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Toward Data-Driven Glare Classification and Prediction for Marine Megafauna Survey 1

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26th international conference on pattern recognition, ICPR 2022

作者： Power, Joshua Jacoby, Derek Drouin, Marc-Antoine Durand, Guillaume Coady, Yvonne Meng, Julian University of New Brunswick FrederictonNBE3B 5A3 Canada University of Victoria VictoriaBCV8P 5C2 Canada National Research Council Canada OttawaONK1A 0R6 Canada

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. this paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for glare-free data collection. To build this model, we leverage a small labelled dataset to perform semi-supervised learning. the large dataset is labelled with a Cascading Random Forest Model using a naïve pseudo-labelling approach. A reflectance model is used, which pinpoints features of interest, to populate our datasets which allows for context-aware machine learning models. the pseudo-labelled dataset is used on two models: a Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay the foundation for data-driven mission planning;a glare modelling system which allows surveyors to preemptively minimize glare and reduces survey reliance on the detection function as an estimator of whale populations during periods of poor subsurface visibility. © 2023, Springer Nature Switzerland AG.

关键词： Large dataset

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Transformer Partial Discharge recognition Based on Phase-Preserving Data Augmentation

Transformer Partial Discharge Recognition Based on Phase-Pre...

引用

Energy, Power and Grid (ICEPG), international conference on

作者： Shifang He Mingtao Yu Zongzhen Yang Binghui Lei Department of China Yangtze Power Co.Ltd. Yichang Xiling Road 1 Hubei China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

the identification of transformer Partial Discharge (PD) is crucial for the safe and stable operation of modern power systems. Traditional PD identification methods rely on manual feature extraction, which has limited generalization capability. Moreover, when applying deep learning algorithms to Phase-Resolved Partial Discharge (PRPD) patterns, conventional data augmentation techniques may disrupt the phase distribution, leading to the loss of critical features and affecting identification accuracy. To address this issue, this paper proposes a PD identification method based on phase-preserving data augmentation techniques. By incorporating Gaussian noise addition, phase shift processing, and segmented perturbation processing, the proposed method effectively retains the phase information in PRPD patterns, avoiding the loss of critical features during data augmentation. Experimental results show that the proposed method significantly outperforms traditional methods in terms of accuracy and stability, achieving a maximum accuracy of 96.39%, demonstrating great potential for practical engineering applications.

关键词： Partial discharges Accuracy Perturbation methods Gaussian noise Power system stability Data augmentation Data models Stability analysis Robustness pattern recognition

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Lace Fabric Image Retrieval Using Siamese Neural Network 6

Lace Fabric Image Retrieval Using Siamese Neural Network

引用

6th international conference on Signal and Image Processing, ICSIP 2021

作者： Xu, Dongdong Li, Yueyang Luo, Haichi Jiangnan University Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi China Jiangnan University College of Internet of Things Engineering Wuxi China

ISBN: (纸本)9780738133737

An efficient lace fabric image retrieval method based on DCNN learning features is proposed in this paper. Fine-tuning with Siamese Neural Network is used to learn effective feature of lace fabric image. During the process of training the Siamese Neural Network, hard negative pairs are selective to achieve fast convergence and good performance. the DCNN learning features are combined with the unique shape feature to enable accurate and efficient retrieval of massive image data. Experimental results demonstrate the effectiveness of retrieval performance of the proposed algorithm and possible practical application of the retrieval system in lace fabric industry to improve management efficiency. © 2021 IEEE.

关键词： Content based retrieval

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MS-Net: A CNN Architecture for Agriculture pattern Segmentation in Aerial Images 6th

MS-Net: A CNN Architecture for Agriculture Pattern Segmenta...

