Computer Vision based Identification and Mosaic of Gramineous Grass Seeds

Preface
Contents
1 Introduction
	1.1 Digitization Grassland
	1.2 Computer Vision Based Research Status of Grassland Digitization
		1.2.1 Grassland Digitization Based on Remote Sensing
		1.2.2 Grassland Digitization Based on Common Digital Images
		1.2.3 Researches on the Related Fields
	1.3 Research Content
	References
2 Forage Identification and Experimental Materials
	2.1 Definition of Forage Automated Identification
	2.2 Automatic Identification of Herbage Based on Seed Images
	2.3 Experimental Materials
		2.3.1 Image Acquisition
		2.3.2 Image Preprocessing
	2.4 Seed Image DatabaseI
	References
3 Identification of Gramineous Grass Seeds Using Gabor and Locality Preserving Projections
	3.1 Feature Extraction
		3.1.1 Gabor Filters
		3.1.2 Locality Preserving Projections
	3.2 Feature Matching
	3.3 Results and Discussion
		3.3.1 Experiments on Seed Image DatabaseI
		3.3.2 Experiments on Seed Image DatabaseII
	3.4 Summary
	References
4 Identification of Gramineous Grass Seeds Using Difference of Local Fractal Dimensions
	4.1 Fractal and Fractal Dimension
	4.2 Difference of Local Fractal Dimension
	4.3 Results and Discussions
		4.3.1 Local Division Strategy
		4.3.2 Comparison of Different Seed Identification Algorithms
	4.4 Summary
	References
5 Identification of Gramineous Grass Seeds Using Local Similarity Pattern and Linear Discriminant Analysis
	5.1 Identification Based on LSP and LDA
	5.2 Experimental Results and Discussions
		5.2.1 Image Database and Preprocessing
		5.2.2 Identification Experiments
	5.3 Summary
	References
6 Identification of Gramineous Grass Seeds Using Local Similarity Pattern and Gray Level Co-occurrence Matrix
	6.1 Fusion of LSP and GLCM in Feature Extraction
		6.1.1 Gray Level Co-occurrence Matrix
		6.1.2 Fusion of LSP and GLCM for feature extraction
	6.2 Experimental Results and Discussions
	References
7 Microscopic Image Mosaic of Gramineous Grass Seeds
	7.1 Image Mosaic
	7.2 Materials and Methods
		7.2.1 Experimental Materials
		7.2.2 Mosaic Method
	7.3 Experimental Results and Discussions
	7.4 Summary
	References
8 Digital Information Platform of Grassland and Forage Based on Computer Vision
	8.1 Digital Information Platform of Grassland and Forage
		8.1.1 Main Development Tools and Models for Platform
		8.1.2 Main Function Modules of the Platform
	8.2 Key Technological Problems and Solutions of the Platform
		8.2.1 Hybrid Programming of Java and Matlab
		8.2.2 Design and Implementation of Module Interface
		8.2.3 Design and Implementation of GUI for Image Mosaic
	8.3 Implementation of the Platform
	8.4 Summary
	References
Appendix Gramineous Grass
A. Elymus L.
A1. Elymus Dahuricus Turcz
A2. Elymus Nutans Griseb
A3. Elymus Cylindricus (Frmch) Hinda
A4. Elymus Sibiricus Linn
A5. Roegneria Kamoji Ohwi
A6. Roegneria Tunczaninovii(Drob.)Nevski Var.Macrathera Ohwi
A7. Roegneria Varia Keng
B. Agropyron Gaertn
B1. Agropyron Cristatum (L.) Gaertn
B2. Agropyron Cristatum (L.) Gaertn. Var. Pectiniforme (Roem. et Schult.) H. L. Yang
B3. Agropyron Mongolicum Keng
B4. Agropyron Desertorum (Fisch.) Schult
C. Bromus L
C1. Bromus Inermis Leyss
D. Stipa L
D1. Stipa Grandis P. Smirn
E. Leymus Hochst
E1. Leymus Chinensis(Trii)Tzvel