Machine Vision Inspection Systems, Image Processing, Concepts, Methodologies, and Applications

Image Processing, Concepts, Methodologies, and Applications

Muthukumaran Malarvel, Soumya Ranjan Nayak, Surya Narayan Panda, Prasant Kumar Pattnaik, Nittaya Muangnak, Muthukumaran Malarvel, Soumya Ranjan Nayak, Surya Narayan Panda, Prasant Kumar Pattnaik, Nittaya Muangnak

• Publisher: Wiley-Scrivener
• Year: 2020
• Print ISBN: 9781119681809
• eBook ISBN: 9781119682097
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

This edited book brings together leading researchers, academic scientists and research scholars to put forward and share their experiences and research results on all aspects of an inspection system for detection analysis for various machine vision applications. It also provides a premier interdisciplinary platform to present and discuss the most recent innovations, trends, methodology, applications, and concerns as well as practical challenges encountered and solutions adopted in the inspection system in terms of image processing and analytics of machine vision for real and industrial application.

Machine vision inspection systems (MVIS) utilized all industrial and non-industrial applications where the execution of their utilities based on the acquisition and processing of images. MVIS can be applicable in industry, governmental, defense, aerospace, remote sensing, medical, and academic/education applications but constraints are different. MVIS entails acceptable accuracy, high reliability, high robustness, and low cost. Image processing is a well-defined transformation between human vision and image digitization, and their techniques are the foremost way to experiment in the MVIS. The digital image technique furnishes improved pictorial information by processing the image data through machine vision perception. Digital image pro­cessing has widely been used in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc., ), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), barcode reading and traceability, medical diagnosis, weather forecasting, face recognition, defence and space research, etc. This edited book is designed to address various aspects of recent methodologies, concepts and research plan out to the readers for giving more depth insights for perusing research on machine vision using image processing techniques.

1
Land-Use Classification with Integrated Data

D. A. Meedeniya*, J. A. A. M Jayanetti, M. D. N. Dilini, M. H. Wickramapala and J. H. Madushanka

Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka

Abstract

The identification of the usage and coverage of the land is a major part of regional development. Crowdsourced geographic information systems provide valuable information about the land use of different regions. Although these data sources lack reliability and possess some limitations, they are useful in deriving building blocks for the usage of the land, where the manual surveys are not up-to-date, costly, and time consuming. At present, in the context of Sri Lanka, there is a lack of reliable and updated land-use data. Moreover, there is a rapid growth in the construction industry, resulting in frequent changes in land-use and land-cover data. This paper presents a novel and an automated methodology based on learning models for identifying the usage and coverage of the land. The satellite imagery is used to identify the information related to land cover. They are integrated with Foursquare venue data, which is a popular crowdsourced geographic information, thus, enhancing the information level and the quality of land-use visualization. The proposed methodology has shown a kappa coefficient of 74.03%, showing an average land-use classification accuracy within a constrained environment.

Keywords: Geographic information system, land-cover identification, land-use classification, social computing, decision support system, satellite images, Foursquare data

1.1 Introduction

Regional planning and management are major concerns in the development strategy of a country. The information related to the coverage and usage of lands can be used to extract the features in an area and facilitate development activities. The land-use data are related to human activities, whereas the land-cover information represent the natural features and artificial constructions on the earth surface. Crowdsourced geographic information systems provide valuable information about the land use of different regions. At present, up-to-date data on land usage and coverage are not available for all the cities in Sri Lanka. This is due to the cost of labor, lack of the required technologies, and resources associated with the data surveys. Unavailability of a cost-effective way of obtaining such latest and reliable data is a bottleneck to the long-term planning and development of a region. This results in unplanned ad hoc developments, construction of unhealthy residential areas, deterioration of service and infrastructure, environmental pollution, increased traffic congestion, and so on [1], which can be widely seen in many urban areas in Sri Lanka. Therefore, up-to-date data on the usage and coverage of land are important to make strategic decisions on sustainable region planning.

The objective of this research is to design and develop a support system to classify the land-cover and land-use data using Google Satellite imagery [2] and Foursquare data, which is a type of volunteer geographic information (VGI), respectively [3]. The system produces a visualization of different types of land-use in each area (eg. residential, industrial, commercial, agriculture etc.) on a land-use map based on heterogeneous data sources including crowdsourced Foursquare data. Acquiring data on land cover and land use from different data types, which can be integrated into the classification system, will enhance the quality of the processed information [4].

Therefore, this research provides a novel way of identifying and classifying different forms of land-use data, specifically satellite imagery and Foursquare data, with the extensible features for other types of related data. The system refines the land-use mapping with the use of additional parameters, such as context-specific different data sources. Ultimately, the retrieved data can be used to monitor land-use changes in near real time [2]. Moreover, this study focuses on developing a common platform that enables the collaboration of heterogeneous data sources to produce enhanced land-use data. Further, this will increase the utility value of the retrieved information on land-cover and land-use, hence, widening the range of applicable applications from the results. Colombo district is selected as the study area considering the availability and sampling rates of different data sets and issues associated with data validation [4]. The proposed land-use visualization approach identifies and classifies different forms of land cover and land use in a selected area considering the satellite imagery and Foursquare data, respectively, and displays the classification on a land-use map.

The land-use data retrieved from the proposed methodology can be used to monitor land-use changes near real time. Analysis of these detailed snapshots of land-use enables authorities to detect a change and foresee its social and environmental consequences. This, in turn, will enable them to identify long-lasting sustainable solutions to urbanization issues in Sri Lanka.

The paper is structured as follow...

Table of contents

• Cover
• Table of Contents
• Preface
• 1 Land-Use Classification with Integrated Data
• 2 Indian Sign Language Recognition Using Soft Computing Techniques
• 3 Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model
• 4 Object Descriptor for Machine Vision
• 5 Flood Disaster Management
• 6 Temporal Color Analysis of Avocado Dip for Quality Control
• 7 Image and Video Processing for Defect Detection in Key Infrastructure
• 8 Methodology for the Detection of Asymptomatic Diabetic Retinopathy
• 9 Offline Handwritten Numeral Recognition Using Convolution Neural Network
• 10 A Review on Phishing—Machine Vision and Learning Approaches
• Index
• End User License Agreement