Computer Vision-Become an ace of Computer Vision, Computer Vision for Apps using Python, OpenCV, TensorFlow, etc.

What you'll learn
• The introduction and importance of Computer Vision (CV).
• Why is CV such a popular field nowadays?
• The fundamental concepts from the absolute beginning with comprehensive unfolding with examples in Python.
• Practical explanation and live coding with Python.
• The concept of colored and black and white images with practice.
• Deep details of Computer Vision with examples of every concept from scratch.
• TensorFlow (Deep learning framework by Google).
• The use and applications of state-of-the-art Computer Vision (with implementations in state-of-the-art framework Numpy and TensorFlow).
• Theory and implementation of Panoramic images.
• Geometric transformations.
• Image Filtering with implementation in Python.
• Edge Detection, Shape Detection, and Corner Detection.
• Object Tracking and Object detection.
• 3D images.
• Building your own applications for change detection in the live feed of cameras by using Computer Vision Techniques using Python.
• Developing a complete project to make a very intelligent and efficient DVR using Python.
Requirements
• No prior knowledge is needed. You will start from the basics and slowly build your knowledge in computer vision.
• A willingness to learn and practice.
• Knowledge of Python will be a plus.
• Since we teach by practical implementations, practice is a must.
Description
Comprehensive Course Description:Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. In the CV process, real-world images and videos are captured, processed, and analyzed to allow machines to extract contextual, useful information from the physical world.Until recently, computer vision functioned in a limited capacity. But due to the recent innovations in artificial intelligence and deep learning, this field has made great leaps. Today, CV surpasses humans in most routine tasks connected with detecting and labeling objects.The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you to understand the digital imaging process and identify the key application areas of CV. The course is:· Easy to understand.· Descriptive.· Comprehensive.· Practical with live coding.· Rich with state of the art and updated knowledge of this field.Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The Homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-time) and Smart DVRs (Real-time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.The course tutorials are divided into 320+ videos along with detailed code notebooks. The videos are available in HD, and the total runtime of the videos is 27 hours+.Now is the perfect time to learn computer vision. Get started with this best-in-class course without any further delay!Teaching is our passion:In this course, we apply the proven learning by doing methodology. We build the interest of learners first. We start from the basics and focus on helping you understand each concept clearly. The explanation of each theoretical concept is followed by practical implementation. We then encourage you to create something new out of your learning.Our aim is to help you master the basic concepts of CV before moving onward to advanced concepts. The course material includes online videos, course notes, hands-on exercises, project work, quizzes, and handouts. We also offer you learning support. You can approach our team in case of any queries, and we respond in quick time.Course Content:The comprehensive course consists of the following topics:1. Introductiona. Introi. What is computer vision?2. Image Transformationsa. Introduction to imagesi. Image data structureii. Color imagesiii. Grayscale imagesiv. Color spacesv. Color space transformations in OpenCVvi. Image segmentation using Color space transformationsb. 2D geometric transformationsi. Scalingii. Rotationiii. Sheariv. Reflectionv. Translationvi. Affine transformationvii. Projective geometryviii. Affine transformation as a matrixix. Application of SVD (Optional)x. Projective transformation (Homography)c. Geometric transformation estimationi. Estimating affine transformationii. Estimating Homographyiii. Direct linear transform (DLT)iv. Building panoramas with manual key-point selection3. Image Filtering and Morphologya. Image Filteringi. Low pass filterii. High pass filteriii. Band pass filteriv. Image smoothingv. Image sharpeningvi. Image gradientsvii. Gaussian filterviii. Derivative of Gaussiansb. Morphologyi. Image Binarizationii. Image Dilationiii. Image Erosioniv. Image Thinning and skeletonizationv. Image Opening and closing4. Shape Detectiona. Edge Detectioni. Definition of edgeii. Naïve edge detectoriii. Canny edge detector1. Efficient gradient computations2. Non-maxima suppression using gradient directions3. Multilevel thresholding- hysteresis thresholdingb. Geometric Shape detectioni. RANSACii. Line detection through RANSACiii. Multiple lines detection through RANSACiv. Circle detection through RANSACv. Parametric shape detection through RANSACvi. Hough transformation (HT)vii. Line detection through HTviii. Multiple lines detection through HTix. Circle detection through HTx. Parametric shape detection through HTxi. Estimating affine transformation through RANSACxii. Non-parametric shapes and generalized Hough transformation5. Key Point Detection and Matchinga. Corner detection (Key point detection)i. Defining Cornerii. Naïve corner detectoriii. Harris corner detector1. Continuous directions2. Tayler approximation3. Structure tensor4. Variance approximation5. Multi-scale detectionb. Project: Building automatic panoramasi. Automatic key point detectionii. Scale assignmentiii. Rotation assignmentiv. Feature extraction (SIFT)v. Feature matchingvi. Image stitching6. Motiona. Optical Flow, Global Flowi. Brightness constancy assumptionii. Linear approximationiii. Lucas–Kanade methodiv. Global flowv. Motion segmentationb. Object Trackingi. Histogram based trackingii. KLT trackeriii. Multiple object trackingiv. Trackers comparisons7. Object detectiona. Classical approachesi. Sliding windowii. Scale spaceiii. Rotation spaceiv. Limitationsb. Deep learning approachesi. YOLO a case study8. 3D computer visiona. 3D reconstructioni. Two camera setupsii. Key point matchingiii. Triangulation and structure computationb. Applicationsi. Mocapii. 3D Animations9. Projectsa. Change detection in CCTV cameras (Real-time)b. Smart DVRs (Real-time)After completing this course successfully, you will be able to:· Relate the concepts and theories in computer vision with real-world problems.· Implement any project from scratch that requires computer vision knowledge.· Know the theoretical and practical aspects of computer vision concepts.Who this course is for:· Learners who are absolute beginners and know nothing about Computer Vision.· People who want to make smart solutions.· People who want to learn computer vision with real data.· People who love to learn theory and then implement it using Python.· People who want to learn computer vision along with its implementation in realistic projects.· Data Scientists.· Machine learning experts.

