Computer Vision and Image Analysis for Industry 4.0

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

INTRODUCTION

CONVOLUTIONAL NEURAL NETWORK

THE PROPOSED MODEL

Data Source

Dataset Description

Work Procedure

Data Preprocessing

Experimental Setup and Evaluation Metrics

RESULT AND DISCUSSION

CONCLUSION

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

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

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

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

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

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

INTRODUCTION

RELATED WORK

METHODOLOGY

EVALUATION ON OMNIGLOT

EVALUATION ON MNIST

CONCLUSION

INTRODUCTION

METHODOLOGY

Data Acquisition

Feature Extraction

Classification model

RESULT AND ANALYSIS

CONCLUSION AND FUTURE WORK

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

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

INTRODUCTION

RELATED WORK

METHODOLOGY AND SYSTEM ARCHITECTURE

DATASET

RESULT ANALYSIS

CONCLUSION AND FUTURE WORK

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

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.