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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Video 63: Comparison of Threshold Estimation Methods for Wavelet based Denoising of Audio Signals (with MATLAB Code)

 

Hello Viewers. In this video, a comparative study is shown to help us in selecting best combination of thresholding method, wavelet function and level of decomposition for denoising of audio of some Indian musical instruments.

This video includes following components,

• Introduction to denoising using wavelets.
• Various Noise estimation and Threshold Selection methods.
• MATLAB implementation (with MATLAB code).
• Applying these methods on audio of some Indian musical instruments.
• Comparative study and Result Analysis.

Wavelet transform is a very powerful tool in the field of Signal Denoising. It gives far better denoising results as compared to frequency selective filters.

Links of previous videos.

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

2. Wavelet based denoising of audio signals using MATLAB and SIMULINL: Click Here

3.  Wavelet Based Denoising of 1D Signals using Python: Click Here

Download Audio Files

Click Here

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Video 62: Color Edge Features and DWT based Image Retrieval (With MATLAB Code)

 

Hello viewers, in this video, Content Based Image Retrieval (CBIR) is implemented. This CBIR utilizes both the color and edge features of the images. For this purpose, Color Edge Histograms are obtained. To reduce the size of feature vector, Discrete Wavelet Transform (DWT) is also used. The simulation results show the effectiveness of the proposed algorithm for effective CBIR.     

 

This video includes following contents, 

* Introduction to Content Based Image Retrieval (CBIR).

* Color Edge Feature (Proposed  Algorithm).

* Finding Feature Vector (Training Process).

* Testing Process.

* MATLAB implementation (with MATLAB code).

* Result Analysis.

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1. Previous video:

   Color Layout Descriptor (CLD) of MPEG7 for Image Retrieval: Click Here

   

2. Previous video:

   Edge Histogram Descriptor (EHD) of MPEG7 for Image Retrieval: Click Here

   

3. Previous video:

   Content Based Image Retrieval (CBIR) using Wavelet features, CLD and EHD of MPEG7: Click Here

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Download Resources:

Image Database: Click Here

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Video 61: Time Series Prediction using ANFIS (With MATLAB Code)

 

Hello viewers, in this video, The Time Series Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS) is explained. The time series taken here is Mackey-Glass chaotic time series, which is considered as benchmark problem. The ANFIS based algorithm for time series prediction is explained in detail.     

 

This video includes following contents, 

* Introduction to time-series prediction.

* ANFIS for time-series prediction.

* Mackey-Glass chaotic time series (A benchmark).

* Time series prediction algorithm using ANFIS.

* MATLAB implementation (with MATLAB code).

* Result Analysis.

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1. Previous video:

"Fuzzy Logic Controller (FLC)": Click Here

2. Previous video:

"ANFIS (Adaptive Neuro Fuzzy Inference System)": Click Here

 

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Video 60: ANFIS: Neuro-Fuzzy Inference System (Theory and MATLAB Implementation)

 

Hello viewers, in this video, The Neuro-Fuzzy modelling highlighting ANFIS is explained. The basic theory of ANFIS is presented. Also the complete process of MATLAB implementation is given. In MATLAB implementation, the ANFIS is used as universal approximator. The two functions 1D sin(t) and 2D sin(r)/r are realized using ANFIS.   

 This video includes following contents, 

• Neuro – Fuzzy Modelling.
• Adaptive Neuro-Fuzzy Inference System (ANFIS).
• ANFIS Architecture.
• ANFIS Hybrid learning algorithm.
• ANFIS Applications.
• ANFIS as Universal Approximator (UA).
• MATLAB Implementation of ANFIS as UA (with MATLAB code).

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1. Previous video:

"Fuzzy Logic Controller (FLC)": Click Here

2. Link for research paper of Jang: Click Here 

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