Overview

Section 1: Introduction to Course and Instructor

Lecture 1 Why Computer Vision

Lecture 2 Introduction to Instructor

Lecture 3 About AI Sciences

Lecture 4 Course Outline (Optional)

Lecture 5 Methodology

Lecture 6 Computer Vision Applications

Lecture 7 Final Project

Lecture 8 Request for Your Honest Review

Lecture 9 Github & OneDrive Link to get the Course Materials

Section 2: Introduction to Images

Lecture 10 Github & OneDrive Link to get the Course Materials

Lecture 11 Grayscale Image

Lecture 12 Quiz(Grayscale Image)

Lecture 13 Solution(Grayscale Image)

Lecture 14 Python Warning

Lecture 15 Grayscale Spectrum

Lecture 16 Answer to Question

Lecture 17 Reading, Manipulating and Saving Grayscale Image using Matplotlib Python

Lecture 18 Quiz(Reading, Manipulating and Saving Grayscale Image using Matplotlib Python)

Lecture 19 Solution(Reading, Manipulating and Saving Grayscale Image using Matplotlib Python)

Lecture 20 Reading, Manipulating and Saving Grayscale Image using OpenCV Python

Lecture 21 Introduction to RGB Images

Lecture 22 Quiz(Introduction to RGB Images)

Lecture 23 Solution(Introduction to RGB Images)

Lecture 24 RGB Color Images Matplotlib and OpenCV

Lecture 25 Quiz(RGB Color Images Matplotlib and OpenCV)

Lecture 26 Solution(RGB Color Images Matplotlib and OpenCV)

Lecture 27 RGB to HSV theory and Algorithm

Lecture 28 RGB to HSV Algorithm Implementation using Python

Lecture 29 Quiz(RGB to HSV Algorithm Implementation using Python)

Lecture 30 Solution(RGB to HSV Algorithm Implementation using Python)

Lecture 31 Red Rose Extraction or Segmentation using HSV Python

Lecture 32 Quiz(Red Rose Extraction or Segmentation using HSV Python)

Lecture 33 Solution(Red Rose Extraction or Segmentation using HSV Python)

Lecture 34 Hyper Spectral Images

Section 3: 2D Scaling Transformations

Lecture 35 Github & OneDrive Link to get the Course Materials

Lecture 36 Introduction to Geometric Transformations

Lecture 37 Scaling Example in OpenCV

Lecture 38 Quiz(Scaling Example in OpenCV)

Lecture 39 Solution(Scaling Example in OpenCV)

Lecture 40 Scaling in Real Space

Lecture 41 Quiz(Scaling in Real Space)

