scholarly journals Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168565-168574
Author(s):  
Kunlin Zou ◽  
Luzhen Ge ◽  
Chunlong Zhang ◽  
Ting Yuan ◽  
Wei Li
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Support Vector ◽  
Color Texture

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

Automatic Fruit Disease Classification Using Images

2014 ◽  
pp. 82-100 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Support Vector Machine ◽  
Apple Scab ◽  
Texture Features ◽  
Disease Classification ◽  
Economic Losses ◽  
Support Vector ◽  
Test Case ◽  
Agricultural Industry ◽  
Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

Classification of Foot Thermograms using Texture Features and Support Vector Machine

2021 ◽  
Author(s):  
Josephine Selle J ◽  
K V M Vara Prakash ◽  
G Arun Sai ◽  
B Vinod ◽  
Kalaivani Chellappan
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Support Vector
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