Detecting Phases in Steel Microstructure

Singh, Suraj

dc.contributor.author	Singh, Suraj
dc.date.accessioned	2022-02-03T06:05:44Z
dc.date.available	2022-02-03T06:05:44Z
dc.date.issued	2019-07
dc.identifier.citation	22p.	en_US
dc.identifier.uri	http://hdl.handle.net/10263/7263
dc.description	Dissertation under the supervision of Dr. Dipti Prasad Mukherjee	en_US
dc.description.abstract	Detecting Phases in Steel Microstructure is one of the interesting problem in the field of computer vision. In this work, we discuss a pixel based classification approach. A classifier is only as good as the information you give it. On the other hand it may have a huge intrinsic disproportion the number of examples in each class, Which hinder the classification performance. There are many ways you can adjust how you’re representing your input data for learning of model. In this paper, we propose ensemble method based on outlier detection to comprehend better the data used as a part of learning of model , which is random forest and discuss it merits and demerits of other related method which we use.	en_US
dc.language.iso	en	en_US
dc.publisher	Indian Statistical Institute,Kolkata	en_US
dc.relation.ispartofseries	Dissertation;;2019-15
dc.subject	outlier	en_US
dc.subject	Isolation Forest	en_US
dc.title	Detecting Phases in Steel Microstructure	en_US
dc.type	Other	en_US