Detection of disease using block-based unsupervised natural plant leaf color image segmentation

Authors:
Shitala Prasad;Piyush Kumar;Anuj Jain
Affiliations:
Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee, Roorkee, India;Department of Information Technology, Indian Institute of Information Technology, Allahabad, India;Department of Computer Science and Engineerning, NRI Institute of Technology and Managment, Gwalior, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

Citing 9
Cited 1

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
An analysis of histogram-based thresholding algorithms

CVGIP: Graphical Models and Image Processing
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Color texture segmentation using feature distributions

Pattern Recognition Letters
Contour Continuity in Region Based Image Segmentation

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Geometric Level Set Methods in Imaging,Vision,and Graphics

Geometric Level Set Methods in Imaging,Vision,and Graphics
Relative sub-image based features for leaf recognition using support vector machine

Proceedings of the 2011 International Conference on Communication, Computing & Security
Image segmentation with ratio cut

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic image segmentation by integrating color-edge extraction and seeded region growing

IEEE Transactions on Image Processing

Plant leaf disease detection using gabor wavelet transform

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel unsupervised color image segmentation method is proposed in this paper. First, the image is converted to HSI color model and then it is divided into 5x5 grid matrix resulting into 25 blocks of original leaf image. Each block is then processed separately and passed under an unsupervised segmentation. This segmentation is based on minimizing the energy of each region in the image. This gives the better result in cased of diseased leaf image dataset. This automated system is very much applicable in research work by the botanists specially working with crop diseases and production.