Video 69: Gray Level Co-occurrence Matrix (GLCM)

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Hello viewers. In this video, a very popular texture descriptor Gray Level Co-occurrence Matrix (GLCM) is explained. Here its basic theory, calculation method, its performance analysis and MATLAB implementation is given.  

This video includes following components,

* Introduction to Gray Level Co-occurrence Matrix (GLCM).

* Finding GLCM.

* GLCM size reduction.

* Statistical features of GLCM.

* MATLAB Implementation and result analysis.

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Link to Haralick's Paper: https://ieeexplore.ieee.org/document/4309314

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Video 68: Texture Classification using LBP

 

Hello viewers. In this video, a very popular texture descriptor Local Binary Pattern (LBP) is utilized for texture classification. For classification, three very popular databases are considered, Surotex, Kyberge and FourShapes. The classification task is fully implemented in MATLAB and simulation results show the strength of LBP as feature vector. This video has following contents.

• Introduction to Local Binary Pattern (LBP).
• Image databases used.
• Training and testing procedures.
• Implementation in MATLAB (MATLAB Code).
• Simulation outcomes.

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Link of previous video,

https://www.exptech.co.in/2025/08/video-67-local-binary-pattern-lbp.html

Link of Ojala's Paper,

https://drive.google.com/file/d/1g7vsvA4uog-t8UvB4HREJYc-nMtTbA3X/view?usp=sharing

All Databases links.

1. SUROTEX: Download

2. KEYBERGE: Download

3. FOURSHAPES: Download

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Video 67: Local Binary Pattern (LBP)

 

Hello viewers. In this video, a very popular texture descriptor Local Binary Pattern (LBP) is explained. Here its basic theory, calculation method, its performance analysis and MATLAB implementation is given.  This video includes following components,

• Introduction to Local Binary Pattern (LBP).

• How an LBP feature is obtained from an image.

• Implementation of LBP in MATLAB (with MATLAB Code).

• Comparative performance analysis of LBP.

• LBP as feature vector for Machine Learning applications.

Download Timo Ojala Paper: Click here

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Video 66: Evolution of Laplace and Z-Transform from Fourier Transform

 

Hello viewers. In this video, the evolution of Laplace transform, and Z-transform from Fourier transform is explained. It is explained how the problem of non-convergence of Fourier for some signals is overcome by Laplace and Z-transform for continuous time and discrete time signals respectively.

This lecture includes following points.

• Fourier Transform review (CTFT and DTFT).
• Problem with Fourier Transform.
• Birth of Laplace Transform.
• Birth of Z-Transform.

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Video 65: Texture Classification using Wavelet Scattering Transform (with MATLAB Code)

 

Hello viewers. In this video, Texture Classification is presented based on Wavelet Scattering Transform (WST). WST is also briefly explained. This lecture explains that how WST coefficients can be used as feature vectors for classification task. Texture images are taken from KTH_TIPS and KYBERGE image databases. 

This video includes following components,

* Brief introduction to Wavelet Scattering Transform (WST).

* Computing Image Features (WST Coefficients as Features).

* Texture Image Databases used.

* Training Algorithm.

* Testing Procedure.

* MATLAB Implementation (with MATLAB Code).

Download:

Texture Image Database: Download

Link of previous video:

1. Introduction to Wavelet Theory and Its Applications: Click Here

2. Wavelet Scattering Transform for Signals and Images: Click Here

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Video 64: Wavelet Scattering Transform (WST) for Signals and Images (with MATLAB code)

 

Hello Viewers. In this video, Wavele Scattering Transform (WST) is explained for both 1D and 2D signals. This lectures explains that how WST coefficients can be computed for 1D and 2D signals. The MATLAB implementation is disscussed. MATLAB code is given and well explained for both the cases of 1D and 2D signals.

This video includes following components,

* Introduction to Wavelet Scattering Transform (WST).

* Computing WST coefficients of 1D signal.

* WST network similarity as CNN and WST coefficients as feature vector.

* WST for images (2D signals).

* MATLAB implementation (with MATLAB code).

Link of previous video:

Introduction to Wavelet Theory and Its Applications: Click Here

Download:

Test Images and Audio: Click Here

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