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Third International conference on Computer Vision and pattern analysis, ICCPA 2023

Third International Conference on Computer Vision and Patter...

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

ISBN: (纸本)9781510667563

The proceedings contain 140 papers. The topics discussed include: digital multi-scale visual planning model of spatial-geographical landscape pattern of smart parks;visual question answering model based on fusing global-local feature;image processing of the special sensor microwave/imager based on passive microwave remote sensing;image processing of the special sensor microwave/imager based on passive microwave remote sensing;graptolite image classification based on feature transfer and mixup data enhancement;an image classification method based on few-shot learning;fine-grained image recognition based on multi-branch and multi-scale learning;research on road extraction model of remote sensing image based on the fused convolutional module and attention mechanism;unsupervised aircraft detection in SAR images with image-level domain adaption from optical images;the role of echocardiography segmentation evaluation metrics in clinical diagnosis;and machine vision-based measurement of air compressor crankshaft journal dimensions.

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Third International conference on image Processing and Intelligent Control, IPIC 2023

Third International Conference on Image Processing and Intel...

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3rd International conference on image Processing and Intelligent Control, IPIC 2023

ISBN: (纸本)9781510668140

The proceedings contain 54 papers. The topics discussed include: image recognition and position technology based on super-pixel fuzzy C-means clustering in industrial assembly systems;a method of extracting outer eye corners of terra cotta warriors based on point cloud data;research on UI design and optimization of digital media with artificial intelligence;video description method with fusion of instance-aware temporal features;a multi-modal information fusion-based method for repairing cracks in train hooks;research on optical detection technology for underwater archaeology;discharging state recognition method of intelligent ring network cabinet based on audio signal spectrum analysis;numerical analysis and calculation of urban landscape spatial pattern;and sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image.

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

11th Iberian Conference on Pattern Recognition and Image Ana...

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

ISBN: (纸本)9783031366154

The proceedings contain 56 papers. The special focus in this conference is on pattern recognition and image analysis. The topics include: Clustering ECG Time Series for the Quantification of Physiological Reactions to Emotional Stimuli;detecting Loose Wheel Bolts of a Vehicle Using Accelerometers in the Chassis;study and Automatic Translation of Toki Pona;multi-view Infant Cry Classification;fishing Gear Classification from Vessel Trajectories and Velocity Profiles: Database and Benchmark;a Fuzzy Logic Inference System for Display Characterization;enhancing Transferability of Adversarial Audio in Speaker recognition Systems;Automated Orientation Detection of 3D Head Reconstructions from sMRI Using Multiview Orthographic Projections: An image Classification-Based Approach;microgliaJ: An Automatic Tool for Microglial Cell Detection and Segmentation;a Deep Approach for Volumetric Tractography Segmentation;synthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning;An Ensemble-Based Phenotype Classifier to Diagnose Crohn’s Disease from 16s rRNA Gene Sequences;Few-Shot image Classification for Automatic COVID-19 Diagnosis;deep Neural Networks to Distinguish Between Crohn’s Disease and Ulcerative Colitis;automatic Eye-Tracking-Assisted Chest Radiography Pathology Screening;Inter vs. Intra Domain Study of COVID Chest X-Ray Classification with Imbalanced Datasets;DARTS with Degeneracy Correction;object Detection for Rescue Operations by High-Altitude Infrared Thermal Imaging Collected by Unmanned Aerial Vehicles;identifying Thermokarst Lakes Using Discrete Wavelet Transform–Based Deep Learning Framework;real-Time Unsupervised Object Localization on the Edge for Airport Video Surveillance;object Localization with Multiplanar Fiducial Markers: Accurate Pose Estimation;hierarchical Line Extremity Segmentation U-Net for the SoccerNet 2022 Calibration Challenge - Pitch Localization;Py4MER: A CTC-Based Mathematical Expression recognition System;lightweig

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Myocardial Infarction Diagnosis: pattern analysis of ECG Report images Using Machine Learning Techniques 3

Myocardial Infarction Diagnosis: Pattern Analysis of ECG Rep...

