A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0301-0.ch006 ◽

2020 ◽

pp. 104-117

Author(s):

Osman Hürol Türkakın

Keyword(s):

Machine Learning ◽

Image Processing ◽

Computer Vision ◽

Current Literature ◽

Machine Learning Techniques ◽

Scientometric Analysis ◽

Learning Approaches ◽

Learning Techniques ◽

Computer Vision Applications ◽

Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

Download Full-text

Machine Learning:A Review

Semiconductor Science and Information Devices ◽

10.30564/ssid.v2i2.1931 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Isonkobong Christopher Udousoro

Keyword(s):

Machine Learning ◽

Image Processing ◽

Data Interpretation ◽

Relevant Information ◽

Machine Learning Techniques ◽

Predictive Analysis ◽

Learning Approaches ◽

Processing Data ◽

Learning Techniques ◽

Machine Learning Applications

Due to the complexity of data, interpretation of pattern or extraction of information becomes difficult; therefore application of machine learning is used to teach machines how to handle data more efficiently. With the increase of datasets, various organizations now apply machine learning applications and algorithms. Many industries apply machine learning to extract relevant information for analysis purposes. Many scholars, mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems. In this paper, we focus on general review of machine learning including various machine learning techniques. These techniques can be applied to different fields like image processing, data mining, predictive analysis and so on. The paper aims at reviewing machine learning techniques and algorithms. The research methodology is based on qualitative analysis where various literatures is being reviewed based on machine learning.

Download Full-text

Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images

Machine Learning ◽

10.4018/978-1-60960-818-7.ch406 ◽

2012 ◽

pp. 817-829

Author(s):

Nikolaos Giannakeas ◽

Dimitrios I. Fotiadis

Keyword(s):

Machine Learning ◽

Image Processing ◽

Expression Profiles ◽

Machine Learning Techniques ◽

Experimental Conditions ◽

Microarray Image ◽

Learning Techniques ◽

Common Substrate ◽

Processing Techniques ◽

Comprehensive Measurement

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.

Download Full-text