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6th international conference on Computer Vision and Image Processing, CVIP 2021

作者： Bhagat, Sandesh Kokare, Manesh Haswani, Vineet Hambarde, Praful Kamble, Ravi SGGS Institute of Engineering and Technology Nanded431606 India Indian Institute of Technology Ropar140001 India

ISBN: (纸本)9783031113451

Computer vision for crop sciences is overgrowing with the advancement in pattern recognition and deep learning. Agriculture pattern segmentation is an important application such as segmentation of cloud shadow, waterway, standing water, weed cluster, planter skip, double plant, etc. However, the segmentation of agriculture patterns is challenging due to multi-scale variations of patterns and considerable overlap between classes. Furthermore, size and shape variation, unclear boundaries, and missing edges make this task more complex. To address this problem, we proposed the encoder-decoder architecture with input as a multi-scale image pyramid to improve the multi-scale feature extraction of agriculture patterns. EfficientNetB7 is utilized as an encoder for efficient feature extraction. In addition, the proposed Lateral-output layer shows rich semantic features by aggregating low level and high level features from the decoder, shows improvement in dice score. the proposed research uses the Agriculture Vision dataset with aerial farmland images. the proposed method outperformed different state-of-the-art methods in the literature for six agricultural patterns segmentation with a mean dice score of 74.78, 68.11, and 84.23 for RGB, NIR, and RGB + NIR images, respectively. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Multilingual Text & Handwritten Digit recognition and Conversion of Regional languages into Universal Language Using Neural Networks 6

Multilingual Text & Handwritten Digit Recognition and Conver...

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6th international conference for Convergence in Technology (I2CT)

作者： Vidhale, Bhushan Khekare, Ganesh Dhule, Chetan Chandankhede, Pankaj Titarmare, Abhijit Tayade, Meenal GH Raisoni Coll Engn Dept Elect & Telecommun Engg Nagpur Maharashtra India GH Raisoni Coll Engn Dept Informat & Technol Nagpur Maharashtra India

ISBN: (纸本)9781728188768

the Character recognition techniques equate an illustrative identity with the image of character. Handwritten human character recognition is a machine's ability to obtain and recognize handwritten information from various sources such as papers, photos, tactile touch devices etc. recognition of handwriting and computer characters is an evolving field of study and has broad uses in banks, offices and industries. the key objective of this research work is to develop a knowledgeable framework for "Handwritten Character recognition (HCR) victimization Neural Network" which might effectively acknowledge selected type-format character victimization as the substitute Neural Network approach. Neural method is the best method for controlling images, thus style parts square measure less all around plot as compared to various designs. Neural computers do parallel results. Neural computers square measure run during a manner that's utterly different from traditional operation. Neural computers square measure conditioned (not programmed) in such a way, that how it's given in an explicit beginning state (data input);they either assign the information (input file or computer file) into one amongst the quantity of categories or permit the initial data to evolve to maximize an explicit fascinating property. In this research work, a purely handwritten digit recognition using machine learning model as well as character recognition matlab model is used. A translator using MATLAB to beat the barrier of various languages is designed. the projected style is also used for English, Marathi and Guajarati text to speech conversion into English language. Input is taken in English, Marathi and Gujrati text manually to the interface or image of written text or handwritten text and output can be translated in English Language by facilitating use of Optical Character recognition (OCR) technique. the projected methodology is also used to produce help to folks that lack the ability of speech or non-n

关键词： Off-line Handwritten recognition Handwritten Character pattern recognition Feature Extraction Neural Network

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Doubly Regularized Least Squares Twin Extreme Learning Machine for pattern Classification 23

Doubly Regularized Least Squares Twin Extreme Learning Machi...

引用

Proceedings of the 2023 6th international conference on Artificial Intelligence and pattern recognition

作者： Qing Wu Ming Yu Lu Chen School of Automation Xi' an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Least squares twin extreme learning machine (LSTELM) possesses better generalization ability than the standard extreme learning machine (ELM) by finding two nonparallel hyperplanes in ELM random feature space. However, LSTELM is not sparse in nature and does not take into account the intrinsic geometric information of the data. To overcome the above, this paper proposes one classification method based on LSTELM, namely doubly regularized least squares twin extreme learning machine (DRLSTELM). DRLSTELM uses L2,1-norm regularization and manifold regularization methods to improve the classification performance. Furthermore, in its nonlinear case, a hybrid kernel function is introduced to improve the generalization and learning ability of the model. To evaluate the classification performance of the proposed algorithm, experiments were conducted on the NDC datasets and the benchmark datasets respectively. the results show that the classification effect of DRLSTELM is significantly better than other state-of-the-art methods.