Lecture 42 Solution(Scaling in Real Space)

Lecture 43 Linear Transformation Explained

Lecture 44 Scaling is a Linear Transformations

Lecture 45 Scaling as a Matrix Multiplication Example Python

Lecture 46 Quiz(Scaling as a Matrix Multiplication Example Python)

Lecture 47 Solution(Scaling as a Matrix Multiplication Example Python)

Lecture 48 Image Coordinate System

Lecture 49 Image Copy and Flipping Vertically

Lecture 50 Quiz 01(Image Copy and Flipping Vertically)

Lecture 51 Solution 01(Image Copy and Flipping Vertically)

Lecture 52 Quiz 02(Image Copy and Flipping Vertically)

Lecture 53 Solution 02(Image Copy and Flipping Vertically)

Lecture 54 Continuous Coordinates

Lecture 55 Saturations and Holes

Lecture 56 Image Doubling and Holes using Python

Lecture 57 Inverse Scaling and Quiz

Lecture 58 Solution and Nearest Neighbour Interpolation

Lecture 59 Inverse Scaling Python

Lecture 60 Quiz 01(Inverse Scaling Python)

Lecture 61 Solution 01(Inverse Scaling Python)

Lecture 62 Quiz 02 (Inverse Scaling Python)

Lecture 63 Solution 02(Inverse Scaling Python)

Lecture 64 Nearest Neighbour Interpolation

Lecture 65 Weighted Average vs Simple Average

Lecture 66 Bilinear Interpolation

Lecture 67 Bilinear Interpolation Implementation in Python

Lecture 68 Scaling Transformation with Bilinear Interpolation Implementation

Lecture 69 Scaling Transformation Algorithm(Recap)

Lecture 70 Exam

Lecture 71 Exam Solution 01

Lecture 72 Exam Solution 02

Section 4: 2D Geometric Transformations

Lecture 73 Github & OneDrive Link to get the Course Materials

Lecture 74 Rotation Introduction

Lecture 75 Optional Rotation is Linear Transform Proof

Lecture 76 Rotation can Result Negative Coordinates(Problem)

Lecture 77 Rotation Computing Width and Hight of Resultant Image(Solution)

Lecture 78 Rotation Index Shifting

Lecture 79 Quiz(Rotation Index Shifting)

Lecture 80 Solution(Rotation Index Shifting)

Lecture 81 Rotation Implementation Complete

Lecture 82 Quiz(Rotation Implementation Complete)

Lecture 83 Solution(Rotation Implementation Complete)

Lecture 84 Rotation Implementation(Good Coding Practice)

Lecture 85 Quiz(Rotation Implementation(Good Coding Practice))

Lecture 86 Solution(Rotation Implementation(Good Coding Practice))

Lecture 87 Reflection Introduction

Lecture 88 Quiz(Reflection Introduction)

Lecture 89 Solution(Reflection Introduction)

Lecture 90 Reflection Implementation

Lecture 91 Quiz 01(Reflection Implementation)

Lecture 92 Solution 01(Reflection Implementation)

Lecture 93 Quiz 02(Reflection Implementation)

Lecture 94 Solution 02(Reflection Implementation)

Lecture 95 Shear Introduction

Lecture 96 Shear Implementation and Quiz

Lecture 97 Translation and its Nonlinearity(Problem)

Lecture 98 Homoginuous Coordinates

Lecture 99 Translation as a Matrix(solution)

Lecture 100 Homoginuous Representations Off all Transformations

Lecture 101 Affine Transformation Implementation

Lecture 102 Quiz(Affine Transformation Implementation)

Lecture 103 Rotation about any Point Theory

Lecture 104 Rotation about any Point Implementation

Lecture 105 Reflection about a Line Quiz

Lecture 106 Solution(Reflection about a Line)

Lecture 107 Transformation Matrix Properties

Lecture 108 Transformation Matrix Properties Implementation

Lecture 109 Affine Transformation Hierarchy

Lecture 110 Optional Affine Transformation SVD

Lecture 111 Projective Transformation Homography

Lecture 112 Projective Transformation Implementation

Lecture 113 Projective Warping Algorithm

Section 5: Geometric Transformation Estimation(Panorama)

Lecture 114 Github & OneDrive Link to get the Course Materials

Lecture 115 Goal

Lecture 116 Affine Transformation Estimation Introduction

Lecture 117 Quiz(Affine Transformation Estimation Introduction)