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3rd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Raghukumar, B.S. Naveen, B. Adichunchnagiri University Electronics And Communication Engineering Bgsit BG Nagara Mandya India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

The ECG machine data is utilized to diagnose cardiac conditions, specifically focusing on identifying myocardial infarction rates by analyzing pattern variations within ECG report images. Variations in the output of electrodes 2 and 3 are noted as indicative of a heart attack. The authors employ various image processing techniques like thresholding, contrast enhancement and machine learning methods like SVM, GBC, k-neighbors to process these patterns, aiming to enhance accuracy. After extracting four features, the most effective classifiers are employed, with the Gradient Boosting Classifier (GBC) with set of features exhibiting the highest accuracy at 76.60%. This paper emphasizes effective preprocessing methods crucial for obtaining structured and refined data, facilitating better feature selection and extraction from ECG graph images. It underscores the extraction of distinctive features to aid in myocardial infarction rate prediction. The paper evaluates several machines learning classifiers, highlighting their efficiency in simplifying and expediting the diagnosis process. Furthermore, the research suggests that incorporating additional features could potentially improve accuracy. © 2024 IEEE.

关键词： Electrocardiograms

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Fusion of LBP and Median LBP for Dominant Region Based Multimodal recognition Using Imperfect Face and Gait Cues 3rd

Fusion of LBP and Median LBP for Dominant Region Based Multi...

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3rd International conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Annbuselvi, K. Santhi, N. Sivakumar, S. V.V.Vanniaperumal College for Women Madurai Kamaraj University Virudhunagar Tamilnadu Madurai India Cardamom Planters’ Association College Madurai Kamaraj University Bodinayakanur Tamilnadu Madurai India

ISBN: (纸本)9789819966899

The paper provides a novel approach to recognize imperfect face and gait cues by using only the dominant region of such cues which contain more information than that possessed by the other regions. To enhance features in such dominant region, a fusion of Local Binary pattern (LBP) and Median Local Binary pattern (Median LBP) procedure is proposed. Initially, the given imperfect face and gait probe images are divided into six overlapped half regions. After partition, the dominant overlapped half regions of face and gait are selected by using content based image retrieval process. Subsequently, the features of dominant overlapped imperfect face and gait regions are enhanced by fusing the feature vectors obtained by using fusion of LBP and Median LBP methods. Next the Eigen feature vectors followed by Fisher’s vectors are constructed using Principal Component analysis (PCA) followed by Linear Discriminant analysis (LDA) dimensionality reduction algorithms. The decisions from face and gait biometric systems are obtained separately by using Euclidean distance measure. Finally, the decisions of face and gait classifiers are fused at decision level for recognition using AND/OR rule. The method is verified on freely available ORL face and CASIA B gait datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Local binary pattern

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An Ensemble Approach for Facial Behavior analysis in-the-wild Video

An Ensemble Approach for Facial Behavior Analysis in-the-wil...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper introduces the 3rd Affective Behavior analysis in-the-wild (ABAW3) 2022 challenge. We focused on solving the problem of the Valence-Arousal (VA) estimation and Action Unit (AU) detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features;the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with local attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model. The result achieved 0.450 for valence and 0.445 for arousal on the test set, outperforming the baseline method with a corresponding CCC of 0.180 for valence and 0.170 for arousal. We also performed additional experiments on the action unit task with simple transformer blocks. We achieved a score of 49.04 on the test set in terms of F-1 score, which outperforms the baseline method with a corresponding F1 score of 36.50. Our submission to ABAW3 2022 ranks 3rd for both tasks.

关键词： Human computer interaction Sentiment analysis Gold Estimation Logic gates Feature extraction Transformers

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Handwritten Digit recognition for Two Digits Using CNN & image Processing 3

Handwritten Digit Recognition for Two Digits Using CNN & Ima...

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3rd IEEE Global conference for Advancement in Technology, GCAT 2022

作者： Mantri, Rushikesh Meshram, Dikshant Kawale, Akshay Jadhav, Dipti Ramrao Adik Institute of Technology D y Patil Deemed to Be University Department of Information Technology Navi Mumbai India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

The hand-written recognition of digits is a maj or step in many document processing and analysis applications using digital image processing technology. image processing and Machine Learning are fast-growing domain with growing applications in engineering and computer science. Pre-processing and analysis of documents are becoming more popular in many pattern recognition applications. The aim of this paper is to propose a system which will take image as an input and will automatically detect and display the digit present in it. This will save the time and efforts and will reduce the chances of involvement of human error in the system. This system can be further used in many applications like reading postal addresses, bank check amount, forms etc. In the proposed system input image with handwritten digit will be taken as input and after pre-processing it the image will be given to the model for further prediction. The output will be saved in a text file for future use. © 2022 IEEE.

关键词： Convolutional neural networks

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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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