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26th international conference on pattern recognition, ICPR 2022

26th International Conference on Pattern Recognition, ICPR 2...

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26th international conference on pattern recognition, ICPR 2022

ISBN: (纸本)9783031377419

The proceedings contain 49 papers. The special focus in this conference is on pattern recognition. The topics include: Towards Content-Aware Pixel-Wise Comic Panel Segmentation;data-Driven Transform Based Adaptive Compressive Sensing Framework for 3D Reconstruction of Forests;Land Cover Classification for Fires Using Sentinel-2 Satellite RGB Images and Deep Transfer Learning;landsat 8 data for forest fire monitoring: case of Mediouna forest in Tangier, Morocco;flying Wing Wildfire Tracking Using Visual Servoing and Gimbal Control;An Overview of Tools and Algorithms Used to Classify, Detect, and Monitor Forest Area Using LiDAR Data;Towards the Automation of Wildfire Monitoring with Aerial Vehicles: The FIREFRONT Project;real-Time Georeferencing of Fire Front Aerial Images Using Structure from Motion and Iterative Closest Point;forest Fires Identification Using Self-Supervised Learning;georeferencing High-Altitude Aerial Imagery;Wildfires Detection and Segmentation Using Deep CNNs and Vision Transformers;a Method to Annotate Who Speaks a Text Line in Manga and Speaker-Line Dataset for Manga109;towards Multi-class Forest Floor Analysis;combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection;towards Unconstrained Audio Splicing Detection and Localization with Neural Networks;comprint: Image Forgery Detection and Localization Using Compression Fingerprints;H4VDM: H.264 Video Device Matching;GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection;Misalignment Estimation in Non-Aligned Double JPEG Scenario Based on AC Histogram Analysis;attacking and Defending Printer Source Attribution Classifiers in the Physical Domain;descriptive Algorithmic Schemes as Image Analysis machine Programs;application of Artificial intelligence in Ophthalmology for Coagulate Map Formation to Carry Out Laser Eye Treatment;considering Meanings and Effects of Frames Without Onomatopoeias in Japanese Comics.

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Semi-supervised Lightweight Object Detection Method Based on Improved YOLOv7-tiny 25

Semi-supervised Lightweight Object Detection Method Based on...

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Proceedings of the 2025 3rd international conference on Communication Networks and machine Learning

作者： Zhilong Gao Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition Liaoning University Shenyang Liaoning China College of Information Liaoning University Shenyang Liaoning China

ISBN: (纸本)9798400713231

Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.

关键词： Object Detection

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rdMMLND: A New Robust Deep Model for Multiple License Plate Number Detection in Video 3rd

RDMMLND: A New Robust Deep Model for Multiple License Plate ...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Kumar, Amish Shivakumara, Palaiahnakote Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9783031090363

Accurate multiple license plate detection without affecting speed, occlusion, low contrast and resolution, uneven illumination effect and poor quality is an open challenge. This study presents a new Robust Deep Model for Multiple License Plate Number Detection (rdMMLND). To cope with the above-mentioned challenges, the proposed work explores YOLOv5 for detecting vehicles irrespective of type to reduce background complexity in the images. For detected vehicle regions, we propose a new combination of Wavelet Decomposition and Phase Congruency Model (WD-PCM), which enhances the license plate number region such that the license plate number detection step fixes correct bounding boxes for each vehicle of the input images. The proposed model is tested on our own dataset containing video images and standard dataset of license plate number detection to show that the proposed model is useful and effective for multiple license plate number detection. Furthermore, the proposed method is tested on natural scene text datasets to show that the proposed method can be extended to address the challenges of natural scene text detection. © 2022, Springer Nature Switzerland AG.

关键词： Wavelet decomposition

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Flatter Minima of Loss Landscapes Correspond with Strong Corruption Robustness 27th

Flatter Minima of Loss Landscapes Correspond with Strong C...