Lecture 118 Solution(Affine Transformation Estimation Introduction)

Lecture 119 Affine Transformation Estimation Points Correspondences

Lecture 120 Estimation Points Marking using Python and Quiz

Lecture 121 Affine Transformation Min Number of Points Needed

Lecture 122 Affine Transformation Estimation using Python

Lecture 123 Affine Transformation Estimation Verification using Python

Lecture 124 Affine Transformation Estimation with more than 3 Points

Lecture 125 Quiz(Affine Transformation Estimation with more than 3 Points)

Lecture 126 Solution(Affine Transformation Estimation with more than 3 Points)

Lecture 127 Affine Transformation Estimation with more than 3 Points Implementation

Lecture 128 Quiz(Affine Transformation Estimation with more than 3 Points Implementation)

Lecture 129 Solution(Affine Transformation Estimation with more than 3 Points Implementation)

Lecture 130 Optional Affine Transformation Estimation with LeastSquared

Lecture 131 Projective Transformation Estimation Introduction

Lecture 132 Projective Transformation Estimation First Implementation having Bug

Lecture 133 Projective Transformation Estimation Reason of the Bug

Lecture 134 Projective Transformation Estimation Removing Scale Factor

Lecture 135 Projective Transformation Estimation DLT

Lecture 136 Projective Transformation Estimation DLT Nullspace and Why 4 Points

Lecture 137 Projective Transformation Estimation DLT Nullspace Implementation

Lecture 138 DLT Implementation

Lecture 139 Quiz(DLT Implementation)

Lecture 140 Panorama Stitching

Lecture 141 Panorama Stitching Implementation in OpenCV

Lecture 142 How Projective Transformation Helps in Panorama

Section 6: Binary Morphology

Lecture 143 Github & OneDrive Link to get the Course Materials

Lecture 144 Binary Images Theory

Lecture 145 Binary Images Python

Lecture 146 Structuring Element Kernel and Sliding Window Theory

Lecture 147 Structuring Element Python

Lecture 148 Erosion Theory

Lecture 149 Quiz 01(Erosion Theory)

Lecture 150 Solution 01(Erosion Theory)

Lecture 151 Quiz 02(Erosion Theory)

Lecture 152 Solution 02(Erosion Theory)

Lecture 153 Erosion Python

Lecture 154 Dilation Theory

Lecture 155 Quiz 01(Dilation Theory)

Lecture 156 Solution 01(Dilation Theory)

Lecture 157 Quiz 02(Dilation Theory)

Lecture 158 Solution 02(Dilation Theory)

Lecture 159 Dilation Python

Lecture 160 Opening Theory

Lecture 161 Opening Python

Lecture 162 Closing Theory

Lecture 163 Closing Python

Lecture 164 Gradient Morphology

Lecture 165 Gradient Morphology Python

Lecture 166 Tophat Blackhat

Section 7: Image Filtering

Lecture 167 Github & OneDrive Link to get the Course Materials

Lecture 168 Image Blurring 01

Lecture 169 Image Blurring 02

Lecture 170 General Image Filtering

Lecture 171 Convolution

Lecture 172 Naive Edge Detection

Lecture 173 Image Sharpening

Lecture 174 Quiz(Image Sharpening)

Lecture 175 Solution(Image Sharpening)

Lecture 176 Implementation Of Image Blurring Edge Detection Image Sharpening in Python

Lecture 177 Lowpass Highpass Bandpass Filters

Lecture 178 CNN Course(You can Skip)

Section 8: Canny Edge Detector

Lecture 179 Github & OneDrive Link to get the Course Materials

Lecture 180 Canny Edge Detector Algorithm Introduction

Lecture 181 Canny Edge Detector OpenCV

Lecture 182 Quiz(Canny Edge Detector OpenCV)

Lecture 183 Solution(Canny Edge Detector OpenCV)

Lecture 184 Gaussian Filter Introduction

Lecture 185 Gaussian Filter to Mask Computation

Lecture 186 Gaussian Filter Window Size

Lecture 187 Gaussian Filter Implementation

Lecture 188 Quiz(Gaussian Filter Implementation)

Lecture 189 Solution(Gaussian Filter Implementation)

Lecture 190 Gaussian Filter Smoothing Implementation

Lecture 191 Quiz(Gaussian Filter Smoothing Implementation)

