TY - GEN
T1 - Superpixels using morphology for rock image segmentation
AU - Malladi, Sree Ramya S.P.
AU - Ram, Sundaresh
AU - Rodríguez, Jeffrey J.
PY - 2014
Y1 - 2014
N2 - Detection and segmentation of rocks is an important first task in many applications such as geological analysis, planetary science and mining processes. Rocks are usually segmented using a variety of features such as texture, shading, shape and edges. It is easier to compute these features for rock superpixels rather than every pixel in the image. A superpixel is a group of spatially coherent pixels that form a meaningful homogeneous region, usually belonging to the same object. In this paper, we perform a comparative study of some of the current superpixel algorithms on rock images with regard to their ability to adhere to image boundaries, their speed, and their impact on rock segmentation performance. Also, we propose a new and very simple superpixel algorithm, Superpixels Using Morphology (SUM), which permutes a watershed transformation approach to efficiently generate superpixels. We show that SUM achieves a performance comparable to the recent superpixel algorithms on the rock images.
AB - Detection and segmentation of rocks is an important first task in many applications such as geological analysis, planetary science and mining processes. Rocks are usually segmented using a variety of features such as texture, shading, shape and edges. It is easier to compute these features for rock superpixels rather than every pixel in the image. A superpixel is a group of spatially coherent pixels that form a meaningful homogeneous region, usually belonging to the same object. In this paper, we perform a comparative study of some of the current superpixel algorithms on rock images with regard to their ability to adhere to image boundaries, their speed, and their impact on rock segmentation performance. Also, we propose a new and very simple superpixel algorithm, Superpixels Using Morphology (SUM), which permutes a watershed transformation approach to efficiently generate superpixels. We show that SUM achieves a performance comparable to the recent superpixel algorithms on the rock images.
KW - area closing
KW - morphology
KW - rock particles
KW - superpixels
KW - watershed segmentation
UR - http://www.scopus.com/inward/record.url?scp=84902247124&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902247124&partnerID=8YFLogxK
U2 - 10.1109/SSIAI.2014.6806050
DO - 10.1109/SSIAI.2014.6806050
M3 - Conference contribution
AN - SCOPUS:84902247124
SN - 9781479940530
T3 - Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
SP - 145
EP - 148
BT - 2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014
Y2 - 6 April 2014 through 8 April 2014
ER -