Last updated 10/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 472.05 MB | Duration: 5h 18m
Develop interactive computer vision applications with the popular C libraries of OpenCV 3 What you'll learn Scan an image with pointers and neighbor access Represent colors with hue, saturation, and brightness Retrieve similar images using the histogram comparison Calibrate the camera from different image observations Detect people and objects in images using machine learning techniques Reconstruct a 3D scene from images Add the miniaturizing tilt-shift effect Load videos and store them Requirements Basic programming knowledge on C is needed. Description OpenCV 3 is a native cross-platform C Library for computer vision, machine learning, and image processing. Computer vision applications are the latest buzz of recent ! Big brands such as Microsoft, Apple, Google, Facebook, and Apple are increasingly making use of computer vision for object, pattern, image, and face recognition. This has led to a very high demand for computer vision expertise. So, if you're interested to know how to use the OpenCV library to build computer vision applications, then go for this Learning Path. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are Dive into the essentials of OpenCV and build your own projects Learn how to apply complex visual effects to images Reconstruct a 3D scene from images Master the fundamental concepts in computer vision and image processing Let’s take a quick look at your learning journey. This Learning Path helps you to get started with the OpenCV library and shows you how to install and deploy it to write effective computer vision applications following good programming practices. You will learn how to read and display images. You will then be introduced to the basic OpenCV data structures. Further, you will start a new project and see how to load an image file and show it. Next, you'll find out how to handle keyboard events in our display window. In the next project, you will jump into interactively adjusting image brightness. You will then learn to add a miniaturizing tilt-shift effect and how to blur images. In the final project, you will learn to apply Instagram-like color ambiance filters to images. By the end of this Learning Path, you will be able to build computer vision applications that make the most of OpenCV 3. Meet Your Experts We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth Robert Laganiere is a professor at the School of Electrical Eeering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.AdiShavit is an experienced software architect and has been an OpenCV user since it was in early beta back in 2000. Since then he has been using it pretty much continuously to build systems and products rag from embedded, vehicle, and mobile apps to desktops and large, distributed cloud-based servers and services. His specialty is in computer vision, image processing, and machine learning with an emphasis on real- applications. He specializes in cross-platform, high performance software combined with a high production-quality maintainable code base. He builds many products, apps, and services that leverage OpenCV. Overview Section 1: OpenCV 3 – Getting Started with Image Processing Lecture 1 The Course Overview Lecture 2 Installing the OpenCV Library Lecture 3 Loading, Displaying, and Saving Images Lecture 4 Exploring the cv::Mat Data Structure Lecture 5 Defining Regions of Interest Lecture 6 Accessing Pixel Values Lecture 7 Scanning an Image with Pointers Lecture 8 Scanning an Image with Iterators Lecture 9 Writing Efficient Image-Scanning Loops Lecture 10 Scanning an Image with Neighbor Access Lecture 11 Perfog Simple Image Arithmetic Lecture 12 Remapping an Image Lecture 13 Comparing Colors Using the Strategy Design Pattern Lecture 14 Snting an Image with the GrabCut Algorithm Lecture 15 Converting Color Representations Lecture 16 Representing Colors with Hue, Saturation, and Brightness Lecture 17 Computing an Image Histogram Lecture 18 Applying Look-Up Tables to Modify the Image's Appearance Lecture 19 Equalizing the Image Histogram Lecture 20 Backprojecting a Histogram to Detect Specific Image Content Lecture 21 Using the Mean Shift Algorithm to Find an Object Lecture 22 Retrieving Similar Images Using Histogram Comparison Lecture 23 Counting Pixels with Integral Images Section 2: OpenCV 3 – Advanced Image Detection and Reconstruction Lecture 24 The Course Overview Lecture 25 Detecting Corners in an Image Lecture 26 Detecting Features Quickly Lecture 27 Detecting Scale-Invariant Features Lecture 28 Detecting FAST Features at Multiple Scales Lecture 29 Matching Local Templates Lecture 30 Describing and Matching Local Intensity Patterns Lecture 31 Matching Keypoints with Binary Descriptors Lecture 32 Computing the Fundamental Matrix of an Image Pair Lecture 33 Matching Images Using Random Sample Consensus Lecture 34 Computing a Homography Between Two Images Lecture 35 Detecting a Planar Target in Images Lecture 36 Recognizing Faces Using Nearest Neighbors Lecture 37 Finding Objects and Faces with a Cascade of Haar Features Lecture 38 Detecting Objects and People with Support Vector Machines Section 3: OpenCV 3 Projects for Photo Filtering Lecture 39 The Course Overview Lecture 40 Building OpenCV Lecture 41 Creating a New Project Lecture 42 Loading Images Lecture 43 Showing Images Lecture 44 Keyboard Events Lecture 45 Understanding Brightness and Contrast Lecture 46 Adjusting Brightness and Contrast with OpenCV Lecture 47 Interactive Image Adjustment Lecture 48 Storing Images Lecture 49 Miniature Faking Lecture 50 Blurring Images Lecture 51 Compositing Images Lecture 52 The Miniature Effect via Partial Blurring Lecture 53 Handling Mouse Events Lecture 54 Color Filters Lecture 55 Remapping Colors Lecture 56 Processing Video Lecture 57 Recoloring Video Lecture 58 Saving Video This learning path is appropriate for novice C programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming. HomePage:
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