Diagnosis of Schizophrenia Disorder Using Wasserstein Based Active Contour and Texture Features

2018 ◽  
pp. 23-32
Author(s):  
M. Latha ◽  
G. Kavitha
Keyword(s):  
Active Contour ◽  
Texture Features

Texture Segmentation of Natural Images Based on Active Contour Model

2012 ◽  
Vol 546-547 ◽  
pp. 553-558
Author(s):  
Zhan Wang ◽  
Yun Hui Yan ◽  
De Wei Dong ◽  
Ke Chen Song
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Synthesis ◽  
Texture Features ◽  
Texture Segmentation ◽  
Natural Images ◽  
Contour Model ◽  
Signal Features ◽  
Environmental Image

To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.

Feature Based Classification of Melanoma From Skin Images

2015 ◽  
Author(s):  
Jiexin Guo ◽  
Prahlad G. Menon
Keyword(s):  
Active Contour ◽  
Texture Features ◽  
Automatic Feature Extraction ◽  
Contour Segmentation ◽  
Classification Framework ◽  
Skin Cancers ◽  
Feature Based ◽  
Fold Cross Validation ◽  
Validation Strategy

Melanoma is one of the most deadly skin cancers and amounts for ∼79% of skin cancer deaths. Early detection and timely therapeutic action can reduce mortality owing to melanoma. In this study, we demonstrate the feasibility of our in-house skin image classification framework, trained based on a library of normal as well as pathological skin images, for automatic feature extraction and detection of melanoma. The described framework begins with active contour segmentation the skin images followed by extraction of both color and texture features from the segmented image and employs a neural network classifier to for trained identification of melanoma cases. Training and testing was conducted using a 10-fold cross validation strategy and led to 88.06% ± 1.65% accuracy in classification of melanoma images.

An active contour model based on fused texture features for image segmentation

2015 ◽  
Vol 151 ◽  
pp. 1133-1141 ◽  
Author(s):  
Qinggang Wu ◽  
Yong Gan ◽  
Bin Lin ◽  
Qiuwen Zhang ◽  
Huawen Chang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Features ◽  
Model Based ◽  
Contour Model

Distinction of Wet Road Surface Condition at Night-time using Texture Features

2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface ◽  
Night Time

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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