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HOG Feature-Based Offline Handwritten Malayalam Word Clustering with Lexicon Reduction 3rd

HOG Feature-Based Offline Handwritten Malayalam Word Cluster...

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3rd International conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2022

作者： Anju, A.T. Chacko, Binu P. Mohamed Basheer, K.P. Sullamussalam Science College University of Calicut Kerala Areekode India Prajyoti Niketan College University of Calicut Kerala Pudukad India

ISBN: (纸本)9789819926015

Handwriting recognition is an active research in the field of pattern recognition, image processing and computer vision. The handwriting recognition system can recognize handwritten words extracted from the scanned document image. For a better recognition model, the most inevitable thing is a good dataset for training purpose. Here, the clustering is performed on the segmented handwritten words to group the matching words for easing the process of labelled dataset creation. In this, after doing some pre-processing operations, as a feature descriptor, histogram of oriented gradient features is extracted holistically on the word image. For feature reduction, Principal Component analysis is applied on the extracted feature set and finally K-means clustering used for grouping the matched handwritten words together from the unlabelled dataset. Here, training the recognition model for keyword searching, the clustering helps to create a lexicon reduced dataset and the non-repeated segmented words are neglected after clustering. This will make the most complicated and time-consuming manual task, grouping and labelling the handwritten words for dataset creation easier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Principal component analysis

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3rd International conference on Innovations in Computing Research, ICR 2024

3rd International Conference on Innovations in Computing Res...

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3rd International conference on Innovations in Computing Research, ICR 2024

ISBN: (纸本)9783031655210

The proceedings contain 67 papers. The special focus in this conference is on Innovations in Computing Research. The topics include: Exploratory analysis of Gamblers’ Financial Transactions to Mine Behavioral pattern Data;The Detection of Misstated Financial Reports Using XBRL Mining and Intelligible MLP;university Student Enrollment Prediction: A Machine Learning Framework;early Prediction of Sepsis Utilizing Multi-branches Multi-tasks Hybrid Deep Learning Model;comprehensive analysis of Iris Dataset Using K-Mean and Fuzzy K-Mean Clustering Algorithm;An Efficient and Reliable scRNA-seq Data Imputation Method Using Variational Autoencoders;Prediction of Automotive Vehicles Engine Health Using MLP and LR;medical image Character recognition Using Attention-Based Siamese Networks for Visually Similar Characters with Low Resolution;toward Smart Bicycle Safety: Leveraging Machine Learning Models and Optimal Lighting Solutions;vThrot: Fine-Grained, Virtual I/O Resource Redistribution Scheme;Bayesian Optimization-Based CNN Model for Blood Glucose Estimation Using Photoplethysmography Signals;comparing Convolutional Neural Networks and Transformers in a Points-of-Interest Experiment;Gender and Age Extraction from Audio Signal Using Convolutional Neural Network, MFCC and Spectrogram;The Hybrid Model Combination of Deep Learning Techniques, CNN-LSTM, BERT, Feature Selection, and Stop Words to Prevent Fake News;comparative analysis of Decision Tree Algorithms Using Gini and Entropy Criteria on the Forest Covertypes Dataset;A Comparative analysis of Random Forest and Support Vector Machine Techniques on the UNSW-NB15 Dataset;a Comparative Study of Speed Measurement Using Radar Guns and Pneumatic Counter;Comparative analysis of Preprocessing Techniques for KNN Classification on the Diabetes Dataset;code Smells for Assessing and Improving Students’ Coding Skills and Practices;analysis of eSIM/iSIM for Critical Communications.

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ChromEnhance-X: A Multi-Modal Deep Learning Framework for Chromosome image Enhancement 3

ChromEnhance-X: A Multi-Modal Deep Learning Framework for Ch...

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3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Dinu, A.G. Biju, V.G. Vinod, B.R. Chellapan, Nonam Raj, Smitha G. College of Engineering Trivandrum Apj Abdul Kalam Technological University India College of Engineering Dept of Ece Kerala Munnar India Govt Medical College Hospital Dept of Oncology Kerala Trissur India Govt T D M Hospital Dept of Anatomy Kerala Alappuzha India

