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Poplar Optimization Algorithm || Step-By-Step || ~xRay Pixy

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The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā€“ Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā€“ Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā€“ Strong branches grow ...
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Face Recognition using Local Binary Patterns (LBP) [2/2] ~xRay Pixy

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Face Recognition Using Local Binary Pattern (LBP)


Multi-Block LBP is popular in texture recognition and is used for facial features extraction and detection has been used. The local binary operator is used for the calculation of binary patterns in digital images. The extracted features of the input images are displayed using the binary image. Binary images used two-pixel values and color black and white. The calculation of the local binary pattern is shown in Figure 3. A comparison of every neighboring pixel is done with the center pixel is done. If the neighbor pixel value is greater or equal (>=) to the center pixel value than we will assign 1 and if the neighbor pixel is smaller (<) than the central pixel than assign 0. Steps to calculate the binary patterns for face facial feature extraction and face detection are given below:
Algorithm:

Multi-Block LBP is used to encode the rectangular regionā€™s intensity by using local binary patterns. Local Binary Pattern (LBP) looks at nine pixels at a time (i.e., 3x3 window of image = 9-pixel values and 2^9 = 512 possible values). By using Local Binary Pattern we can turn this 3x3 matrix into a single value. LBP focus on the very local neighborhood like 3x3. Multi-block LBP has been used for the large-scale structure. With the help of Multi-Block Local Binary Pattern (MB-LBP), 256 types of different binary patterns can be formed for edge detection and face detection from still images.

Face Recognition Using Local Binary Pattern (LBP)
Local Binary Pattern (LBP) Image Dataset Training for Face Recognition
Local Binary Pattern (LBP) Image Dataset Testing for Face Recognition
Local Binary Pattern (LBP) Feature Extraction, Local Binary Pattern (LBP) Histogram Construction.
Face Recognition using Local Binary Pattern (LBP) in MATLAB.

Local Binary Pattern (LBP) Videos | Projects

Local Binary Pattern (LBP) Image Dataset Training for Face Recognition [1/2] ~xRay Pixy

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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Grey Wolf Optimization Algorithm Numerical Example

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