Skip to main content

Ghaith T Hussein

Followers

0

Following

0

Public Views

University of Zagreb

Uppsala University

University of East London

University of Leicester

Gwen Robbins Schug

University of North Carolina at Greensboro

Gabriel Gutierrez-Alonso

University of Salamanca

Macquarie University

Universidade Federal do Rio Grande do Sul

Swansea University

Jesper Hoffmeyer

University of Copenhagen

Uploads

Papers by Ghaith T Hussein

Using Wavelet and Fast Discrete Curvelet Transform (FDCT) with (OTSU) Segmentation for Locating and Recognize Satellite Image Remote Sensing for Aircraft

This paper proposes the application different kinds of feature extractors technique to recognize ... more This paper proposes the application different kinds of feature extractors technique to recognize & locating the aircraft satellite image. Recognition of object (Aircraft) in an image based on the combination of feature extractors which contain of fast Fourier transform, discrete wavelet transform & discrete curvelet transform in addition to the correlation on shape analysis. Also an object can be recognized with help of texture or appearance features through Scale invariant feature transform (Wavelet Transform) and OTSU (Multi Scale segmentation), To justify the correct amount of each feature extractor, we perform per of the mentioned transforms to input images, precisely. The used classifier in this paper using Detect Fuzzy Clustering and the results of this test show, that the right recognition rate of aircraft in this recognition system, at the time of using curve let transform and all curvelet coefficients is 100%. For decreasing the dimension of feature vectors more & choosing ...

Using Wavelet and Fast Discrete Curvelet Transform (FDCT) with (OTSU) Segmentation for Locating and Recognize Satellite Image Remote Sensing for Aircraft

This paper proposes the application different kinds of feature extractors technique to recognize ... more This paper proposes the application different kinds of feature extractors technique to recognize & locating the aircraft satellite image. Recognition of object (Aircraft) in an image based on the combination of feature extractors which contain of fast Fourier transform, discrete wavelet transform & discrete curvelet transform in addition to the correlation on shape analysis. Also an object can be recognized with help of texture or appearance features through Scale invariant feature transform (Wavelet Transform) and OTSU (Multi Scale segmentation), To justify the correct amount of each feature extractor, we perform per of the mentioned transforms to input images, precisely. The used classifier in this paper using Detect Fuzzy Clustering and the results of this test show, that the right recognition rate of aircraft in this recognition system, at the time of using curve let transform and all curvelet coefficients is 100%. For decreasing the dimension of feature vectors more & choosing the best features we've used of interclass variance criteria to intra class variance criteria. As a result of this performance, the size of feature vectors will be extremely decreased. Then, we perform our final impact feature vectors (the best curvelet coefficients or the best wavelet coefficients or the best Fourier coefficients) to the fuzzy and neural network.

Using Wavelet and Fast Discrete Curvelet Transform (FDCT) with (OTSU) Segmentation for Locating and Recognize Satellite Image Remote Sensing for Aircraft

This paper proposes the application different kinds of feature extractors technique to recognize ... more This paper proposes the application different kinds of feature extractors technique to recognize & locating the aircraft satellite image. Recognition of object (Aircraft) in an image based on the combination of feature extractors which contain of fast Fourier transform, discrete wavelet transform & discrete curvelet transform in addition to the correlation on shape analysis. Also an object can be recognized with help of texture or appearance features through Scale invariant feature transform (Wavelet Transform) and OTSU (Multi Scale segmentation), To justify the correct amount of each feature extractor, we perform per of the mentioned transforms to input images, precisely. The used classifier in this paper using Detect Fuzzy Clustering and the results of this test show, that the right recognition rate of aircraft in this recognition system, at the time of using curve let transform and all curvelet coefficients is 100%. For decreasing the dimension of feature vectors more & choosing ...

Using Wavelet and Fast Discrete Curvelet Transform (FDCT) with (OTSU) Segmentation for Locating and Recognize Satellite Image Remote Sensing for Aircraft

This paper proposes the application different kinds of feature extractors technique to recognize ... more This paper proposes the application different kinds of feature extractors technique to recognize & locating the aircraft satellite image. Recognition of object (Aircraft) in an image based on the combination of feature extractors which contain of fast Fourier transform, discrete wavelet transform & discrete curvelet transform in addition to the correlation on shape analysis. Also an object can be recognized with help of texture or appearance features through Scale invariant feature transform (Wavelet Transform) and OTSU (Multi Scale segmentation), To justify the correct amount of each feature extractor, we perform per of the mentioned transforms to input images, precisely. The used classifier in this paper using Detect Fuzzy Clustering and the results of this test show, that the right recognition rate of aircraft in this recognition system, at the time of using curve let transform and all curvelet coefficients is 100%. For decreasing the dimension of feature vectors more & choosing the best features we've used of interclass variance criteria to intra class variance criteria. As a result of this performance, the size of feature vectors will be extremely decreased. Then, we perform our final impact feature vectors (the best curvelet coefficients or the best wavelet coefficients or the best Fourier coefficients) to the fuzzy and neural network.