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Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks 12

Persis: A Persian Font Recognition Pipeline Using Convolutio...

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12th International conference on Computer and Knowledge Engineering, ICCKE 2022

作者： Mohammadian, Mehrdad Maleki, Neda Olsson, Tobias Ahlgren, Fredrik Islamic Azad University Mashhad Branch Department of Computer Engineering Mashhad Iran Linnaeus University Department of Computer Science and Media Technology Kalmar Sweden

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

What happens if we see a suitable font for our design work but we do not know its name? visual Font Recognition (VFR) systems are used to identify the font typeface in an image. These systems can assist graphic designers in identifying fonts used in images. A VFR system also aids in improving the speed and accuracy of Optical Character Recognition (OCR) systems. In this paper, we proposed the first publicly available datasets in the field of Persian font recognition and employed Convolutional Neural Networks (CNN) to address the Persian font recognition problem. The results show that the proposed pipeline obtained 78.0% top-1 accuracy on our new datasets, 89.1% in the IDPL-PFOD dataset, and 94.5% in the KAFD dataset. Furthermore, the average time spent in the entire pipeline for one sample of our proposed datasets is 0.54 and 0.017 seconds for CPU and GPU, respectively. We conclude that CNN methods can be used to recognize Persian fonts without the need for additional pre-processing steps such as feature extraction, binarization, normalization, etc. © 2022 ieee.

关键词： image segmentation

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SLAM Loop Detection Algorithm Based on Improved Bag-of-Words Model 5

SLAM Loop Detection Algorithm Based on Improved Bag-of-Words...

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5th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Xi, Kun He, Jianjun Hao, Shuang Luo, Li Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Aiming at the problem that the vocabulary tree in the traditional bag-of-words model algorithm is not strong enough to represent the scene, which will affect the performance of the loop detection module in the visual SLAM system and produce accumulated errors, this paper proposes a slam loop detection algorithm based on the optimized bag-of-words model, improves the clustering algorithm for generating offline vocabulary tree, and uses the improved K-Means algorithm to build the vocabulary tree;At the same time, the image feature data of the experimental scene is used to update the vocabulary tree to improve the representation ability of the vocabulary tree. In this paper, the data sets of CityCenter, NewCollege, KITTI00 and KITTI06 are compared and analyzed experimentally. Compared with the experiments before and after updating the vocabulary tree, when the accuracy is 100%, the maximum recall rate of the algorithm on the four data sets is improved by 11.28%, 24.73%, 16.58% and 17.73% respectively;Then, compared with the five classical algorithms, the maximum recall rates of the four data sets are 0.39%, 11.27%, 2.39% and 22.67% higher than the best results of the five algorithms, respectively. The improved algorithm reduces the processing time, and the experimental results verify the correctness and effectiveness of the improved algorithm. © 2022 ieee.

关键词： image enhancement

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Research on training image recognition based on deep Convolutional neural networks: A case study of the hand-held insulated ladder recognition model 23

Research on training image recognition based on deep Convolu...

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Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Chaobing Wei Jiangmen power supply Bureau Guangdong Power Grid Corporation China

ISBN: (纸本)9798400716430

In this paper, we will introduce a series of work required for the design model of the insulation ladder identification technology, including the construction of the model training environment, the preparation of data sets, the training of the model, the visual operation of the model and the recognition result of the model. The identification model of walking insulation ladder in electric power construction will be developed based on PyTorch. We will ensure the safety of distribution network operations. In this paper, based on the existing YOLOv5 based target detection model, an improved design was carried out to enhance the accuracy of its detection of small targets, and the model was lightweight. Finally, the improved model was deployed to the edge device for hardware experiment verification.

关键词： Deep convolutional neural network image recognition Recognition model Walking insulated ladder

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image Segmentation for Improved Lossless Screen Content Compression

Image Segmentation for Improved Lossless Screen Content Comp...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shabhrish Reddy Uddehal Tilo Strutz Hannah Och André Kaup Department of Electrical Engineering and Computer Science Coburg University Coburg Germany Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen Germany

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two subimages (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.

