An anatomization of noise removal techniques on medical images

Pandey, Madhulika; Bhatia, Madhulika; Bansal, Abhay

doi:10.1109/iciccs.2016.7542308

Cited by 11 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Images f(x,y) where x and y are the image coordinates are processed on the basis of nature and type of the problem. Digital image processing has wide applications in numerous areas like medical imaging [34][35][36][37], pattern recognition, biotechnology, computer vision and many others.…”

Section: Related Workmentioning

confidence: 99%

SVM Prediction Model Interface for Plant Contaminates

Aggarwal¹,

Bhatia²,

Madaan³

et al. 2021

Self Cite

View full text Add to dashboard Cite

In today's time, our nature is fighting against many life-threatening problems which can even threaten the existence of life on the Earth. Pollution is one of the deadliest problems among them. It is caused primarily by means of air, water and land but air pollution is the most severe and dreadful among them. It is caused by introduction of toxic substances like oxides of Sulphur, nitrogen and carbon into the atmosphere making it unfit for living beings. Along with humans, plants have also become a victim to it, and this fact is mostly ignored. A model has been designed to predict the effect of pollution on plants. Image samples of 5 Indian oxygen rich plants namely Ocimum Tenuiflorum, Sansevieria Trifasciata, Chlorophytum Comosum, and Azadirachta Indica have been taken for analysis and various properties like shape, color, corners and texture of the plants were considered from these input RGB images. As a consequence of these properties and the pollution index value, certain calculations have been performed and the results are compared with the threshold values. Based on the range in which the calculated results lie, the plants will be categorized into a category which depicts the severity level of pollution in the environment. After applying the model on the images, a dataset was prepared and SVM classification model has been trained on it which predict with an accuracy of 85%. It has been presented in the form of an interactive user interface to predict the effect of pollution on plants. Plants are an integral part of nature and should not be ignored.

show abstract

Section: Related Workmentioning

confidence: 99%