A Comparative Study of Texture Features for the Discrimination of Gastric Polyps in Endoscopic Video

2005 ◽  
Author(s):  
D.K. Iakovidis ◽  
D.E. Maroulis ◽  
S.A. Karkanis ◽  
A. Brokos
Keyword(s):  
Comparative Study ◽  
Texture Features ◽  
Gastric Polyps ◽  
Endoscopic Video

scholarly journals A comparative study of different texture features for document image retrieval

2019 ◽  
Vol 121 ◽  
pp. 97-114 ◽  
Author(s):  
Fahimeh Alaei ◽  
Alireza Alaei ◽  
Umapada Pal ◽  
Michael Blumenstein
Keyword(s):  
Comparative Study ◽  
Image Retrieval ◽  
Texture Features ◽  
Document Image

Writer identification using texture features: A comparative study

2018 ◽  
Vol 71 ◽  
pp. 1-12 ◽  
Author(s):  
Priyanka Singh ◽  
Partha Pratim Roy ◽  
Balasubramanian Raman
Keyword(s):  
Comparative Study ◽  
Texture Features ◽  
Writer Identification

A Comparative Study of Characteristics of Gastric Polyps and Colorectal Polyps in Chinese Patients

2015 ◽  
Vol 110 ◽  
pp. S1035
Author(s):  
Endian Zheng ◽  
Liang Zheng ◽  
Ying Wang ◽  
Qiaoli Lan ◽  
Qiang Cai
Keyword(s):  
Comparative Study ◽  
Colorectal Polyps ◽  
Chinese Patients ◽  
Gastric Polyps

Text Localisation for Roman Words from Shop Signage

2017 ◽  
Vol 14 (2) ◽  
pp. 49
Author(s):  
Nurbaity Sabri ◽  
Noor Hazira Yusof ◽  
Zaidah` Ibrahim ◽  
Zolidah Kasiran ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Comparative Study ◽  
Kernel Function ◽  
Texture Features ◽  
Text Recognition ◽  
Primary Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Speeded Up Robust Features ◽  
Better Than

Text localisation determines the location of the text in an image. This process is performed prior to text recognition. Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF). A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Results indicate that HOG with quadratic kernel function localises text for shop signage better than SURF.

A Comparative analysis of stroke diagnosis from retinal images using hand-crafted features and CNN

2021 ◽  
pp. 1-9
Author(s):  
R. S Jeena ◽  
G. Shiny ◽  
A. Sukesh Kumar ◽  
K. Mahadevan
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Early Detection ◽  
Comparative Study ◽  
Texture Features ◽  
Detection Algorithm ◽  
Developing Nations ◽  
Retinal Images ◽  
Fundus Images ◽  
Stroke Diagnosis

Stroke is a major reason for disability and mortality in most of the developing nations. Early detection of stroke is highly significant in bio-medical research. Research illustrates that signs of stroke are reflected in the eye and may be analyzed from fundus images. A custom dataset of fundus images has been compiled for formulating an automated stroke detection algorithm. In this paper, a comparative study of hand-crafted texture features and convolutional neural network (CNN) has been recommended for stroke diagnosis. The custom CNN model has also been compared with five pre-trained models from ImageNet. Experimental results reveal that the recommended custom CNN model gives the best performance by achieving an accuracy of 95.8 %.

Gastric polyps detection based on endoscopic video using modified dense micro-block difference descriptor

2020 ◽  
Author(s):  
Marina Zhdanova ◽  
Viacheslav V. Voronin ◽  
Evgenii Semenishchev ◽  
Oxana Balabaeva ◽  
Alexander Zelensky
Keyword(s):  
Gastric Polyps ◽  
Endoscopic Video
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