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27th international conference on pattern recognition, ICPR 2024

作者： Zhong, Liqun Zhu, Kaijie Yang, Ge School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9783031781063

To develop deep neural networks (DNNs) that maintain strong performance under different natural image corruptions such as noise and blur, namely with strong corruption robustness, is essential for real-world applications. Current methods to enhance corruption robustness focus primarily on data augmentation. But these methods are often empirically designed without a clear theoretical foundation. Here, we show that flatter minima of loss landscapes generally correspond with stronger corruption robustness. Based on this finding, we have developed a method named LASAM-DA that combines layer adaptive sharpness-aware minimization(LASAM) with data augmentation (DA) to boost corruption robustness. LASAM pushes DNNs towards flat minima of loss landscapes, while DA guides DNNs to memorize diverse data distributions. LASAM and DA reinforce each other without interference. Experiments on CIFRA-10-C, CIFAR-100-C and Tiny-ImageNet-C show that LASAM-DA outperforms competing methods and achieves state-of-the-art performance. LASAM-DA is versatile, not restricted to specific data types, network architectures or data augmentation methods. Code is openly accessible at https://***/cbmi-group/Flatter-Minima-Helps-for-Robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Distinguishing Relational pattern Languages With a Small Number of Short Strings 33

Distinguishing Relational Pattern Languages With a Small Num...

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33rd international conference on Algorithmic Learning Theory (ALT)

作者： Holte, Robert C. Mousawi, S. Mahmoud Zilles, Sandra Univ Alberta Edmonton AB Canada Amii Edmonton AB Canada Univ Regina Regina SK Canada

This paper studies the equivalence problem for relational pattern languages, where a relation imposes dependencies between the two strings with which two variables in a pattern can be replaced simultaneously. Our focus is on the question whether the non-equivalence of two relational patterns is witnessed by short strings, namely those generated by replacing variables in the patterns by strings of length bounded by some (small) number z. After establishing a close connection between this problem and the study of the notions of teaching dimension and no-clash teaching dimension, we investigate specific classes of relational pattern languages. We show that the smallest number z that serves as a bound for testing equivalence is 2 when the relation between variable substitutions is that of equal string length, and the alphabet size it at least 3. This has interesting implications on the size and form of non-clashing teaching sets for the corresponding languages. By contrast, not even z = 3 is sufficient when the constraints require two substituted strings to be the reversal of one another, for alphabets of size 2. We conclude with a negative result on erasing pattern languages.

关键词： pattern languages equivalence problem teaching dimension

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Proceedings of the 2022 3rd international conference on Intelligent Computing, Instrumentation and Control Technologies: Computational intelligence for Smart Systems, ICICICT 2022

Proceedings of the 2022 3rd International Conference on Inte...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

ISBN: (纸本)9781665410052

The proceedings contain 311 papers. The topics discussed include: exploration of performance of dynamic branch predictors used in mitigating cost of branching;a comparative study of ant colony optimization and genetic algorithm for path planning of aerial food delivery system;energy management, design constraints and solution for data dissemination in wireless sensor networks;a case study of BIM based approach for electrical installation detail drafting;design of self-tuning fuzzy Pi controller for MIMO process using an analytical approach for selecting the scaling factors;blood donor management system - an android based model and implementation;hybrid network security model for small and mid-sized enterprises;an approach for p2p based botnet detection using machine learning;a pilot investigation on the performance of auditory stimuli based on EEG signals classification for BCI applications;disease classification techniques – a survey;a reliable control system for unmanned aircraft using fault-tolerant control;a review on thermal infrared semantic distribution for nightfall drive;deep convolution neural network in classification of liver tumor as benign or malignant from abdominal computed tomography;and waste management and wealth generation through waste elimination.

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Design and Simulation of Intelligent Cross-border E-commerce Logistics System Based on Genetic Algorithm Technology of Artificial intelligence 3

Design and Simulation of Intelligent Cross-border E-commerce...

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3rd international conference on Electronics Technology and Artificial intelligence, ETAI 2024

作者： Chen, Xiaojuan Lei, Shixin Lei, Fei School of Management Guangdong University of Science & Technology Dongguan523083 China Management Consulting Division Dongguan Jiuxing Enterprise Management Consulting Co. Dongguan523000 China School of Finance and Economics Guangdong University of Science & Technology Dongguan523083 China

ISBN: (纸本)9781510683143

With the rapid development of cross-border e-commerce, the logistics system behind it faces various challenges such as complex routes, diverse transportation modes, and unpredictable customs clearance procedures. To address these challenges, this study proposes a design and simulation of an intelligent cross-border e-commerce logistics system based on genetic algorithm technology of artificial intelligence. The proposed system utilizes genetic algorithm techniques to optimize the logistics route planning, transportation mode selection, and customs clearance process. The genetic algorithm is employed to search for the optimal combination of routes, transportation modes, and customs procedures, considering various factors such as cost, time, and reliability. The system takes into account real-time data, including transportation costs, transit times, customs regulations, and historical shipment information, to make informed decisions and continuously improve the logistics efficiency. The simulation of the proposed system is conducted using historical cross-border e-commerce shipment data. The system is evaluated based on performance metrics such as transportation cost, delivery time, and customer satisfaction. The results demonstrate that the intelligent cross-border e-commerce logistics system based on genetic algorithm technology can effectively optimize the logistics process, reduce costs, and improve customer satisfaction. © 2024 SPIE.

