Title: Announcing "Case Studies: Solving real world problems with computer vision" - PyImageSearch

URL Source: https://pyimagesearch.com/2014/06/26/announcing-case-studies-solving-real-world-problems-computer-vision/

Published Time: 2014-06-26T13:58:32+00:00

Markdown Content:
[![Image 1: Learn how to use HOG and a Linear Support Vector Machine to recognize handwritten text.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20185'%3E%3C/svg%3E)](https://pyimagesearch.com/wp-content/uploads/2014/05/digit_recognition.jpg)

I have some big news to announce today…

Besides writing a ton of blog posts about computer vision, image processing, and image search engines, I’ve been behind the scenes, _**working on a second book**_.

And you may be thinking, hey, didn’t you just finish up _[Practical Python and OpenCV](https://pyimagesearch.com/practical-python-opencv/)_?

Yep. I did.

Now, don’t get me wrong. The feedback for _Practical Python and OpenCV_ has been amazing. And it’s done exactly what I thought it would — teach developers, programmers, and students just like you the basics of computer vision **_in a single weekend_**.

But now that you know the fundamentals of computer vision and have a solid starting point, it’s time to move on to something more interesting…

Let’s take your knowledge of computer vision and solve some **_actual, real world problems_**.

What type of problems?

I’m happy you asked. Read on and I’ll show you.

What does this book cover?
--------------------------

This book covers five main topics related to computer vision in the real world. Check out each one below, along with a screenshot of each.

#1. Face detection in photos and video
--------------------------------------

[![Image 2: Figure 1: Learn how to use OpenCV and Python to detect faces in images.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20556%20354'%3E%3C/svg%3E)](https://pyimagesearch.com/wp-content/uploads/2014/05/face_detection.jpg)

**Figure 1:** Learn how to use OpenCV and Python to detect faces in images.

By far, the most requested tutorial _of all time_ on this blog has been **_“How do I find faces in images?”_** If you’re interested in face detection and finding faces in images and video, then this book is for you.

#2. Object tracking in video
----------------------------

[![Image 3: Figure 2: My Case Studies book will show you how to track objects in video as they move along the screen.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20241'%3E%3C/svg%3E)](https://pyimagesearch.com/wp-content/uploads/2014/05/object_tracking.jpg)

**Figure 2:** My _Case Studies_ book will show you how to track objects in video as they move along the screen.

Another common question I get asked is **_“How can I track objects in video?”_** In this chapter, I discuss how you can use the color of an object to track its trajectory as it moves in the video.

#3. Handwriting recognition with Histogram of Oriented Gradients (HOG)
----------------------------------------------------------------------

[![Image 4: Figure 3: Learn how to use HOG and a Linear Support Vector Machine to recognize handwritten text.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20253'%3E%3C/svg%3E)](https://pyimagesearch.com/wp-content/uploads/2014/05/digits_top.jpg)

**Figure 3:** Learn how to use HOG and a Linear Support Vector Machine to recognize handwritten text.

This is probably my favorite chapter in the entire _Case Studies_ book, simply because it is so _practical and useful_.

Imagine you’re at a bar or pub with a group of friends, when all of a sudden a beautiful stranger comes up to you and hands you their phone number written on a napkin.

Do you stuff the napkin in your pocket, hoping you don’t lose it? Do you take out your phone and manually create a new contact?

Well you could. Or. You could take a picture of the phone number and have it automatically recognized and stored safely.

In this chapter of my _Case Studies_ book, you’ll learn how to use the _Histogram of Oriented Gradients (HOG) descriptor_ and _Linear Support Vector Machines_ to **classify digits in an image.**

#4. Plant classification using color histograms and machine learning
--------------------------------------------------------------------

[![Image 5: Figure 4: Learn how to apply machine learning techniques to classify the species of flowers.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20376'%3E%3C/svg%3E)](https://pyimagesearch.com/wp-content/uploads/2014/05/plant_classification.jpg)

**Figure 4:** Learn how to apply machine learning techniques to classify the species of flowers.

A common use of computer vision is to _classify the contents of an image_. In order to do this, you need to utilize machine learning. This chapter explores how to _extract color histograms_ using OpenCV and then _train a Random Forest Classifier_ using scikit-learn to classify the species of a flower.

#5. Building an Amazon.com book cover search
--------------------------------------------

[![Image 6: Figure 5: Applying keypoint detection and SIFT descriptors to recognize and identify book covers.](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20600%20501'%3E%3C/svg%3E)](https://pyimagesearch.com/wp-content/uploads/2014/05/book_cover.jpg)

**Figure 5:** Applying keypoint detection and SIFT descriptors to recognize and identify book covers.

Three weeks ago, I went out to have a few beers with my friend Gregory, a hot shot entrepreneur in San Francisco who has been developing a piece of software to instantly recognize and identify book covers — using only an image. Using this piece of software, users could snap a photo of books they were interested in, and then have them automatically added to their cart and shipped to their doorstep — at a substantially cheaper price than your standard _Barnes & Noble!_

Anyway, I guess Gregory had one too many beers, because guess what?

He clued me in on his secrets.

Gregory begged me not to tell…but I couldn’t resist.

In this chapter you’ll learn how to utilize **keypoint extraction and SIFT descriptors** to perform keypoint matching.

The end result is a system that can recognize and identify the cover of a book in a snap…of your smartphone!

All of these examples are covered in detail, from front to back, with lots of code.

By the time you finish reading my _Case Studies_ book, you’ll be a pro at solving real world computer vision problems.

So who is this book for?
------------------------

This book is for people like yourself who have a solid foundation of computer vision and image processing. Ideally, you have already read through _Practical Python and OpenCV_ and have a strong grasp on the basics (if you haven’t had a chance to read _Practical Python and OpenCV_, [definitely pick up a copy](https://pyimagesearch.com/practical-python-opencv/)).

I consider my new _Case Studies_ book to be the next logical step in your journey to learn computer vision.

You see, this book focuses on taking the fundamentals of computer vision, and then applying them to **_solve, actual real-world problems_**.

So if you’re interested in applying computer vision to solve real world problems, you’ll definitely want to pick up a copy.

Reserve your spot in line to receive early access
-------------------------------------------------

If you signup for my newsletter, I’ll be sending out previews of each chapter so you can get see **first hand** how you can use computer vision techniques to solve real world problems.

But if you simply _can’t wait_ and want to lock-in your spot in line to receive **_early access_** to my new _Case Studies_ eBook, [just click here](https://pyimagesearch.com/case-studies/).

Sound good?

[Sign-up now to receive an **_exclusive_** pre-release deal when the book launches.](https://pyimagesearch.com/case-studies/)

![Image 7](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20410%20567'%3E%3C/svg%3E)

#### Join the PyImageSearch Newsletter and Grab My FREE 17-page Resource Guide PDF

Enter your email address below to **join the PyImageSearch Newsletter** and **download my FREE 17-page Resource Guide PDF** on Computer Vision, OpenCV, and Deep Learning.