by Param Uttarwar | Released April 2018 | ISBN: 9781788474368

OpenCV Object Recognition: Harness Deep Learning in OpenCVAbout This VideoSee functionalities in OpenCV that can be used for Object detection and recognition through Deep LearningLearn, why Deep Learning algorithms should be preferred for object recognitionMaster use of OpenCV by practicing creation of basic applicationsIn DetailThis 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. Show and hide more

1. Chapter 1 : Basic Operations and Algorithms in OpenCV
   • The Course Overview 00:04:28
   • How to Work with Images in OpenCV? 00:07:03
   • Enhancement and Filtering Operations in OpenCV 00:09:14
   • Saving Images, Accessing Camera 00:04:10
   • Image Transformations 00:06:29
   • Computer Vision Algorithms 00:06:39
2. Chapter 2 : Object Detection and Recognition Using Features
   • Working with Object Recognition 00:02:55
   • Features and Descriptors 00:03:21
   • Feature Matching and Homography 00:06:15
   • Building an Application 00:04:42
3. Chapter 3 : Deep Learning in OpenCV
   • Getting Started with Neural Networks 00:07:54
   • Architecture of a Convolutional Neural Network (CNN) 00:08:06
   • Starting with Caffe 00:04:40
   • Implementing Deep Learning Using OpenCV and Caffe 00:14:38
4. Chapter 4 : Object Classification Using Deep Learning
   • Defining Problem Statement 00:06:26
   • Designing an Algorithm for the Problem 00:05:04
   • Training the Network Using Labeled Data 00:04:03
   • Classification Problem 00:04:56
5. Chapter 5 : Recognizing Text in an Image
   • Problem Definition and Gathering Dataset 00:05:42
   • Modeling Appropriate Algorithm 00:02:46
   • Moving from Algorithm to Code 00:07:33
   • Results and Analysis 00:06:20
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