Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 106 lectures (9h 50m) | Size: 3.15 GB
Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV What you'll learn:
Have a good understanding of the most powerful Computer Vision models
Understand OpenCV
Understand and implement Viola-Jones algorithm
Understand and implement Histogram of Oriented Gradients (HOG) algorithm
Understand and implement convolutional neural network (CNN) related computer vision approaches
Understand and implement YOLO (You Only Look Once) algorithm
Single Shot MultiBox Detection SDD algorithm
Master face detection and object detection
Requirements
Basic Python programming skills
Description
This course is about the fundamental concept of image processing, focusing on face detection and object detection. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation. Self-driving cars (for example lane detection approaches) relies heavily on computer vision.
With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?
Section 1 - Image Processing Fundamentals:
computer vision theory
what are pixel intensity values
convolution and kernels (filters)
blur kernel
sharpen kernel
edge detection in computer vision (edge detection kernel)
Section 2 - Serf-Driving Cars and Lane Detection
how to use computer vision approaches in lane detection
Canny's algorithm
how to use Hough transform to find lines based on pixel intensities
Section 3 - Face Detection with Viola-Jones Algorithm:
Viola-Jones approach in computer vision
what is sliding-windows approach
detecting faces in images and in videos
Section 4 - Histogram of Oriented Gradients (HOG) Algorithm
how to outperform Viola-Jones algorithm with better approaches
how to detects gradients and edges in an image
constructing histograms of oriented gradients
using suppor vector machines (SVMs) as underlying machine learning algorithms
Section 5 - Convolution Neural Networks (CNNs) Based Approaches
what is the problem with sliding-windows approach
region proposals and selective search algorithms
region based convolutional neural networks (C-RNNs)
Download
http://nitroflare.com/view/C47FFD197EC2471/Computer_Vision_Bootcamp%E2%84%A2_with_Python_%28OpenCV%29_-_YOLO%2C_SSD.part2.rar
http://nitroflare.com/view/87EA14F728C7CC5/Computer_Vision_Bootcamp%E2%84%A2_with_Python_%28OpenCV%29_-_YOLO%2C_SSD.part3.rar
http://nitroflare.com/view/2B212FD08DF5B5F/Computer_Vision_Bootcamp%E2%84%A2_with_Python_%28OpenCV%29_-_YOLO%2C_SSD.part4.rar
or
http://rapidgator.net/file/8c5253090adcf7945edee67ea2b6339b/Computer_Vision_Bootcamp™_with_Python_(OpenCV)_-_YOLO,_SSD.part1.rar.html
http://rapidgator.net/file/6651915383edd96bf2afe4eec2caac83/Computer_Vision_Bootcamp™_with_Python_(OpenCV)_-_YOLO,_SSD.part2.rar.html
http://rapidgator.net/file/167938fc0a5f78fd02300088e18243e3/Computer_Vision_Bootcamp™_with_Python_(OpenCV)_-_YOLO,_SSD.part3.rar.html
http://rapidgator.net/file/a4d389f043eeb8cfd69017f30239d0be/Computer_Vision_Bootcamp™_with_Python_(OpenCV)_-_YOLO,_SSD.part4.rar.html