关键词： image segmentation Adaptation models image coding Signal processing Probability distribution Entropy coding Acoustics

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FuzzyMIL: Decoupling Pathological Phenotypes through Deep Fuzzy Clustering for Efficient Whole Slide image Analysis

FuzzyMIL: Decoupling Pathological Phenotypes through Deep Fu...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Anran Liu Tong Li Jing Cai Srinivasa Sampath Veer Vajrala Department of Health Technology and Informatics Hong Kong Polytechnic University Hong Kong SAR China Division of Computational & Data Sciences Washington University in St.Louis St.Louis USA Department of Electronics and Communications Engineering Indian Institute of Information Technology Dharwad Dharwad India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In Multiple Instance Learning (MIL) for Whole Slide image (WSI) analysis, attention mechanisms are often employed to weigh the importance of different instances. However, global attention may lead to feature homogenization and overlook tissue differences. Adding local attention can capture these variations but is more parameter-intensive. To tackle the above challenges, in this work, we propose a deep fuzzy clustering framework (FuzzyMIL) based on a learnable Fuzzy C-means variant named FCM to analyze WSIs in a compact and efficient manner. By iteratively updating the fuzzy clustering centers, FCM decouples the morphological features of WSIs, resulting in more distinct and less correlated phenotypes. In this learning process, FCM compresses the feature representation space of WSI, guiding the features to gradually converge toward the representation prototypes. These prototypes, influenced by the soft assignment mechanism, take into account all updated features, enabling the model to retain both global information and local awareness. We evaluated our approach using three public datasets for diagnosis and sub-typing. Experimental results show that our approach achieves competitive performance while significantly reducing the downstream task framework parameters, striking a good balance between accuracy and model complexity. We release our code at https://***/Liuanana/FuzzyMIL.

关键词： Pathology Phenotypes image analysis Codes Attention mechanisms Prototypes Signal processing Acoustics Complexity theory Speech processing

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Stereo image Inpainting for UGVs Based on a Multistage Feature Alignment and Refinement Network 2

Stereo Image Inpainting for UGVs Based on a Multistage Featu...

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2nd International conference on Electronics, communications and Information Technology, CECIT 2021

作者： Yang, Xiaokang Li, Hengyu Liu, Jingyi Song, Tao Cui, Guohua Xie, Shaorong School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai201620 China

ISBN: (纸本)9781665437578

The binocular vision systems provide a wealth of visual information for UGVs. However, the dust, soil and water droplets in the environment can easily contaminate the camera lens, which will cause errors in the robot's visual perception system. However, the current GAN-based image inapinting methods usually generate wrong textures, which are quite different from the real scenes. To address this problem, we propose an inpainting method that uses stereo image information as prior knowledge. In this work, we first use a multistage feature alignment module to align the left and right feature maps, and use the dynamic fusion module to fuse the two features after each alignment stage. After that, we use the feature refinement module to refine the texture information of the feature maps to generate more refined details. Experiments show that our method surpasses previous work, and achieves a higher PSNR and SSIM evaluation metrics while running over 25 fps on single NVIDIA GeForce RTX 2080 Ti, which provides effective self-inpainting capabilities for the binocular vision system of UGVs to keep driving safety. © 2021 ieee.

关键词： Stereo image processing

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

ISBN: (纸本)9789819660070

The proceedings contain 29 papers. The special focus in this conference is on Intelligent Information and Database Systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning Algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for visual Communication;Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution Algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning Systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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An Efficient Layout Index Characters for Automatic License Plate Recognition System Based on the YOLO-v8 Detector

An Efficient Layout Index Characters for Automatic License P...

