Automatic Shadow Direction Determination using Shadow Low Gradient Direction Feature in RGB VHR Remote Sensing Images

M. Kakooei; Y. Baleghi

Volume 10, Issue 1 , January 2022, , Pages 53-61

http://dx.doi.org/10.22044/jadm.2021.10705.2205

Abstract
  Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very ...  Read More

H.3. Artificial Intelligence
MEFUASN: A Helpful Method to Extract Features using Analyzing Social Network for Fraud Detection

Z. Karimi Zandian; M. R. Keyvanpour

Volume 7, Issue 2 , April 2019, , Pages 213-224

http://dx.doi.org/10.22044/jadm.2018.6311.1746

Abstract
  Fraud detection is one of the ways to cope with damages associated with fraudulent activities that have become common due to the rapid development of the Internet and electronic business. There is a need to propose methods to detect fraud accurately and fast. To achieve to accuracy, fraud detection methods ...  Read More

H.3.2.2. Computer vision
Camera Pose Estimation in Unknown Environments using a Sequence of Wide-Baseline Monocular Images

Seyyed A. Hoseini; P. Kabiri

Volume 6, Issue 1 , March 2018, , Pages 93-103

http://dx.doi.org/10.22044/jadm.2017.976

Abstract
  In this paper, a feature-based technique for the camera pose estimation in a sequence of wide-baseline images has been proposed. Camera pose estimation is an important issue in many computer vision and robotics applications, such as, augmented reality and visual SLAM. The proposed method can track captured ...  Read More

H.6.3.2. Feature evaluation and selection
Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

M. Imani; H. Ghassemian

Volume 5, Issue 1 , March 2017, , Pages 39-53

http://dx.doi.org/10.22044/jadm.2017.787

Abstract
  Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it ...  Read More

H.6.3.2. Feature evaluation and selection
Feature extraction in opinion mining through Persian reviews

E. Golpar-Rabooki; S. Zarghamifar; J. Rezaeenour

Volume 3, Issue 2 , July 2015, , Pages 169-179

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.02.06

Abstract
  Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. In general, opinion mining extracts user reviews at three levels of document, sentence and feature. ...  Read More

H.6.3.3. Pattern analysis
Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery

M. Imani; H. Ghassemian

Volume 3, Issue 2 , July 2015, , Pages 181-190

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.02.07

Abstract
  Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very ...  Read More

Feature selection using genetic algorithm for classification of schizophrenia using fMRI data

Hossein Shahamat; Ali A. Pouyan

Volume 3, Issue 1 , March 2015, , Pages 30-37

http://dx.doi.org/10.5829/idosi.JAIDM.2015.03.01.04

Abstract
  In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component ...  Read More