Image Processing Expert System with Knowledgeâ€”Generative Functionâ€”Application to Footprint Image Understanding

Pre-processing is very useful in a variety of situations since it helps to suppress information that is not related to the exact image processing or analysis task. Mathematical morphology is used for analysis, understanding and image processing. It is an influential method in the geometric morphological analysis and image understanding. It has befallen a new theory in the digital image processing domain. Edges detection and noise reduction are a crucial and very important pre-processing step. The classical edge detection methods and filtering are less accurate in detecting complex edge and filtering various types of noise. This paper proposed some useful mathematic morphological techniques to detect edge and to filter noise in metal parts image. The experimental result showed that the proposed algorithm helps to increase accuracy of metal parts inspection system.

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Artificial Intelligence in Medical Science

Medical Diagnosis Using Artificial Neural Networks ◽

10.4018/978-1-4666-6146-2.ch002 ◽

2014 ◽

pp. 11-23 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Image Processing ◽

Decision Making ◽

Signal Processing ◽

Expert System ◽

Knowledge Base ◽

Medical Diagnosis ◽

Medical Science ◽

Inference Engine ◽

Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

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Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽

10.4018/978-1-5225-0983-7.ch017 ◽

2017 ◽

pp. 382-402

Author(s):

Petre Anghelescu

Keyword(s):

Image Processing ◽

Cellular Automata ◽

Edge Detection ◽

Digital Image Processing ◽

Digital Image ◽

Image Understanding ◽

Software Application ◽

Detection Algorithms ◽

Human Image ◽

The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

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