Book Image

Hands-On Java Deep Learning for Computer Vision

By : Klevis Ramo
Book Image

Hands-On Java Deep Learning for Computer Vision

By: Klevis Ramo

Overview of this book

Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The book is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy.
Table of Contents (8 chapters)

Building a Java edge detection application

Now, we'll see different type of filters and apply them to different images. Also, we'll explore how the neural network is using convolution or edge detection.

Types of filters

There are other types of filters apart from the vertical and horizontal filters we've seen so far:

Two other popular filters are as follows:

  • Sobel: This filter simply adds a little bit more weight or value to the middle
  • Scharr: Besides adding even more weight to the middle, this filter also adds weight to the sides

As we can see, the zeros are placed in the middle column of the Vertical, Sobel, and Scharr filters. Hence, we can say that Sobel and Scharr measure the difference between the left...