Learn Computer Vision Using Opencv: With Deep Learning Cnns and Rnns (Paperback)

Chapter 1: Computer Vision and its application Chapter Goal: The goal of this chapter is to have readers understand the landscape of Artificial Intelligence and the role of computer vision in AI applications. An understanding of how computer vision is applied in various domains and its role in building AI systems No of pages 25 Sub -Topics 1. Overview of AI, computer vision and related fields 2. Real-world usecases and domains for computer vision application 3. Introduction to computer vision and its subfields that includes OCR, ICR etc... 4. Deep dive into deep learning techniques applied in computer vision 5. Architecture, operating model and challenges in building computer vision applications Chapter 2: Introduction to OpenCV and python Chapter Goal: This chapter provides step-by-step instructions to setting up OpenCV with python. Learn core libraries, syntax and interfaces. No of pages: 20 Sub - Topics 1. Install and set-up OpenCV and python 2. Core operations & syntax 3. GUI features 4. OpenCV-Python bindings 5. Build, deploy and debug OpenCV projects 6. Build OpenCV applications for scale by integrating with file systems. 7. Hands-on code using OpenCV libraries Chapter 3: Images: Manipulation & Segmentation Chapter Goal: This chapter focuses on understanding images, how they are stored and processed by a computer. Image Transformations, translations, rotations, scaling, cropping and operations are covered. Segmentation and pattern recognition within images and tagging using OpenCV libraries is covered No of pages: 20 Sub - Topics: 1. Image translations - moving images up, down. left and right 2. Rotations - how to spin your image around and do horizontal flipping 3. Scaling, re-sizing and interpolations - understand how re-sizing affects quality 4. Blurring and sharpening 5. Segmentation and contours - extract defined shapes In your image 6. Blob detection - detect the center of flowers 7. Hands-on code using OpenCV libraries Chapter 4: Object Detection: Chapter Goal: Finding patterns in objects, SIFT, SURF, FAST, BRIEF & ORB - learn the different ways to get image features No of pages: 20 pages Sub - Topics: 1. Objective Detection Overview 2. Videos: reading from Webcam, storing and interpreting 3. Face and eye detection - detect human faces and eyes in any image. face analysis and filtering - identify face outline, lips, eyes even eyebrows. merging faces (face swaps) - combine two faces for fun & sometimes scary results 4. Detecting specific things: landmark, car and pedestrian detection in videos 5. Hands-on code using OpenCV libraries Chapter 5 : Tracking and Motion Analysis Chapter Goal: No of pages: 20 pages Sub - Topics: 1. Learn how to programmatically track a single point over time. 2. Learn how to analyze videos as sequences of individual image frames 3. Motion filed and optical flow 4. Camera models and caliberation 5. Hands-on code using OpenCV libraries Chapter 6: Looking ahead: upcoming applications and trends in Computer Vision Chapter Goal: No of pages: 15 pages Sub - Topics: 1. Upcoming technologies, applications and technologies in computer vision 2. Other open source frameworks landscape both commercial and opensource 3. Intro to advanced computer vision and deep learning techniques