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Improved Time Position Estimation of Manet UAV Routing Protocol 6

Improved Time Position Estimation of Manet UAV Routing Proto...

引用

6th international conference on Signal and Image Processing, ICSIP 2021

作者： Mbyamm Kiki, Mesmin J. Iddi, Ibrahim Yunusa, Haruna Beihang University School of Electronic and Information Engineering Beijing China Beihang University School of Pattern Recognition and Intelligent Systems Beijing China

ISBN: (纸本)9780738133737

With routing protocols that are primarily concerned with location estimates, the most significant problem is that new neighbors are acquired slowly, which results in inefficient routing decisions. Because routing and forwarding would fail if the next-hop unmanned aerial vehicle (UAV) was no longer in sight. In this paper a system that makes use of unscented Kalman filter prediction in response to improve the next-hop position in real time in order to reduce the overall performance of route forwarding is proposed. It is being introduced to the classic GPRS protocol in order to facilitate the development of new routing protocols with OPNET. the new routing protocol demonstrates a greater rise in point-to-point packet delay and forwarding rate when compared to the original routing system. © 2021 IEEE.

关键词： Kalman filters

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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

引用

6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. the algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. the experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks Detection algorithms Cervical cancer

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survAIval: Survival Analysis with the Eyes of AI 1

引用

5th international conference on CHIRA 2021 and 6th international conference on CHIRA 2022

作者： Kowol, Kamil Bracke, Stefan Gottschalk, Hanno School of Mathematics and Natural Sciences IZMD University of Wuppertal Gaußstraße 20 Wuppertal Germany Chair of Reliability Engineering and Risk Analytics IZMD University of Wuppertal Gaußstraße 20 Wuppertal Germany Institute of Mathematics Technical University Berlin Straße des 17. Juni 135 Berlin Germany

ISBN: (数字)9783031419621

ISBN: (纸本)9783031419614

In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-critical corner cases in a short period of time, as already presented in [12]. Our results show that incorporating these corner cases during training improves the recognition of corner cases during testing, even though, they were recorded due to visual impairment. Using the corner case triggering pipeline developed in the previous work, we investigate the effectiveness of using expert models to overcome the domain gap due to different weather conditions and times of day, compared to a universal model from a development perspective. Our study reveals that expert models can provide significant benefits in terms of performance and efficiency, and can reduce the time and effort required for model training. Our results contribute to the progress of automated driving, providing a pathway for safer and more reliable autonomous vehicles on the road in the future. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： bioinformatics

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Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge 25th

Reference Algorithms for the Mitosis Domain Generalization...

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25th international conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2022

作者： Ammeling, Jonas Wilm, Frauke Ganz, Jonathan Breininger, Katharina Aubreville, Marc Technische Hochschule Ingolstadt Ingolstadt Germany Pattern Recognition Lab Computer Sciences Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783031336577

Robust mitosis detection on images from different tumor types, pathology labs, and species is a challenging task that was addressed in the MICCAI Mitosis Domain Generalization (MIDOG) 2022 challenge. In this work, we describe three reference algorithms that were provided as a baseline for the challenge: A Mask-RCNN-based instance segmentation model trained on the MIDOG 2022 dataset, and two different versions of the domain-adversarial RetinaNet which already served as the baseline for MIDOG 2021 challenge, one trained on the MIDOG 2022 dataset and the other trained only on human breast carcinoma from MIDOG 2021. the domain-adversarial RetinaNet trained on the MIDOG 2022 dataset had the highest F 1 score of 0.7135 on the final test set. When trained on breast carcinoma only, the same network had a much lower F 1 score of 0.4719, indicating a significant domain shift between mitotic figure and tissue representation in different tumor types. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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