- Description
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Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered.
In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques. If you have never heard about computer vision, at the end of this course you will have a practical overview of all areas. Below you can see some of the content you will implement:
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Detect faces in images and videos using OpenCV and Dlib libraries
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Learn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib libraries
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Track objects in videos using KCF and CSRT algorithms
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Learn the whole theory behind artificial neural networks and implement them to classify images
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Implement convolutional neural networks to classify images
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Use transfer learning and fine tuning to improve the results of convolutional neural networks
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Detect emotions in images and videos using neural networks
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Compress images using autoencoders and TensorFlow
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Detect objects using YOLO, one of the most powerful techniques for this task
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Recognize gestures and actions in videos using OpenCV
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Create hallucinogenic images using the Deep Dream technique
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Combine style of images using style transfer
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Create images that don’t exist in the real world with GANs (Generative Adversarial Networks)
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Extract useful information from images using image segmentation
You are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab
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3Plan of attack
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4Images and pixels
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5Cascade classifier - intuition
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6Loading and pre-processing the image
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7Face detection with Haarcascade and OpenCV
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8Haarcascades parameters 1
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9Haarcascades parameters 2
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10Eye detection with haarcascades
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11HOMERWORK – detecting other objects
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12Homework solution
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13HOG (Histrograms of Oriented Gradients) – intuition
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14Face detection with HOG and Dlib
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15Face detection with CNN and Dlib
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16HOMEWORK – Haarcascade x HOG x CNN
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17Homework solution
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18Anaconda and PyCharm
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19Face detection on the webcam
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20Additional reading
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21Plan of attack
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22LBPH algorithm - intuition
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23Loading the faces dataset
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24Preprocessing the images
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25Training the LBPH classifier
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26Recognizing faces with LBPH
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27Evaluating the LBPH classifier
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28LBPH parameters
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29LBPH parameters – implementation
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30Detecting facial points
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31Detecting facial descriptors 1
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32Detecting facial descriptors 2
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33Calculating distances between faces
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34Recognizing faces with Dlib 1
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35Recognizing faces with Dlib 2
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36HOMEWORK
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37Homework solution
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38Face recognition on the webcam
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39Additional reading