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Machine Vision Inspection Systems

Image Processing, Concepts, Methodologies, and Applications

Muthukumaran Malarvel (Redaktør) ; Soumya Ranjan Nayak (Redaktør) ; Surya Narayan Panda (Redaktør) ; Prasant Kumar Pattnaik (Redaktør) ; Nittaya Muangnak (Redaktør)

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. Les mer
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Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

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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 pro cessing 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.

Fakta

Innholdsfortegnelse

Preface xi


1 Land-Use Classification with Integrated Data 1
D. A. Meedeniya, J. A. A. M Jayanetti, M. D. N. Dilini, M. H. Wickramapala and J. H. Madushanka


1.1 Introduction 2


1.2 Background Study 3


1.2.1 Overview of Land-Use and Land-Cover Information 3


1.2.2 Geographical Information Systems 4


1.2.3 GIS-Related Data Types 4


1.2.3.1 Point Data Sets 4


1.2.3.2 Aerial Data Sets 5


1.2.4 Related Studies 6


1.3 System Design 6


1.4 Implementation Details 10


1.4.1 Materials 10


1.4.2 Preprocessing 11


1.4.3 Built-Up Area Extraction 11


1.4.4 Per-Pixel Classification 12


1.4.5 Clustering 14


1.4.6 Segmentation 14


1.4.7 Object-Based Image Classification 16


1.4.8 Foursquare Data Preprocessing and Quality Analysis 20


1.4.9 Integration of Satellite Images with Foursquare Data 21


1.4.10 Building Block Identification 21


1.4.11 Overlay of Foursquare Points 22


1.4.12 Visualization of Land Usage 23


1.4.13 Common Platform Development 23


1.5 System Evaluation 25


1.5.1 Experimental Evaluation Process 25


1.5.2 Evaluation of the Classification Using Base Error Matrix 28


1.6 Discussion 31


1.6.1 Contribution of the Proposed Approach 31


1.6.2 Limitations of the Data Sets 32


1.6.3 Future Research Directions 33


1.7 Conclusion 34


References 35


2 Indian Sign Language Recognition Using Soft Computing Techniques 37
Ashok Kumar Sahoo, Pradeepta Kumar Sarangi and Parul Goyal


2.1 Introduction 37


2.2 Related Works 38


2.2.1 The Domain of Sign Language 39


2.2.2 The Data Acquisition Methods 41


2.2.3 Preprocessing Steps 42


2.2.3.1 Image Restructuring 43


2.2.3.2 Skin Color Detection 43


2.2.4 Methods of Feature Extraction Used in the Experiments 44


2.2.5 Classification Techniques 45


2.2.5.1 K-Nearest Neighbor 45


2.2.5.2 Neural Network Classifier 45


2.2.5.3 Naive Bayes Classifier 46


2.3 Experiments 46


2.3.1 Experiments on ISL Digits 46


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