Lecture 192 Solution(Gaussian Filter Smoothing Implementation)

Lecture 193 Image Gradients Theory

Lecture 194 Image Gradients Implementation

Lecture 195 Image Gradients Implementation Datatype Bug

Lecture 196 Derivative of Gaussian

Lecture 197 Derivative of Gaussian Expression

Lecture 198 Derivative of Gaussian Implementation

Lecture 199 Applying DOG Filters

Lecture 200 Gradient Vector

Lecture 201 Gradient Magnitude and Gradient Direction

Lecture 202 Non Maxima Suppression

Lecture 203 Gradient Direction Quantization

Lecture 204 Quiz(Gradient Direction Quantization)

Lecture 205 Solution(Gradient Direction Quantization)

Lecture 206 Gradient Direction Quantization Implementation

Lecture 207 Gradient Direction Quantization Implementation Better Way

Lecture 208 NMS Implementation

Lecture 209 Quiz 01(NMS Implementation)

Lecture 210 Solution 01(NMS Implementation)

Lecture 211 Quiz 02(NMS Implementation)

Lecture 212 Solution 02(NMS Implementation)

Lecture 213 Last Step Thresholding

Lecture 214 Hesterysis Thresholding

Lecture 215 Hesterysis Thresholding Implementation

Section 9: Shape Detection

Lecture 216 Github & OneDrive Link to get the Course Materials

Lecture 217 Shape Detection Introduction

Lecture 218 Why Edge Detection is not Enough

Lecture 219 RANSAC Introduction

Lecture 220 RANSAC For Lines Coordinate Arrays

Lecture 221 RANSAC For Lines Sampling Points Randomly Implemenation

Lecture 222 Quiz(RANSAC For Lines Sampling Points Randomly Implemenation)

Lecture 223 Solution(RANSAC For Lines Sampling Points Randomly Implemenation)

Lecture 224 RANSAC For Lines Fitting Line With 2 Points

Lecture 225 RANSAC For Lines Fitting Line With 2 Points Implementation

Lecture 226 Quiz(RANSAC For Lines Fitting Line With 2 Points Implementation)

Lecture 227 Solution(RANSAC For Lines Fitting Line With 2 Points Implementation)

Lecture 228 RANSAC For Lines Computing Consistency Score

Lecture 229 RANSAC For Lines Computing Consistency Score Implementation

Lecture 230 RANSAC For Lines Implementation

Lecture 231 RANSAC For Lines Implementation Test on Real Image

Lecture 232 Drawback

Lecture 233 RANSAC For Lines Implementation Test on Real Image Drawing and Quiz

Lecture 234 RANSAC For Circles

Lecture 235 RANSAC For Circles Consistency Score

Lecture 236 RANSAC For Circles Implementation

Lecture 237 RANSAC For Circles Implementation Real Image

Lecture 238 Drawback

Lecture 239 RANSAC For Circles Implementation Real Image Drawing

Lecture 240 RANSAC General

Lecture 241 RANSAC Quiz

Lecture 242 RANSAC Quiz Solution

Section 10: Shape Detection Hough Transform

Lecture 243 Github & OneDrive Link to get the Course Materials

Lecture 244 Hough Transform Introduction

Lecture 245 Hough Transform as Voting

Lecture 246 Hough Transform as Voting Loop

Lecture 247 Hough Transform Polar Representation

Lecture 248 Hough Transform Polar Representation Benifits

Lecture 249 Hough Transform Polar Representation Implementation

Lecture 250 Hough Transform Lines Implementation Real Image

Lecture 251 Hough Transform Lines Parameters Conversion

Lecture 252 Hough Transform Lines Drawing

Lecture 253 Solution(Hough Transform Lines Drawing)

Lecture 254 Hough Transform Fast Version

Lecture 255 Hough Transform Circles

Lecture 256 Hough Transform Circles Implementation

Lecture 257 Hough Transform Circles Implementation Drawing

Lecture 258 Solution(Hough Transform Circles Implementation Drawing)

