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Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs
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Sunila Gollapudi
Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs by Sunila Gollapudi at Abbey's Bookshop,

Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs

Sunila Gollapudi



Computer vision


151 pages

$59.95  $53.95
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Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you'll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision. After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. What You Will Learn Understand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis Who This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.

By:   Sunila Gollapudi
Imprint:   APress
Country of Publication:   United States
Edition:   1st ed.
Dimensions:   Height: 235mm,  Width: 155mm, 
Weight:   272g
ISBN:   9781484242605
ISBN 10:   1484242602
Pages:   151
Publication Date:   April 2019
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

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 systemsNo of pages 25Sub -Topics1. Overview of AI, computer vision and related fields2. Real-world usecases and domains for computer vision application3. 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 pythonChapter Goal:This chapter provides step-by-step instructions to setting up OpenCV with python. Learn core libraries, syntax and interfaces.No of pages: 20Sub - Topics 1. Install and set-up OpenCV and python2. Core operations & syntax3. GUI features4. OpenCV-Python bindings5. Build, deploy and debug OpenCV projects6. Build OpenCV applications for scale by integrating with file systems.7. Hands-on code using OpenCV libraries Chapter 3: Images: Manipulation & SegmentationChapter 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 coveredNo of pages: 20Sub - Topics: 1. Image translations - moving images up, down. left and right 2. Rotations - how to spin your image around and do horizontal flipping3. Scaling, re-sizing and interpolations - understand how re-sizing affects quality 4. Blurring and sharpening5. Segmentation and contours - extract defined shapes In your image6. 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 pagesSub - Topics: 1. Objective Detection Overview2. Videos: reading from Webcam, storing and interpreting3. 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 AnalysisChapter Goal:No of pages: 20 pagesSub - Topics: 1. Learn how to programmatically track a single point over time.2. Learn how to analyze videos as sequences of individual image frames3. Motion filed and optical flow4. Camera models and caliberation5. Hands-on code using OpenCV libraries Chapter 6: Looking ahead: upcoming applications and trends in Computer Vision Chapter Goal: No of pages: 15 pagesSub - Topics: 1. Upcoming technologies, applications and technologies in computer vision2. Other open source frameworks landscape both commercial and opensource3. Intro to advanced computer vision and deep learning techniques

Sunila Gollapudi has over 17 years of experience in developing, designing and architecting data-driven solutions with a focus on the banking and financial services sector. She is currently working at Broadridge, India as vice president. She's played various roles as chief architect, big data and AI evangelist, and mentor. She has been a speaker at various conferences and meetups on Java and big data technologies. Her current big data and data science expertise includes Hadoop, Greenplum, MarkLogic, GemFire, ElasticSearch, Apache Spark, Splunk, R, Julia, Python (scikit-learn), Weka, MADlib, Apache Mahout, and advanced analytics techniques such as deep learning, computer vision, reinforcement, and ensemble learning.

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