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ieee International conference on Signal and image processing Applications (ICSIPA)

作者： Aymen Saad Usman Ullah Sheikh Zaid Abdi Alkareem Alyasseri Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Kufa Iraq Information Technology Research and Development Center (ITRDC) University of Kufa Najaf Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Automatic License Plate Recognition (ALPR) systems have been widely used for traffic management, law enforcement, and security. Previous ALPR methods have achieved impressive performance on single-type license plates. However, multi-type license plate recognition is still challenging due to various character layouts and fonts. This study presents an efficient ALPR system based on the YOLO-V8 object detector and optical character recognition (OCR) to read the license plate (LP) content. OCR sometimes misinterprets certain English letters as Arabic numbers and certain Arabic numbers as English letters because of their visual similarities. Therefore, this study has improved the OCR to improve recognition results by including the checking character index (CCI) stage to fine-tune the detection results. The proposed ALPR uses YOLO-v8 and was evaluated using real Iraqi license plate datasets and achieved a precision and recall of $100 \%$, and $\mathbf{9 9 . 8 \%}$ respectively, with mAP confidence curve values of 99.5.

关键词： visualization image recognition Layout Lighting Symbols Detectors Character recognition Indexes Security License plate recognition

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Target detection algorithm based on improved homomorphic filter in haze days

Target detection algorithm based on improved homomorphic fil...

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2022 Global Reliability and Prognostics and Health Management conference, PHM-Yantai 2022

作者： Dong, Yanzhi Liu, Lekang Xu, Jiaxin Wan, Guangqi Yantai University School of Physics and Electronic Information Yantai China Yantai Dongfang Wisdom Electric CO. LTD Department of Hardware R&D Yantai China

ISBN: (数字)9781665496315

ISBN: (纸本)9781665496315

With the frequent occurrence of smog weather, traffic accidents occur frequently. It poses new challenges to the transportation system. Aiming at the serious fog weather, the paper proposes a YOLO-Demist algorithm based on the homomorphic filter function model which adopted the linear improvements factor em. By using the improved homomorphic filter for image enhancement and the maximum inhibition processing on photographs or videos, the results show that the average detection rate is increased from 66.01% to 83.16%, the detection rate is more stable, and the leakage and error detection rate are significantly improved compared with YOLO-V3. The algorithms run with OpenCV for target detection, and the main objects detected are cars and birds. Compared with HLE (Histogram Equalization), SSR(Single Scale Retinex), and other traditional algorithms, the results show that the algorithm can effectively detect the visual disability of road traffic objects under haze weather and reduce the frequency of traffic accidents under severe weather. © 2022 ieee.

关键词： image enhancement

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image-Based Contextual Pill Recognition with Medical Knowledge Graph Assistance 14th

Image-Based Contextual Pill Recognition with Medical Knowled...

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14th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Anh Duy Nguyen Thuy Dung Nguyen Huy Hieu Pham Thanh Hung Nguyen Phi Le Nguyen Hanoi Univ Sci & Technol Hanoi Vietnam VinUniv Coll Engn & Comp Sci Hanoi Vietnam VinUniv VinUni Illinois Smart Hlth Ctr Hanoi Vietnam

ISBN: (纸本)9789811982330;9789811982347

In many healthcare applications, identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the deep learning-based approach to tackle the pill recognition problem in the literature. However, due to the high similarity between pills' appearance, misrecognition often occurs, leaving pill recognition a challenge. To this end, in this paper, we introduce a novel approach named PIKA that leverages external knowledge to enhance pill recognition accuracy. Specifically, we address a practical scenario (which we call contextual pill recognition), aiming to identify pills in a picture of a patient's pill intake. Firstly, we propose a novel method for modeling the implicit association between pills in the presence of an external data source, in this case, prescriptions. Secondly, we present a walk-based graph embedding model that transforms from the graph space to vector space and extracts condensed relational features of the pills. Thirdly, a final framework is provided that leverages both image-based visual and graph-based relational features to accomplish the pill identification task. Within this framework, the visual representation of each pill is mapped to the graph embedding space, which is then used to execute attention over the graph representation, resulting in a semantically-rich context vector that aids in the final classification. To our knowledge, this is the first study to use external prescription data to establish associations between medicines and to classify them using this aiding information. The architecture of PIKA is lightweight and has the flexibility to incorporate into any recognition backbones. The experimental results show that by leveraging the external knowledge graph, PIKA can improve the recognition accuracy from 4.8% to 34.1% in terms of F1-score, compared to baselines.

关键词： Pill recognition Knowledge graph Graph embedding

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