ISBN: (纸本)9798331518592

Chromosome image analysis is crucial in cytogenetics, particularly for diagnosing genetic disorders and conducting cancer research. However, the low quality of these images often hampers accurate analysis. To address this, we introduce ChromEnhance-X, an advanced multi-modal deep learning framework that enhances chromosome image quality while preserving essential biological details. ChromEnhance-X integrates a conditional Generative Adversarial Network (cGAN) with a U-Net generator, a PatchGAN discriminator, a modified DnCNN for denoising, a self-attention module, and a learnable contrast enhancement network. This architecture tackles noise reduction, feature preservation, and contrast enhancement simultaneously. Our framework is designed with chromosome-specific considerations in mind, utilizing custom loss functions for band pattern preservation, karyotype consistency, and centromere detection accuracy. This ensures that the enhanced images retain critical biological features vital for precise cytogenetic analysis. We trained ChromEnhance-X on two datasets: the Cell image Library (CIL) with 5,000 metaphase cell images, and the Bio image Chromosome Classification Dataset (BICCD) with 42 chromosome objects. The results show significant improvements in image quality, with PSNR increasing by 25.3%, SSIM improving from 0.72 to 0.89, and FSIM rising from 0.81 to 0.93. Chromosome-specific metrics also improved markedly. Moreover, ChromEnhance-X is computationally efficient, processing images in under 50ms, making it ideal for real-time applications on resource-limited systems. Its robust performance across diverse datasets highlights its potential for broad applications in cytogenetics and biomedical imaging. © 2024 IEEE.

关键词： image quality

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Detecting pattern irregularities in astronomical images using deep learning 3

Detecting pattern irregularities in astronomical images usin...

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3rd International conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shankar, T. Deekshitha, Bada Rohini, Vempalli Nakarajan, Venkataraman Muthiah Sivasankar, P. Murali, J. Vellore Institute of Technology School of Electronics Engineering Vellore India NITTTR Department of ECE Chennai India

ISBN: (纸本)9798350372120

Classification of astronomical images is a field of long standing interest. With modern technology, we have been able to obtain many techniques and consequently generated huge amounts and type of data. This amount of new and variety in astronomical data along with the complexities presents itself as a affair that a human analysis is not possible. In our project, we use deep learning techniques to explore the various challenges faced by astronomers when processing huge amounts of data, looking for information relevant to each target. We have implemented existing methods like InceptionV3 and CNN to detect and classify galaxies. Then we proposed to use VGG16, VGG19, and MobileNetV2 for classification and image segmentation to detect and classify galaxies. And we compared the performance of existing and proposed methods. Our proposal was approximately 20% more accurate than existing methods CNN and InceptionV3. © 2024 IEEE.

关键词： Galaxies

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Object detection algorithm based on mixed dilated convolution pyramid 3

Object detection algorithm based on mixed dilated convolutio...

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

作者： Yu, Tingjian Yuan, Zemin Huang, Tao Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510667563

To address the problem that FPN in the one-stage object detection algorithm loses a large amount of semantic information after upsampling and downsampling fusion, leading to difficulties in detecting small objects in dense scenes. This paper proposes a new FPN structure, Residual dilated FPN(rdFPN), compared to the traditional FPN, which uses multiple Dilated Block instead of the standard convolution in the FPN horizontal structure to mix the receptive fields of different layers in the feature map and expand the object size range of the feature map, while improving the network's ability to extract features at different scales. Experimental results show that rdFPN improves the mAP of the one-stage object detection algorithms RetinaNet, FCOS, and GFL by 0.9%, 0.7%, and 0.8% on the MS COCO dataset, enough to improve the overall performance of the detector. © 2023 SPIE.

关键词： Object detection

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Facial Emotion recognition for Sentiment analysis of Social Media Data 10th

Facial Emotion Recognition for Sentiment Analysis of Social ...

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

作者： de Paula, Diandre Alexandre, Luis A. Univ Beira Interior Dept Informat Covilha Portugal Univ Beira Interior NOVA LINCS Covilha Portugal

ISBN: (纸本)9783031048814;9783031048807

Despite the diversity of work done in the area of image sentiment analysis, it is still a challenging task. Several factors contribute to the difficulty, like socio-cultural issues, the difficulty in finding reliable and properly labeled data to be used, as well as problems faced during classification (e.g. the presence of irony) that affect the accuracy of the developed models. In order to overcome these problems, a multitasking model was developed, which considers the entire image information, information from the salient areas in the images, and the facial expressions of faces contained in the images, together with textual information, so that each component complements the others during classification. The experiments showed that the use of the proposed model can improve the image sentiment classification, surpassing the results of several recent social media emotion recognition methods.

关键词： image sentiment analysis Multimodal Facial expression recognition Salient areas Text sentiment analysis

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Integrating VGG16 and LBP for Accurate E-Waste Classification Towards Environmental Sustainability 3

Integrating VGG16 and LBP for Accurate E-Waste Classificatio...

