scholarly journals A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey

2018 ◽  
Vol 7 (03) ◽  
pp. 23755-23760
Author(s):  
S. Dhivya ◽  
Dr.R. Shanmugavadivu
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Big Data ◽  
Edge Detection ◽  
Research Area ◽  
Detection Methods ◽  
Image Mining ◽  
Survey Paper ◽  
Research Problems ◽  
Image Pattern

In Today’s era Big Data is one of the most well-known research area that try to solve many research problems. The focus is mainly on how to come out those problems of Big Data and it could be handling in recent systems. Image mining and genetic algorithm is used to automate the process of images, patterns, data sets and etc. Image mining is used to extract the hidden images from the set of images. Genetic algorithm is also quite effective in solving certain optimization and intelligence problems and it is used in many applications, including image pattern recognition. The survey paper reviews of Big Data with edge detection methods on various types of images. In edge detection image pattern recognition is to choose the best images from the group of images by using both image mining and genetic algorithm techniques

scholarly journals Digitalization and Big Data in Smart Farming – Bibliometric and Systemic Analysis

2020 ◽  
Author(s):  
Jaqueline Iaksch ◽  
Ederson Fernandes ◽  
Milton Borsato
Keyword(s):  
Big Data ◽  
Value Chain ◽  
New Technologies ◽  
Research Area ◽  
Management Control ◽  
Digital Information ◽  
Agricultural Efficiency ◽  
Systemic Analysis ◽  
Smart Farming ◽  
Research Problems

Agriculture has always had a great significance in the civilization development. However, modern agriculture is facing increasing challenges due to population growth and environmental degradation. Commercially, farmers are looking for ways to improve profitability and agricultural efficiency to reduce costs. Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain. Thus, better decisions and efficient management control are required through generated information and knowledge at any farm. New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing, and thereby contribute to increased agricultural productivity. Therefore, this article aims to gain state-of-art insight and identify proposed solutions, trends and unfilled gaps regarding digitalization and Big Data applications in Smart Farming, through a literature review. The current study accomplished these goals through analyses based on ProKnow-C (Knowledge Development Process – Constructivist) methodology. A total of 2401 articles were found. Then, a quantitative analysis identified the most relevant ones among a total of 39 articles were included in a bibliometric and text mining analysis, which was performed to identify the most relevant journals and authors that stand out in the research area. A systemic analysis was also accomplished from these articles. Finally, research problems, solutions, opportunities, and new trends to be explored were identified.

Image pattern recognition in big data: taxonomy and open challenges: survey

2017 ◽  
Vol 77 (8) ◽  
pp. 10091-10121 ◽  
Author(s):  
Saber Zerdoumi ◽  
Aznul Qalid Md Sabri ◽  
Amirrudin Kamsin ◽  
Ibrahim Abaker Targio Hashem ◽  
Abdullah Gani ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Big Data ◽  
Image Pattern

A REVIEW OF WAVELET-BASED EDGE DETECTION METHODS

2012 ◽  
Vol 26 (07) ◽  
pp. 1255011 ◽  
Author(s):  
P. S. P. WANG ◽  
JIANWEI YANG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Edge Detection ◽  
Main Idea ◽  
Detection Methods ◽  
Local Regularity ◽  
The Past ◽  
Different Types ◽  
Key Issues

Edges are prominent features in images. The detection and analysis of edges are key issues in image processing, computer vision and pattern recognition. Wavelet provides a powerful tool to analyze the local regularity of signals. Wavelet transform has been successfully applied to the analysis and detection of edges. A great number of wavelet-based edge detection methods have been proposed over the past years. The objective of this paper is to give a brief review of these methods, and encourage the research of this topic. In practice, an image is usually of multistructure edge, the identification of different edges, such as steps, curves and junctions play an important role in pattern recognition. In this paper, more attention is paid on the identification of different types of edges. We present the main idea and the properties of these methods.

Research of edge detection methods in the task of pattern recognition on images with low contrast

2021 ◽  
Vol 1 ◽  
pp. 123-128
Author(s):  
E.V. Belyaeva ◽  
Keyword(s):  
Pattern Recognition ◽  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Detection Methods ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Low Contrast ◽  
Gaussian Filters

The article discusses edge detection methods separately and combinations of edge detection filters with antialiasing filters in the task of pattern recognition on images with low contrast. Sobel, Canny, Otsu and thresholding filters are considered as edge detection methods. Median and Gaussian filters are considered as smoothing filters. The performance of the filters is assessed using the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM).

Image Pattern Recognition for an Intelligent Healthcare System: An Application Area of Machine Learning and Big Data

2019 ◽  
Vol 16 (9) ◽  
pp. 3932-3937 ◽  
Author(s):  
Mohit Chhabra ◽  
Rajneesh Kumar Gujral
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Big Data ◽  
Health Sector ◽  
Medical Application ◽  
Medical Data ◽  
Healthcare Sector ◽  
Medical Expert ◽  
Intelligent Healthcare ◽  
Image Pattern

Today healthcare sector is completely distinguished from other industries. It is a highly important area and people wants highest level of care and facilities irrespective of cost. It could not accomplish social prospect even though it consumes vast fraction of budget. Frequently the analyses of medical data were done by the medical expert. In terms of image analysis by different human expert, it is often restricted due to its subjectivity, image complexity, widespread differences occur across different translators, and fatigue. As after the feat of Big Data and machine learning in real world medical application, it is similarly giving exhilarating results with fine precision for medical imaging and is viewed as an important factor for upcoming applications in area of health sector. This paper presents survey of different applications on the Machine Learning and Big Data which relies on image pattern recognition.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Comparative Analysis of Edge Detection Methods and its Implementation using UTLP Kit

2016 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
HEMALATHA R. ◽  
SANTHIYAKUMARI N. ◽  
MADHESWARAN M. ◽  
SURESH S. ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Edge Detection ◽  
Detection Methods
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