediction fluctuation pattern recognition long- and short-term memory network regional photovoltaic power satellite image

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Golf Swing Sequencing Using Computer Vision 10th

Golf Swing Sequencing Using Computer Vision

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Marais, Marc Brown, Dane Rhodes Univ Dept Comp Sci ZA-6140 Grahamstown South Africa

ISBN: (纸本)9783031048814;9783031048807

analysis of golf swing events is a valuable tool to aid all golfers in improving their swing. image processing and machine learning enable an automated system to perform golf swing sequencing using images. The majority of swing sequencing systems implemented involve using expensive camera equipment or a motion capture suit. An image-based swing classification system is proposed and evaluated on the GolfDB dataset. The system implements an automated golfer detector combined with traditional machine learning algorithms and a CNN to classify swing events. The best performing classifier, the LinearSVM, achieved a recall score of 88.3% on the entire GolfDB dataset when combined with the golfer detector. However, without golfer detection, the pruned VGGNet achieved a recall score of 87.9%, significantly better (>10.7%) than the traditional machine learning models. The results are promising as the proposed system outperformed a Bi-LSTM deep learning approach to achieve swing sequencing, which achieved a recall score of 76.1% on the same GolfDB dataset. Overall, the results were promising and worked towards a system that can assist all golfers in swing sequencing without expensive equipment.

关键词： Golf swing sequencing Histogram of oriented gradients Linear Discriminate analysis Support Vector Machines Gradient Boosting Machines Deep learning

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