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3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Krishna, N. Hari Kumaraswamy, I. Bose, N. Subhash Chandra Sudhakar, P. Reddy, T. Ravi Sankar Krishnan, G. Hari Government Polytechnic Dept. of Eee Andhra Pradesh Kurnool India School of Engineering Mohan Babu University Dept. of Eee Andhra Pradesh Tirupati India Government Polytechnic Dept. of Eee Andhra Pradesh Rayachoti India

ISBN: (纸本)9798331509675

The growing challenge of electronic waste (e-waste) management necessitates advanced solutions for automated classification and recycling. This study presents a hybrid methodology combining transfer learning with VGG16 and handcrafted Local Binary patterns (LBP) features, enhanced by Principal Component analysis (PCA) for dimensionality reduction, and classification using a Random Forest Classifier. The methodology achieves a remarkable accuracy of 98.54% on the E-Waste image Dataset, outperforming existing approaches. Extensive evaluation across multiple metrics, including precision, recall, F1 score, and Cohen's Kappa, demonstrates the robustness of the approach. This research offers an efficient framework for sustainable e-waste classification, supporting environmental conservation efforts. © 2025 IEEE.

关键词： Local binary pattern

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Automated analysis of Connections in Model Diagrams 3rd

Automated Analysis of Connections in Model Diagrams

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The 3rd International conference on Frontiers in Computing and Systems (COMSYS 2022)

作者： Erudiyanathan, Sandeep Kumar Manjunath, Chikkamath Atul, Gohad BGSW/ETI1 Bosch Global Software Technologies Pvt. Ltd. Koramangala Bengaluru560095 India

ISBN: (纸本)9789819926794

A model diagram is a crucial aspect of various scientific documents that are available as hard or soft copies. Often, the information captured in these diagrams is complex and needs deeper comprehension by human experts. Automating this process of reading and analyzing has become extremely important for various industrial applications. Hence, in this paper, we aim to extract the context from model diagrams by solving for connections and converting them into a graph using sophisticated image processing and pattern recognition algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Research on ancient pottery image restoration and feature enhancement techniques based on chunked PCA and SMACC 24

Research on ancient pottery image restoration and feature en...

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3rd International conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Tang, Bin Luo, Xiling Fan, Wenqi He, Yulong Zhao, Mingfu Chongqing university of technology Chongqing400054 China Chongqing Key Laboratory of Fiber Optic Sensing and Photoelectric Detection Chongqing400054 China Key Scientific Research Base of Pest and Mold Control of Heritage Collection State Administration of Cultural Heritage Chongqing China Three Gorges Museum Chongqing400060 China

ISBN: (纸本)9798400716942

Colored pottery has deep historical and cultural significance and value. As the pottery was buried in the tomb for a long time, it made the surface of the sample pottery attached with a lot of soil and dust, and even peeled off in some areas. In short, the pattern information on the surface of the sample pottery becomes very vague. Therefore, a method of virtual image restoration and linear feature enhancement combining the mutual fusion of chunked principal component analysis (PCA) and Sequential maximum angle convex cone analysis (SMACC) is proposed. Firstly the acquired hyperspectral images are preprocessed for easier processing and analysis, then for the preprocessed images we use Support Vector Machine (SVM) to obtain the labeling data as well as the extraction of the edges. After that the abundance map of the lines is obtained using Sequential maximum angle convex cone analysis. Finally, the obtained first principal component image and the obtained abundance map are fused to obtain the feature fusion image. In this paper, the Han Dynasty pottery lids unearthed in the Three Gorges archaeology are taken as the research object to be studied. Based on the above research, the virtual restoration results of the cloud pattern area are obtained. The research results can be used as a reference basis for ancient pottery color visual painting restoration, which is highly practical. © 2024 ACM.

关键词： image reconstruction

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Object Size Detection Based on image Processing 3

Object Size Detection Based on Image Processing

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

作者： Zhao, Mingrui Shi, Chunjing School of Mechanical Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510667563

This paper proposes a novel method for detecting object sizes in images. Traditional methods have limitations due to image quality, which affects detection accuracy. To address this problem, a new approach is proposed that combines Zernike moments and deep learning models. This approach combines the strengths of both methods and overcomes the limitations of traditional approaches. The proposed approach improves the accuracy of object size detection and is robust to image noise and distortion. Experimental results demonstrate the effectiveness of this approach, which outperforms existing methods in terms of accuracy and robustness. © 2023 SPIE.

关键词： Deep learning

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