scholarly journals Plant Leaf Image Reconstruction Based on Point Cloud Characteristics

2019 ◽  
Author(s):  
Ling LU ◽  
Wen-li WANG ◽  
Xing-xiao GENG ◽  
Li-hua LI ◽  
Lei WANG
Keyword(s):  
Image Reconstruction ◽  
Point Cloud ◽  
Plant Leaf ◽  
Cloud Characteristics ◽  
Leaf Image

An Improved Leaf Area Measurement Based on Image Processing

2012 ◽  
Vol 182-183 ◽  
pp. 624-628
Author(s):  
Dian Yuan Han
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Good Accuracy ◽  
Area Measurement ◽  
Scanning Method ◽  
Plant Leaf ◽  
Unknown Area ◽  
Leaf Image ◽  
Background Area

This paper concerns the plant leaf area measurement based on improved image processing. Firstly, the referenced rectangle was detected with 2-side scanning method. Then the leaf region was segmented according to 2G-R-B of every pixel with two different thresholds, and by using of dilatation operation, the trimap of leaf image was got. Next the pixels in unknown area were classified to the foreground or background area with improved knockout method and the exact leaf was segmented. Lastly, the leaf area was calculated according to the pixels proportion between leaf region and the referenced rectangle. Experiment results show this method has good accuracy and rapid speed.

scholarly journals Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021 ◽  
Vol 8 ◽  
Author(s):  
Sufola Das Chagas Silva Araujo ◽  
V. S. Malemath ◽  
K. Meenakshi Sundaram
Keyword(s):  
Feature Learning ◽  
Region Of Interest ◽  
Physical Method ◽  
Image Database ◽  
Features Extraction ◽  
Plant Leaves ◽  
Rotation Invariant ◽  
Plant Leaf ◽  
Plant Image ◽  
Leaf Image

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

scholarly journals A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering

2012 ◽  
Vol 44 (13) ◽  
pp. 10-20 ◽  
Author(s):  
N. Valliammal ◽  
S.N.Geethalakshmi S.N.Geethalakshmi
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Plant Leaf ◽  
Novel Approach ◽  
Leaf Image

Voxelization based PoCA point cloud filtration algorithm for image reconstruction for Muon Tomography

2020 ◽  
Vol 15 (09) ◽  
pp. P09012-P09012
Author(s):  
R. Sehgal ◽  
M. Sengupta Mitra ◽  
Tushar Roy ◽  
S.T. Sehgal ◽  
L.M. Pant ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Point Cloud ◽  
Muon Tomography ◽  
Filtration Algorithm

scholarly journals Venation Skeleton-Based Modeling Plant Leaf Wilting

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Shenglian Lu ◽  
Chunjiang Zhao ◽  
Xinyu Guo
Keyword(s):  
Surface Deformation ◽  
Leaf Surface ◽  
Three Dimensional ◽  
Plant Leaf ◽  
Biological Responses ◽  
Leaf Image

A venation skeleton-driven method for modeling and animating plant leaf wilting is presented. The proposed method includes five principal processes. Firstly, a three-dimensional leaf skeleton is constructed from a leaf image, and the leaf skeleton is further used to generate a detailed mesh for the leaf surface. Then a venation skeleton is generated interactively from the leaf skeleton. Each vein in the venation skeleton consists of a segmented vertices string. Thirdly, each vertex in the leaf mesh is banded to the nearest vertex in the venation skeleton. We then deform the venation skeleton by controlling the movement of each vertex in the venation skeleton by rotating it around a fixed vector. Finally, the leaf mesh is mapped to the deformed venation skeleton, as such the deformation of the mesh follows the deformation of the venation skeleton. The proposed techniques have been applied to simulate plant leaf surface deformation resulted from biological responses of plant wilting.

Fine-Grained Plant Leaf Image Retrieval Using Local Angle Co-occurrence Histograms

2021 ◽  
Author(s):  
Xin Chen ◽  
Jiawei You ◽  
Hui Tang ◽  
Bin Wang ◽  
Yongsheng Gao
Keyword(s):  
Image Retrieval ◽  
Plant Leaf ◽  
Fine Grained ◽  
Local Angle ◽  
Leaf Image

ORTHOGONAL MAXIMUM MARGIN DISCRIMINANT PROJECTION WITH APPLICATION TO LEAF IMAGE CLASSIFICATION

2014 ◽  
Vol 28 (04) ◽  
pp. 1450010 ◽  
Author(s):  
SHAN-WEN ZHANG ◽  
XIANFENG WANG ◽  
CHUANLEI ZHANG
Keyword(s):  
Image Data ◽  
Original Data ◽  
Dimensional Subspace ◽  
Neighborhood Density ◽  
Linear Discriminant ◽  
Plant Leaf ◽  
Maximum Margin ◽  
Data Points ◽  
Leaf Image ◽  
Discriminant Projection

A novel supervised dimensionality reduction method called orthogonal maximum margin discriminant projection (OMMDP) is proposed to cope with the high dimensionality, complex, various, irregular-shape plant leaf image data. OMMDP aims at learning a linear transformation. After projecting the original data into a low dimensional subspace by OMMDP, the data points of the same class get as near as possible while the data points of the different classes become as far as possible, thus the classification ability is enhanced. The main differences from linear discriminant analysis (LDA), discriminant locality preserving projections (DLPP) and other supervised manifold learning-based methods are as follows: (1) In OMMDP, Warshall algorithm is first applied to constructing both of the must-link and class-class scatter matrices, whose process is easily and quickly implemented without judging whether any pairwise points belong to the same class. (2) The neighborhood density is defined to construct the objective function of OMMDP, which makes OMMDP be robust to noise and outliers. Experimental results on two public plant leaf databases clearly demonstrate the effectiveness of the proposed method for classifying leaf images.

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