Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

2008 ◽  
Author(s):  
Namkug Kim ◽  
Joon Beom Seo ◽  
Yu Sub Sung ◽  
Bum-Woo Park ◽  
Youngjoo Lee ◽  
...  
Keyword(s):  
Classification System ◽  
Lung Diseases ◽  
Texture Features ◽  
Automatic Classification

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.

scholarly journals Towards an automatic classification system for supporting the development of critical reflective skills in L2 learning

2016 ◽  
Author(s):  
Gary Cheng
Keyword(s):  
Undergraduate Students ◽  
Classification System ◽  
Automatic Classification ◽  
Structured Interview ◽  
Active System ◽  
Online Questionnaire ◽  
Classification Framework ◽  
L2 Learning ◽  
Students Attitudes ◽  
Reflective Skills

This study aimed to develop an automatic classification system, namely ACTIVE, for generating immediate and individualised feedback on students’ reflective entries about their second language (L2) learning experiences. It also aimed to explore students’ attitudes towards using the system to support the development of their reflective skills in L2 learning. A total of 466 undergraduate students took part in the study. One hundred and twenty-seven participants were involved in the development phase, where their reflective entries were manually annotated according to a classification framework for critical reflection on L2 learning, and the annotated entries were then used to develop the ACTIVE system. The remaining participants were asked to generate automated feedback reports on their reflective entries for improvement by using the system. To solicit their views towards the system, the participants were administered an online questionnaire and some of them were also invited to attend a semi-structured interview. The overall results indicate that the classification accuracy of the system is comparable to that of human annotators. They also suggest that both teacher and machine feedback types have strengths and limitations, highlighting the need to further explore the use of multi-channel, multi-layer feedback in improving students’ reflective skills in L2 learning.

Ensemble Classification System for Scientific Chart Recognition from PDF Files

2012 ◽  
Vol 2 (4) ◽  
pp. 1-10 ◽  
Author(s):  
S. Nagarajan ◽  
V. Karthikeyani
Keyword(s):  
Feature Extraction ◽  
Classification System ◽  
Texture Features ◽  
Portable Document Format ◽  
Ensemble Classification ◽  
3 Dimensional ◽  
Feature Based ◽  
Separation Feature ◽  
Object Features ◽  
Document Format

Portable Document Format (PDF) is the most frequently used universal document format on the Internet and E-Publishing. Wide usage of PDF files has increased the need of conversion tools that convert PDF file content to text or HTML formats. A PDF converter can be categorized into two domains, namely, text recognition and graphics recognition. This paper focus on graphic recognition, especially chart type identification, which is concerned with developing algorithms that has the ability to determine the type of a given chart image from a PDF file. In the proposed system, initially an enhanced connected component and statistical feature based method is used to separate the chart region from other regions. The chart region is then analyzed and grouped as either 2-dimensional or 3-dimensional chart. After separating the graphic component from the text components, feature extraction is performed. The features can be grouped as object features, texture features and shape features. The combined feature vector is then classified using ensemble classification system. Experimental results show that the chart separation, feature extraction and ensemble classification models significantly improve the quality of chart identification.

scholarly journals Hierarchical Automatic Classification System of Electronic Parts

2006 ◽  
Vol 126 (12) ◽  
pp. 1447-1453 ◽  
Author(s):  
Hirotake Esaki ◽  
Kiyoyuki Kagii ◽  
Taizo Umezaki ◽  
Tetsumi Horikoshi
Keyword(s):  
Classification System ◽  
Automatic Classification ◽  
Electronic Parts
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