1st Edition

Computer Vision and Image Analysis for Industry 4.0

    213 Pages 110 B/W Illustrations
    by Chapman & Hall

    213 Pages 110 B/W Illustrations
    by Chapman & Hall

    Computer vision and image analysis are indispensable components of every automated environment. Modern machine vision and image analysis techniques play key roles in automation and quality assurance. Working environments can be improved significantly if we integrate computer vision and image analysis techniques. The more advancement in innovation and research in computer vision and image processing, the greater the efficiency of machines as well as humans. Computer Vision and Image Analysis for Industry 4.0 focuses on the roles of computer vision and image analysis for 4.0 IR-related technologies. The text proposes a variety of techniques for disease detection and prediction, text recognition and signature verification, image captioning, flood level assessment, crops classifications and fabrication of smart eye-controlled wheelchairs.

    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

    14. Flood Region Detection Based on K-Means Algorithm and Color Probability

    Promiti Chakraborty, Sabiha Anan, and Kaushik Deb

    INTRODUCTION

    LITERATURE REVIEW

    OUTLINE OF METHODOLOGY

    Background Subtraction

    Dynamic K-Means Clustering Algorithm

    Connected Component Labelling

    Morphological Closing

    Color Probability

    Edge Density

    EXPERIMENTAL RESULT ANALYSIS

    CONCLUSION AND FUTURE WORK

    15. Fabrication of Smart Eye Controlled Wheelchair for Disabled Person

    Md. Anisur Rahman, Md. Abdur Rahman, Md. Imteaz Ahmed, and Md. Iftekher Hossain

    INTRODUCTION

    RELATED WORK

    NOVELTY AND CONTRIBUTION

    ORGANIZATION OF THE PAPER

    SYSTEM DESIGN

    Hardware configuration

    Software configuration

    METHODOLOGY

    RESULT

    CONCLUSION AND FUTURE WORK

    Biography

    Nazmul Siddique is with the School of Computing, Engineering and Intelligent Systems, Ulster University. He obtained Dipl.-Ing. degree in Cybernetics from the Dresden University of Technology, Germany, MSc in Computer Science from Bangladesh University of Engineering and Technology and PhD in Intelligent Control from the Department of Automatic Control and Systems Engineering, University of Sheffield, England. His research interests include: cybernetics, computational intelligence, nature-inspired computing, stochastic systems and vehicular communication. He has published over 170 research papers including five books published by John Wiley, Springer and Taylor Francis. He guest-edited eight special issues of reputed journals on Cybernetic Intelligence, Computational Intelligence, Neural Networks and Robotics. He is on the editorial board of seven international journals including Nature Scientific Research. He is a Fellow of the Higher Education Academy, a senior member of IEEE and member of different committees of IEEE SMC Society and UK-RI Chapter. He was involved in organising many national and international conferences and co-edited seven conference proceedings.

    Mohammad Shamsul Arefin is in lien from Chittagong University of Engineering and Technology (CUET) and currently affiliated with the Department of Computer Science and Engineering (CSE), Daffodil International University, Bangladesh. Earlier he was the Head of the Department of CSE, CUET. Prof. Arefin received his Doctor of Engineering Degree in Information Engineering from Hiroshima University, Japan with support of the scholarship of MEXT, Japan. As a part of his doctoral research, Dr. Arefin was with IBM Yamato Software Laboratory, Japan. His research includes privacy preserving data publishing and mining, distributed and cloud computing, big data management, multilingual data management, semantic web, object oriented system development and IT for agriculture and environment. Dr. Arefin has more than 120 referred publications in international journals, book series and conference proceedings. He is a senior member of IEEE, Member of ACM, Fellow of IEB and BCS. Dr. Arefin is the Organizing Chair of BIM 2021; TPC Chair, ECCE 2017; Organizing Co-Chair, ECCE 2019; and Organizing Chair, BDML 2020. Dr. Arefin visited Japan, Indonesia, Malaysia, Bhutan, Singapore, South Korea, Egypt, India, Saudi Arabia and China for different professional and social activities.

    Md Atiqur Rahman Ahad, SMIEEE, SMOSA; Professor, University of Dhaka (DU); Specially Appointed Associate Professor, Osaka University. He studied at the University of Dhaka, University of New South Wales, and Kyushu Institute of Technology. His authored/edited 10 books in Springer, e.g., “IoT-sensor based Activity Recognition”; “Motion History Images for Action Recognition and Understanding”; “Computer Vision and Action Recognition”. He published 180+ journal/conference papers, chapters, 120+ keynote/invited talks, 35+ Awards/Recognitions. He is an Editorial Board Member of Scientific Reports, Nature; Assoc. Editor of Frontiers in Computer Science; Editor of Int. Journal of Affective Engineering; Editor-in-Chief: IJCVSP; Guest-Editor: PRL, Elsevier; JMUI, Springer; JHE, Hindawi; IJICIC; Member: ACM, IAPR.

    M. Ali Akber Dewan, Member, IEEE received the B.Sc. degree in computer science and engineering from Khulna University, Bangladesh, in 2003, and the Ph.D. degree in computer engineering from Kyung Hee University, South Korea, in 2009. From 2003 to 2008, he was also a Lecturer with the Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Bangladesh, where he was an Assistant Professor, in 2009. From 2009 to 2012, he was a Postdoctoral Researcher with Concordia University, Montreal, QC, Canada. From 2012 to 2014, he was a Research Associate with the ť Ecole de Technologie Supťerieure, Montreal. He is currently an Assistant Professor with the School of Computing and Information Systems, Athabasca University, Canada. He has published more than 50 articles in high impact journals and conference proceedings. His research interests include artificial intelligence, affective computing, computer vision, data mining, information visualization, machine learning, biometric recognition, medical image analysis, and health informatics. He has served as an editorial board member, a Chair/Co-Chair and a TPC member in several prestigious journals and conferences. He received the Dean’s Award and the Excellent Research Achievement Award for his excellent academic performance and research achievements during his Ph.D. studies in South Korea.