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Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade Prediction

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Abstract

The aim of the present study is to analysing the effect of water absorption on iron ore samples in the performances of SVM-based machine vision system. Two types of SVM-based machine vision system (classification and regression) were designed and developed, and performances were compared with dry and wet ore sample images. The images of the ore samples were captured in both the conditions (wet and dry) to examine the proposed model performance. A total of 280 image features were extracted and optimised using sequential forward floating selection (SFFS) algorithm for model development. The iron ore samples were collected from an Indian iron ore mine (Guamine), and image capturing system was fabricated in the laboratory for executing the proposed study. The results indicated that a different set of optimised features obtained for dry and wet sample images in both the models (classification and regression). Furthermore, the performance of both the models with dry sample images was found to be relatively better than the wet sample images.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, K L Education Foundation, Guntur, Andhra Pradesh, 522502, India

    Ashok Kumar Patel

  2. Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, 49931, USA

    Snehamoy Chatterjee

  3. Department of Mining Engineering, National Institute of Technology, Rourkela, Orissa, 769008, India

    Amit Kumar Gorai

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  1. Ashok Kumar Patel
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  2. Snehamoy Chatterjee
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Correspondence to Snehamoy Chatterjee or Amit Kumar Gorai.

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Ashok Kumar Patel completed his PhD in the field of machine vision application in mine systems in the Department of Mining Engineering, National Institute of Technology, Rourkela. After completion of B.Tech. and M.Tech. in Computer Science and Engineering, he had worked as a PhD scholar in NIT Rourkela during 2013 to 2017. Currently, he has been working as an Assistant Professor in Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, AP, India.

Snehamoy Chatterjee is faculty in the Department of Geological and Mining Engineering and Sciences, Michigan Technological University, USA. Earlier he served as an Assistant Professor in Department of Mining Engineering, NIT Rourkela. He did his BE in Mining Engineering in 2000 and ME in Mining Engineering in 2002 from IIEST, Shibpur. He completed his PhD in 2007 from IIT Kharagpur, India. He had served as a post-doctoral Fellow at the University of Alaska, Fairbanks, USA, and McGill University, Canada. He has published a number of technical papers in various journals. His research areas include geostatistics, machine vision system.

Amit Kumar Gorai did his B.E. and M.E. in Mining Engineering from IIEST, Shibpur and PhD in Environmental Science and Engineering from IIT (ISM) Dhanbad. For the last 10 years, he has been involved in teaching and research in the field of mine environment. He is currently working as an Associate Professor in the Department of Mining Engineering, NIT, Rourkela. He has to his credit many publications in various National and International Journals. He has also received the Young Scientist Research Grant from SERB, New Delhi, Raman Postdoctoral Fellowship Award under Indo-US Knowledge Initiative from UGC, New Delhi, and Endeavour Executive Fellowship of Australian Government.

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Patel, A.K., Chatterjee, S. & Gorai, A.K. Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade Prediction. Pattern Recognit. Image Anal. 29, 309–324 (2019). https://doi.org/10.1134/S1054661819010097

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