English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 13m | 601 MB
OpenCV Object Recognition: Harness Deep Learning in OpenCV
This course teaches effective object recognition and its implementation with the powerful OpenCV libraries. You will learn how to enhance your OpenCV skills with deep learning. You will explore and master OpenCV for Object Recognition/Classification. The course explains all the necessary theory and concepts of computer vision, image processing, and machine learning. You also learn the practical application of OpenCV libraries. Its capabilities and functionality are shown along with a tutorial on how to set up a machine such that it’s able to use OpenCV in codes. You will start by seeing how to work with images in OpenCV, enhancement and filtering in OpenCV. You will then move on to building an application which is capable of object recognition and performing homography. You will then move on to object classification and recognizing text in an image.
In the end, you will be able to use object recognition algorithm which will be used by you for practical application.
This course will help you practice deep learning principles and algorithms for detecting and decoding images using OpenCV, by following step by step easy to understand instructions.
What You Will Learn
- Getting started with OpenCV
- Showing how to attempt your first OpenCV code
- See the architecture of a convolutional neural network
- Learn how to enhance and filter an Image
- Practice use of popular CV algorithms
- Get started with Caffe
- Train your the network using labeled data
- know how to train the network using labeled data
- See how to Model an appropriate algorithm for your requirement
- Quickly move from algorithm to code
Table of Contents
Basic Operations and Algorithms in OpenCV
1 The Course Overview
2 How to Work with Images in OpenCV
3 Enhancement and Filtering Operations in OpenCV
4 Saving Images, Accessing Camera
5 Image Transformations
6 Computer Vision Algorithms
Object Detection and Recognition Using Features
7 Working with Object Recognition
8 Features and Descriptors
9 Feature Matching and Homography
10 Building an Application
Deep Learning in OpenCV
11 Getting Started with Neural Networks
12 Architecture of a Convolutional Neural Network (CNN)
13 Starting with Caffe
14 Implementing Deep Learning Using OpenCV and Caffe
Object Classification Using Deep Learning
15 Defining Problem Statement
16 Designing an Algorithm for the Problem
17 Training the Network Using Labeled Data
18 Classification Problem
Recognizing Text in an Image
19 Problem Definition and Gathering Dataset
20 Modeling Appropriate Algorithm
21 Moving from Algorithm to Code
22 Results and Analysis
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