TY - GEN
T1 - A Structured Image Processing Operation Library to Automatically Isolate Weeds and Crops
AU - Ashqer, Yaqeen Salatneh
AU - Bikdash, Marwan
AU - Liang, Chyi Lyi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - We propose a computer-vision framework that isolate parts of the image pertaining to specific weeds and crops using image processing. We developed a structured Image Processing Operation (IPO) library, implemented in JSON format, for efficient representations of the algorithms needed to isolate the desired parts of the image. For each species, a specialized sequence of IPOs, along with their parameters, are obtained. Emphasis is placed on morphological image processing. The IPOs are mostly standard MATLAB functions, but specific and specialized image processing algorithms can be added as needed. We show preliminary success and discuss various aspects and complications.
AB - We propose a computer-vision framework that isolate parts of the image pertaining to specific weeds and crops using image processing. We developed a structured Image Processing Operation (IPO) library, implemented in JSON format, for efficient representations of the algorithms needed to isolate the desired parts of the image. For each species, a specialized sequence of IPOs, along with their parameters, are obtained. Emphasis is placed on morphological image processing. The IPOs are mostly standard MATLAB functions, but specific and specialized image processing algorithms can be added as needed. We show preliminary success and discuss various aspects and complications.
UR - https://dx.doi.org/10.1109/SoutheastCon42311.2019.9020595
U2 - 10.1109/southeastcon42311.2019.9020595
DO - 10.1109/southeastcon42311.2019.9020595
M3 - Conference contribution
BT - 2019 IEEE SoutheastCon, SoutheastCon 2019
ER -