Computer vision and image analysis for industry 4.0. (2022)

• Record Type: Book
• Further Information: Note: Edited by Nazmul Siddique, Mohammad Shamsul Arefin, Md Atiqur Rahman Ahad, M. Ali Akber Dewan.
• Editors: Siddique, Nazmul; Arefin, Mohammad Shamsul; Ahad, Md. Atiqur Rahman; Dewan, M. Ali Akber
• Contents: 1. BN-HTRD: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Seg-mentation Md. Ataur Rahman, Nazifa Tabassum, Mitu Paul, Riya Pal and Mohammad Khairul Islam INTRODUCTION RELATED WORK DATA ANNOTATION Data Collection and the Source Data Distribution Annotation Guidelines Annotation Scheme and Agreement Data Correction LINE SEGMENTATION: METHODOLOGY Thresholding and Edge Detection Morphological Operation and Noise Removal Hough Line Detection 9 1.4.4 Hough Circle Removal Bounding Box OPTICS Clustering Line Extraction and Cropping RESULTS AND EVALUATION Evaluation Metrics Line Segmentation Results CONCLUSION AND FUTURE WORK 2. A New Approach Using Convolutional Neural Network for Crops and Weeds Classification Nawmee Razia Rahman and Md. Nazrul Islam Mondal INTRODUCTION CONVOLUTIONAL NEURAL NETWORK THE PROPOSED MODEL Data Source Dataset Description Work Procedure Data Preprocessing Experimental Setup and Evaluation Metrics RESULT AND DISCUSSION CONCLUSION 3. Lemon Fruits Detection and Instance Segmentation Under Orchard Environment Using Mask R-CNN and YOLOv5 S M Shahriar Sharif Rahat, Manjara Hasin Al Pitom, Mridula Mahzabun, and Md. Shamsuzzaman INTRODUCTION LITERATURE REVIEW Texture, color and Shape based fruits detection Machine learning based fruits detection MATERIALS AND METHODS Image data acquisition Image pre-processing Model architecture Model training RESULT ANALYSIS AND COMPARISON Result analysis Discussion CONCLUSION 4. A Deep Learning Approach in Detailed Fingerprint Identifica-tion Mohiuddin Ahmed, Abu Sayeed, Azmain Yakin Srizon, Md Rakibul Haque, and Md. Mehedi Hasan INTRODUCTION RELATED WORKS DATASET METHODOLOGY Convolutional Neural Network Model EXPERIMENTAL SETUP AND IMPLEMENTATION Hyperparameters Optimization Evaluation Criteria RESULTS AND DISCUSSION Gender Classification Hand Classification Finger Classification CONCLUSION 5. Probing Skin Lesions and Performing Classification of Skin Cancer Using EfficientNet while Resolving Class Imbalance Using SMOTE Md Rakibul Haque, Azmain Yakin Srizon, and Mohiuddin Ahmed INTRODUCTION METHODOLOGY Dataset Description SMOTE Efficient-Net PROPOSED APPROACH Resolving Class Imbalance Using SMOTE Extracting Complex and Versatile Features Using Efficient-NetB0 EXPERIMENTAL ANALYSIS Experimental Setup Classification Result Understanding the Misclassifications CONCLUSION 6. Advanced GradCAM++: Improved Visual Explanations of CNN’s decision in Diabetic Retinopathy Md. Shafayat Jamil, Sirdarta Prashad Banik, G. M. Atiqur Rahaman, and Sajib Saha INTRODUCTION BACKGROUND Convolutional Neural Networks (CNNs) Visualizing CNNs PROPOSED VISUALIZATION TECHNIQUE EXPERIMENTS AND RESULTS Training CNN model for disease level grading of DR Visualizing CNN through GradCAM++ and proposed method CONCLUSION 7. Bangla Sign Language Recognition Using Concatenated BdSL Network Thasin Abedin, Khondokar S. S. Prottoy, Ayana Moshruba, and Safayat Bin Hakim INTRODUCTION LITERATURE REVIEW METHODOLOGY Data Preprocessing Proposed Architecture Image Network Pose Estimation Network Concatenated BDSL Network Training Method RESULTS Dataset And Experimental Setup Performance of Concatenated BDSL Network DISCUSSION AND FUTURE SCOPE 8. ChestXRNet: A Multi-class Deep Convolutional Neural Net-works for Detecting Abnormalities in Chest X-Ray Images Ahmad Sabbir Chowdhury and Aseef Iqbal INTRODUCTION RELATED WORK METHODOLOGY Data Preprocessing Data Augmentation Proposed ChestXRNet Model Proposed Transfer Learning Methods for Benchmarking Callbacks in Keras RESULT ANALYSIS AND DISCUSSION Data Description and Datasets Experimental Setup ChestXRNet Model’s Training, Validation Accuracy and Loss Result Comparison Between ChestXRNet and Other PreTrained Models Model Evaluation and Prediction CONCLUSION 9. Achieving Human Level Performance on the Original Om-niglot Challenge Shamim Ibne Shahid INTRODUCTION RELATED WORK METHODOLOGY EVALUATION ON OMNIGLOT EVALUATION ON MNIST CONCLUSION 10. A Real-Time Classification Model for Bengali Character Recognition in Air-Writing Mohammed Abdul Kader, Muhammad Ahsan Ullah, and Md Saiful Islam INTRODUCTION METHODOLOGY Data Acquisition Feature Extraction Classification model RESULT AND ANALYSIS CONCLUSION AND FUTURE WORK 11. A Deep Learning Approach for Covid-19 Detection in Chest X-Rays SK. Shalauddin Kabir, Mohammad Farhad Bulbul, Fee Faysal Ahmed, Syed Galib, and Hazrat Ali INTRODUCTION LITERATURE REVIEW DATASET DESCRIPTION Data collection Dataset creation PROPOSED METHODOLOGY Proposed Algorithm Preprocessing: Image resize and normalization Augmentation of Images Deep Neural Networks and Transfer-learning Fine-tuning Experimental Setup Model Evaluation RESULTS AND DISCUSSION Evaluation Results on first setting Results on second setting Result on third setting CONCLUSION 12. Automatic Image Captioning Using Deep Learning Toshiba Kamruzzaman, Abdul Matin, Tasfia Seuti, and Md. Rakibul Islam INTRODUCTION LITERATURE REVIEW MODEL ARCHITECTURE Encoder Decoder Model-1: Base Model (LSTM: Long-Short Term Memory) Model-2: Transformer Model (BERT Integration) Model-3: Our Model (BERT with LSTM and dense layer) EXPERIMENTAL SETUP Dataset Hyperparameters RESULT ANALYSIS Qualitative Analysis Model-1: Base Model (LSTM: Long-Short Term Memory) Model-2: Transformer Model (BERT Integration) Model-3: Our Model (BERT with LSTM and dense layer) Quantitative Analysis CONCLUSION 13. A Convolutional Neural Network Based Approach to Recog-nize Bangla Handwritten Characters Mohammad Golam Mortuza, Saiful Islam, Md. Humayun Kabir, and Uipil Chong INTRODUCTION RELATED WORK METHODOLOGY AND SYSTEM ARCHITECTURE DATASET RESULT ANALYSIS CONCLUSION AND FUTURE WORK &lt
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 658.4038028563; Industry 4.0; Computer vision; Image analysis
• Languages: English
• ISBNs: 9781000804782; 9781000804737
• Related ISBNs: 9781032164168
• Notes: Note: Description based on CIP data; resource not viewed.
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