关键词： Genetic algorithms Artificial intelligence Design intelligence systems Computer simulations Evolutionary algorithms Mathematical optimization Transportation Algorithm development Particle swarm optimization

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GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

GLSI Texture Descriptor Based on Complex Networks for Music ...

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international Joint conference on Neural Networks (IJCNN)

作者： Coca Salazar, Andres Eduardo Fed Univ Technol Parana UTFPR TD Comp Engn Coordinat COENC Toledo PR Brazil

ISBN: (纸本)9781665488679

The texture classification of an image is related to an important musical attribute, the music genre. This relationship is depicted in the visual representation of the audio signal, called as spectrogram. In this paper, we propose a new Music Genre Classification (MGC) system that processes the spectrogram texture using the Gray Level and Structural Information (GLSI) descriptor, and represents the interconnection between the descriptor codes through complex networks. The GLSI descriptor is an improvement of the CLBP (Completed Local Binary pattern) descriptor, which quantifies the texture of an image with three codes: signal (CLBP-S), magnitude (CLBP-M), and central (CLBP-C). By transforming the CLBP-C code, GLSI adds macro-structural information. The network nodes represent the descriptor codes, and the respective edges, the relationship according to the horizontal and vertical consecutive condition. We defined two representations for the nodes: 1) individual code node, obtaining the G(s), G(m) and G(g) networks, and 2) triple code node, obtaining the G(smg) network. For the experimental stage, we used the GTZAN dataset, three types of spectrograms: conventional, mel-spectrogram and gammatonegram;and mining with network topological measures. For each type of spectrogram, we performed three experiments according to feature vector combinations, such as measures of: 1) G(s), G(m) and G(g), 2) G(smg), and 3) all networks. In the machine learning stage, we used the ensemble classifier Bagging with Random Forest, and 10fold cross-validation repeated 100 times. The experiment using all measures and all spectrograms revealed a satisfactory result, indicating that the MGC proposed is promising. We also propose a new equation to calculate the GLSI code, which proved to be much faster and with more intuitive encoding.

关键词： Digital image processing computer vision applications pattern recognition performance evaluation

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Research on Automatic Guided Vehicle Scheduling Strategies based on Artificial Intelligent 3

Research on Automatic Guided Vehicle Scheduling Strategies b...

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3rd international conference on Control and Intelligent Robotics, ICCIR 2023

作者： Wang, Chenke School of Intelligence science and technology University of Science and Technology Beijing China

ISBN: (纸本)9781510671867

Automatic guided vehicles have widely utilized in the transportation works and assist to move the objects into certain areas. Therefore, avoiding the conflict of these vehicles and determine the effective scheduling strategies are an essential component to improve the effectiveness and guarantee the safe of these vehicles. Existing scheduling strategies are concentrated to utilize the mathematical models or train a machine learning algorithm to dispose the scheduling for each vehicles. In this work, we utilize the artificial neural network to solve the scheduling issue and achieve the high effectiveness of moving vehicles. Initially, we order the vehicles with the principle of priority and get the minimum Euclid distance for these vehicles. Subsequently, an artificial neural network is trained to obtain the scheduling routes for each vehicles. From our extensive simulation results and comparison analysis, we can conclude our proposed model can effectively achieve the scheduling routes for each vehicles and avoid the conflict or congestion among guided vehicles with reasonable computation and response time. © 2023 SPIE.

关键词： Automatic guided vehicles

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A Systematic Survey of Human Ear Biometrics recognition by using 2D images

A Systematic Survey of Human Ear Biometrics Recognition by u...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Howayda Abedallah Elmarzaki Bothaina F. Gargoum Amina A Abdo Department of Software Engineering University of Benghazi Benghazi Libya Department of Computer Science University of Benghazi Benghazi Libya Department of Artificial Intelligence Collage of Computer Technology Benghazi Libya

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research.

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