Computer Vision and Image Analysis for Industry 4.0

Nazmul Siddique, Mohammad Shamsul Arefin, Md Atiqur Rahman Ahad, M. Ali Akber Dewan, Nazmul Siddique, Mohammad Shamsul Arefin, Md Atiqur Rahman Ahad, M. Ali Akber Dewan

• Publisher: Chapman and Hall/CRC
• Year: 2023
• Print ISBN:
  9781032187624
  9781032164168
• Edition: 1
• eBook ISBN:
  9781000804782
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Preface
• Contributors
• Editors
• Chapter 1 ◾ BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation
• Chapter 2 ◾ A New Approach Using a Convolutional Neural Network for Crop and Weed Classification
• Chapter 3 ◾ Lemon Fruit Detection and Instance Segmentation in an Orchard Environment Using Mask R-CNN and YOLOv5
• Chapter 4 ◾ A Deep Learning Approach in Detailed Fingerprint Identification
• Chapter 5 ◾ Probing Skin Lesions and Performing Classification of Skin Cancer Using EfficientNet while Resolving Class Imbalance Using SMOTE
• Chapter 6 ◾ Advanced GradCAM++: Improved Visual Explanations of CNN Decisions in Diabetic Retinopathy
• Chapter 7 ◾ Bangla Sign Language Recognition Using a Concatenated BdSL Network
• Chapter 8 ◾ ChestXRNet: A Multi-class Deep Convolutional Neural Network for Detecting Abnormalities in Chest X-Ray Images
• Chapter 9 ◾ Achieving Human Level Performance on the Original Omniglot Challenge
• Chapter 10 ◾ A Real-Time Classification Model for Bengali Character Recognition in Air-Writing
• Chapter 11 ◾ A Deep Learning Approach for Covid-19 Detection in Chest X-Rays
• Chapter 12 ◾ Automatic Image Captioning Using Deep Learning
• Chapter 13 ◾ A Convolutional Neural Network Based Approach to Recognize Bangla Handwritten Characters
• Chapter 14 ◾ Flood Region Detection Based on K-Means Algorithm and Color Probability
• Chapter 15 ◾ Fabrication of Smart Eye Controlled Wheelchair for Disabled Person
• Index