Section 11: Corner Detection

Lecture 259 Github & OneDrive Link to get the Course Materials

Lecture 260 Corner Definition

Lecture 261 Why Corner

Lecture 262 Corner Measure

Lecture 263 SSD

Lecture 264 Why SSD to be Muted Somewhere

Lecture 265 Corner Detection Implementation 01

Lecture 266 Corner Detection Implementation 02

Lecture 267 Corner Detection Implementation 03

Lecture 268 Moravec Corner Detector

Lecture 269 Scale Space

Lecture 270 Infinite Directions Towards Harris Corner Detector

Lecture 271 Harris Corner Detector 01

Lecture 272 Harris Corner Detector 02

Lecture 273 Harris Corner Detector 03

Lecture 274 Harris Corner Detector 04 Structure Tensor

Lecture 275 Harris Corner Detector 05 Final Expression

Lecture 276 Harris Corner Detector Implementation Speedup Convolution

Lecture 277 Harris Corner Detector Implementation 01

Lecture 278 Harris Corner Detector Implementation 02

Lecture 279 Harris Corner Detector as Edge Detector

Section 12: Automatic Panorama SIFT

Lecture 280 Github & OneDrive Link to get the Course Materials

Lecture 281 Point Correspondence Introduction

Lecture 282 Point Drawing Implementation

Lecture 283 Scale and Orientation Alignment

Lecture 284 SIFT and HOG

Lecture 285 Points Matching

Section 13: Object Detection

Lecture 286 Github & OneDrive Link to get the Course Materials

Lecture 287 Introduction to Object Detection

Lecture 288 Classification PipleLine

Lecture 289 Sliding Window Implementation

Lecture 290 Shift Scale Rotation Invariance

Lecture 291 Person Detection

Lecture 292 HOG Features

Lecture 293 HandEngineering vs CNNs

Lecture 294 Implementation

Lecture 295 Activity

Section 14: YOLO Object Detector

Lecture 296 Github & OneDrive Link to get the Course Materials

Lecture 297 CNNS Introduction

Lecture 298 Face Detection Implementation

Lecture 299 YOLO Implementation

Lecture 300 YOLO Image Classfication Revisited

Lecture 301 YOLO Sliding Window Object Localization

Lecture 302 YOLO Sliding Window Efficient Implementation

Lecture 303 YOLO Introduction

Lecture 304 YOLO Training Data Generation

Lecture 305 YOLO Anchor Boxes

Lecture 306 YOLO Algorithm

Lecture 307 YOLO Non Maxima Supression

Lecture 308 YOLO RCNN

Section 15: Motion

Lecture 309 Github & OneDrive Link to get the Course Materials

Lecture 310 Optical Flow

Lecture 311 BC Assumption

Lecture 312 Optical Flow Derivation

Section 16: Object Tracking

Lecture 313 Github & OneDrive Link to get the Course Materials

Lecture 314 Tracking by Detection

Lecture 315 Tracking by Detection Motion Model Assumption

Lecture 316 Tracking KLT TLD

Lecture 317 Single Object Tracking

Lecture 318 Multiple Object Tracking

Lecture 319 WebCam and Saving Annotations of Multiple Object Tracking

Section 17: 3D Reconstruction

Lecture 320 Github & OneDrive Link to get the Course Materials

Lecture 321 3d Reconstruction Introduction

Lecture 322 3d Motion Capture

Lecture 323 Camera

Lecture 324 Camera Matrix

Lecture 325 Triangulation

Lecture 326 Camera Matrix Estimation

Lecture 327 Mocap Revisited

Section 18: Smart CCTV Project

Lecture 328 Github & OneDrive Link to get the Course Materials

Lecture 329 Introduction to the Project

Lecture 330 Introduction to Data

Lecture 331 Reading a Video File

Lecture 332 Change Detection Frame Differencing

Lecture 333 Change Detection Frame Differencing Implementation

Lecture 334 Change Detection Background Subtraction

Lecture 335 Change Detection Background Subtraction MOG

Lecture 336 Denoising using Morphology

Lecture 337 Connected Components

Lecture 338 Connected Components Filtering

Lecture 339 Tracking Change

Lecture 340 Saving Segments

Lecture 341 Saving and Viewing Segments

Lecture 342 Saving and Viewing Segments with Object Detection

Lecture 343 Applications

Lecture 344 THANK YOU Bonus Video

Lecture 345 About AI Sciences

• Learners who are absolute beginners and know nothing about Computer Vision.,• People who want to make smart solutions.,• People who want to learn computer vision with real data.,• People who love to learn theory and then implement it using Python.,• People who want to learn computer vision along with its implementation in realistic projects.,• Data Scientists.,• Machine learning experts.