Video 46: Object Classification using HOG features and ECOC Multi-Class SVM (With Matlab Code)

Hello Viewers, in this video, a multi-class object classification problem using HOG features is explained. To demonstrate the implementation, simple geometrical shapes (Circle, Square, Star and Triangle) are taken for classification. As a classifier, ECOC (Error Correcting Output Codes) based multi-class SVM is used. The shapes image database is obtained from Kaggle. 

The HOG feature is very popular and widely used for object detection in images. To understand the HOG feature computation, viewers are requested to watch my previous video of HOG feature computation.

This video includes following contents:

• Introduction.
• Proposed scheme for object Classification.
• Image Database Preparation.
• ECOC based Multi-Class SVM.
• Appropriate Cell Size selection for HOG feature.
• MATLAB Code for Shapes Classification (Multi-Class).
• MATLAB Code for Discrete Testing.

1. Link for previous video on HOG feature computation: Click Here

2. Link for Kaggle Dataset: https://www.kaggle.com/smeschke/four-shapes

3. Link to download original paper of N. Dalal and Bill Triggs:

https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf

Download Resources: 

1. Image Dataset (Modified): Download (Source: Kaggle)

2. Distorted Test Images: Download

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Video 45: HOG (Histogram of Oriented Gradients) Features (Theory and Implementation using MATLAB and Python)

Hello Viewers, in this video, Histogram of Oriented Gradients (HOG) is explained. This video tutorial includes, its theory and its implementation using both MATLAB and Python.

The HOG feature is very popular and widely used for object detection in images. This tutorial is based on the work proposed by Navneet Dalal and Bill Triggs.

This video includes following contents:

• Introduction.
• Finding Image Gradient.
• Getting Cell Orientation Histogram (Getting Bins).
• Making Blocks and Block Normalization.
• Getting HOG feature Vector.
• MATLAB Code for finding HOG feature vector.
• Python Code for finding HOG feature vector.

Link to download original paper of N. Dalal and Bill Triggs:

https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf

Download Resources: 

1. All Test Images used: Download

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Video 44: Walsh-Hadamard Transform (Signal Filtering and Image Compression)

Hello Viewers, in this video, Walsh-Hadamard Transform (WHT) is explained. This video tutorial includes, its theory, applications and implementation of signal filtering and Image compression using WHT in MATLAB.

The Walsh-Hadamard Transform is non-sinusoidal, orthogonal transform that is widely used in the areas of signals and image processing.

This video has following contents:

• Introduction and Applications.
• Forward and Inverse Walsh-Hadamard Transform (1-D).
• Hadamard Matrix and Walsh Matrix (Sequency and Dyadic Ordering).
• WHT for Images.
• Example: Computing WHT of 1-D signals.
• Example:  Computing WHT of 2-D signals.
• Fast WHT algorithm.
• MATLAB Code for filtering of Noisy ECG signal using WHT.
• MATLAB Code for Image Compression using WHT.

Download Resources: 

1. ECG Signal: Download

2. Lena Image: Download

3. Pepper Image: Download

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Video 43: Symmetrical Components Analysis of Unbalanced Three-Phase Vector (Theory and MATLAB Code)

Hello Viewers, in this video, Symmetrical Components Analysis of three-phase unbalanced voltage or current vectors is presented.

These symmetrical components are useful in solving unsymmetrical faults in power system. In this video, method of obtaining symmetrical components is explained and also a MATLAB program is implemented to do the same.

This video has following contents:

• Introduction (Symmetrical and Asymmetrical 3-phase vectors) .
• Positive Sequence, Negative Sequence and Zero Sequence Vectors.
• Operator ‘a’.
• Method of getting PS, NS and ZS components.
• Example Analysis.
• MATLAB Code to get symmetrical components.

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Video 42: Animated 2D and 3D Plots using MATLAB

Hello Viewers, in this video, It is explained that how one can create animated 2D and 3D plots using MATLAB.

This video tutorial shows the implementation of slow motion 2D, 3D curve plotting, Slow motion movement of data cursor with values, Recording animated plots as videos and 3D surface plots in slow motion.

This video has following contents:

• Why animated plots?
• 2D animated plots: Slow motion Plots (Basic approach).
• 2D animated plots: Slow motion Plots (With inbuilt functions).
• Slow moving Marker with values.
• Making video of animated plots.
• 3D animated plots (Basic approach).
• 3D animated plots (With inbuilt functions).

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Video 41: Detecting STOP Traffic Sign using Deep RCNN (Real time and Offline mode)

Hello Viewers, in this video, It is explained that how one can implement a deep RCNN for detecting 'STOP' traffic signs from image and videos in both offline and real time mode.

Also, it is shown that how one can use MATLAB's Labeler app to create database for training.

Here, transfer learning is used and for fine tuning, a pre trained RCNN is re trained on our own image dataset which is created with help of Image Labeler app.

This video has following contents:

• Introduction.
• What is Deep R-CNN?
• Proposed Scheme using Deep R-CNN.
• Understanding MATLAB’s Image Labeler for image database creation.
• MATLAB Code for training.
• MATLAB Code for testing.
• Code execution and result analysis.

Links of previous videos:

1. How to create a Deep CNN: Click Here

2. ECG signals classification using wavelet features and deep CNN: Click Here​

3. Implementing deep CNN in Python using TF and Keras (Face Mask detection): Click Here

Download Resources: 

1. Test Video: Download (Source: Mathworks.com)

2. Traffic Sign Image Database: Download

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